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School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #13 10/25/2013 Homework Assignment #4 - Solutions Grade Distribution by Question On the next page you will find a breakdown of how well students did on each question. For each question you will see a bar with three dif
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Solutions to Probabilistic Problems 2001 Practice Final December 6th, 2007 1) Solution: a Alice did not violate any of the rules of actional thought. She stated that the only preference she has is that she goes somewhere from
School: Stanford
Course: DECISION ANALYSIS
MS&E252 Decision Analysis I 11/09/2013 Midterm Exam - Solutions MS&E252 Decision Analysis I 11/09/2013 MS&E252 Decision Analysis I 11/09/2013 1) Solution: c Using Deal A and Deal B and applying the substitution rule, we get: 0.6 0.5 $100 0.5 $50 0.5 0.4 0
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Midterm Nov 6th, 2007 Midterm Examination MS&E 252: Decision Analysis I Please read the following instructions carefully! 1. This exam is closed book and closed notes, except for a single sheet (1 side). You may use a calculat
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #7 10/14/2007 Homework Assignment #3 Due on Thursday Oct. 18th 11:59 pm In this assignment you are required to turn in the probabilistic section only. Again the "Food for thought" section is optional but will help you
School: Stanford
STANFORD UNIVERSITY CS 229, Autumn 2011 Midterm Examination Wednesday, November 9, 6:00pm-9:00pm Question Points 1 Generalized Linear Models /15 2 Gaussian Naive Bayes /15 3 Linear Invariance of Logistic Regression /12 4 2-Regularized SVM /18 5 Uniform Co
School: Stanford
MS&E 121 Introduction to Stochastic Modeling Prof. Peter W. Glynn 1.Introduction and Review December 12, 2012 Page 1 of 7 1. (Ross, EX. 1.42) There are three coins in a box. One is a two-headed coin, another is a fair coin, and the third is a biased coin
School: Stanford
Course: Finance For Non-MBAs
Review Session before Final Finance for non MBAs TA: Pablo Villanueva (using previous TAs notes) pvillanueva@stanford.edu December 11th, 2011 Agenda for Today 1. Practice Questions Binomial Options Pricing. Capital Structure. Currency Hedging. Put-Cal
School: Stanford
ProfessorPepaKraft PrinciplesofFinancialAccounting,Homework#2 Homework #2 (due on Wednesday, 2/26/14 in class) [21 POINTS IN TOTAL] Please hand in a hard copy of your assignment in class or before. Please type your homework. Put your section number, name
School: Stanford
Lecture Notes in Macroeconomics John C. Driscoll Brown University and NBER1 December 3, 2001 Department of Economics, Brown University, Box B, Providence RI 02912. Phone (401) 863-1584, Fax (401) 863-1970, email:John Driscoll@brown.edu, web:http:\ c
School: Stanford
Course: Digital Systems I
EE108A Section #1 September 29, 2011 1) Noise Margins A logic family uses signal levels relative to VDD as shown in the following table: Parameter Value VOL 0.2VDD VIL 0.4VDD VIH 0.6VDD VOH 0.8VDD We connect two logic subsystems A and B using this logic f
School: Stanford
Course: Is Stanford A Religion?
Religions cannot be so minimally defined as they historically have been (lecture RS2 01/20/15, lecture RS2 01/13/15). Unlike the explorers of centuries past, today we recognize a diversity of belief and worship that extends far beyond the three monotheist
School: Stanford
Course: Is Stanford A Religion?
Archeologists have found traces of religious thinking at sites dating back longer than we have been fully human (lecture RS2 01/08/15). Religion has been with us since the beginning. But why are people religious? And as society changes in the newly high-t
School: Stanford
Course: Politics And Public Policy
PolicyAnalysisMemo1:PublicOpinioninthePassageoftheCleanAirAct In the late 1960s and early 1970s, large amounts of environmental legislation passed on a federal level, including the creation of the Environmental Protection Agency, the strengthening of the
School: Stanford
Course: Mecanical
Las centrales generadoras Son instalaciones donde se produce la energa elctrica, por conversin a partir de una fuente energtica. UNIVERSIDAD DE TARAPACA Escuela Universitaria de Proceso de generacin Ingeniera Elctrica-Electrnica Energa mecnica Fluido A
School: Stanford
Course: BACK FROM AFRICA WORKSHOP
ItseemstodaythatnothingishappeninginAfricaexceptfortheviolence, whichisplaguingeverysinglecountry.Thetypicalwesternnarrativedictatesthatviolenceis endemictoAfricaandthatitisthewestsresponsibilitytopreservewhateversemblanceof peacethatcouldbemustered.Whati
School: Stanford
Course: MACHINE LEARNING
1 Resampling Detection for Digital Image Forensics John Ho, Derek Ma, and Justin Meyer AbstractA virtually unavoidable consequence of manipulations on digital images are statistical correlations introduced between the pixels. These correlations may not be
School: Stanford
Course: Infrastructure Project Development
Infrastructure Project Development F13-CEE241A/141A Professor Gary Griggs TA Jorge Gonzalez Presidio Parkway (Doyle Drive) Professor Gary Griggs CEE 241A/141A Infrastructure Project Development 1 Presidio Parkway Professor Gary Griggs CEE 241A/141A Infras
School: Stanford
Course: Economic Analysis II
Professor Jay Bhattacharya Spring 2001 Example: Calculating IEPs and Engel Curves Demand II Find the IEP and Engel Curve for a consumer with Recap: last lecture we covered: Income Expansion Paths and Engel curves Inferior and Normal Goods Necessities
School: Stanford
Course: Semiconductor Optoelectronic Devices
1/10/12 EE243 Semiconductor Optoelectronic Devices ! Prof. James Harris! Room 328, Paul Allen Center for Integrated Systems (CISX)! ! Harris@snow.stanford.edu! Web Page - http:/ee.stanford.edu/~harris! (650) 723-9775, (650) 723-4659 fax! Ofce Hours 2: 05
School: Stanford
Introduc)ontoInforma)onRetrieval Introduc)ontoInforma)onRetrieval Informa)onRetrieval Informa)onRetrieval(IR)isndingmaterial(usually documents)ofanunstructurednature(usuallytext) thatsa)sesaninforma)onneedfromwithinlarge collec)ons(usuallystoredoncompute
School: Stanford
Course: Infrastructure Project Development
Infrastructure Project Development F13-CEE241A/141A Professor Gary Griggs TA Jorge Gonzalez Class 05 The Planning Phase Study the planning processes used for public sector projects including alternative analyses, project rating and evaluation methods, and
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Solutions to Probabilistic Problems 2001 Practice Final December 6th, 2007 1) Solution: a Alice did not violate any of the rules of actional thought. She stated that the only preference she has is that she goes somewhere from
School: Stanford
Course: DECISION ANALYSIS
MS&E252 Decision Analysis I 11/09/2013 Midterm Exam - Solutions MS&E252 Decision Analysis I 11/09/2013 MS&E252 Decision Analysis I 11/09/2013 1) Solution: c Using Deal A and Deal B and applying the substitution rule, we get: 0.6 0.5 $100 0.5 $50 0.5 0.4 0
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Midterm Nov 6th, 2007 Midterm Examination MS&E 252: Decision Analysis I Please read the following instructions carefully! 1. This exam is closed book and closed notes, except for a single sheet (1 side). You may use a calculat
School: Stanford
STANFORD UNIVERSITY CS 229, Autumn 2011 Midterm Examination Wednesday, November 9, 6:00pm-9:00pm Question Points 1 Generalized Linear Models /15 2 Gaussian Naive Bayes /15 3 Linear Invariance of Logistic Regression /12 4 2-Regularized SVM /18 5 Uniform Co
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 November 29th, 2001 Handout #25, page 1 of 25 SAMPLE FINAL: EES&OR 252 Final Examination (1997-1998) Please do not begin the exam until you are instructed to do so. Name (printed clearly): _ 1. Count the number of pages in this exam. There should
School: Stanford
Course: Database Systems Principles
CS 245 Midterm Exam Winter 2012 This exam is open book and notes. You can use a calculator and your laptop to access course notes and videos (but not to communicate with other people). You have 70 minutes to complete the exam. Print your name: The Honor C
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #13 10/25/2013 Homework Assignment #4 - Solutions Grade Distribution by Question On the next page you will find a breakdown of how well students did on each question. For each question you will see a bar with three dif
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #7 10/14/2007 Homework Assignment #3 Due on Thursday Oct. 18th 11:59 pm In this assignment you are required to turn in the probabilistic section only. Again the "Food for thought" section is optional but will help you
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Problem Session 8 What concepts do we expect you to master for the Final Exam? Medical DA The Foundations of DA PIBP, PISP Relevance The Five Rules U-Curves The Delta Property Sensitivity Analysis Information Gathering, Value
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #14 10/27/2013 Homework Assignment #5 Due on Thursday October 31th, 11:59 pm (Updated on October 27th, 10:30 am) Assigned Reading The Foundation of Decision Analysis: Chapter 10 and 11. Distinctions From the class lect
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout # 8 Due 10/17/2013 Homework Assignment #3 Due on Thursday Oct. 17th 2013, at 11:59 pm Assigned Readings: 1. The Foundation of Decision Analysis (Course Reader I): Finish reading chapter 5 to 7 2. Collection of Readings
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #6 10/4/2013 Homework Assignment #2 Due on Thursday Oct. 10th 11:59 pm Homework Submission Logistics: You can access the MS&E 252 homework submission site from Coursework at http:/coursework.stanford.edu. Click on Subm
School: Stanford
Course: DATA STRUCTURES
E40 / Spring 2012 LAB. 1B: SERIAL AND PARALLEL CONNECTIONS The design portion of the prelab is to be done with your lab partner. Each group of two students need to turn in only one report. OBJECTIVES To examine the current, voltage and power characteristi
School: Stanford
Course: EE - Digital CMOS Integrated Circuits
Custom WaveView User Guide Version F-2011.09-SP1, December 2011 Copyright Notice and Proprietary Information Copyright 2011 Synopsys, Inc. All rights reserved. This software and documentation contain confidential and proprietary information that is the pr
School: Stanford
Course: EE - Digital CMOS Integrated Circuits
EE213 Winter 2014-15 M. Horowitz page 1 of 15 VIRTUOSO TUTORIAL Introduction Before starting on this tutorial, please read the first few paragraphs of HW#2 which provide instructions on creating the proper working directory and sourcing the correct files.
School: Stanford
Course: EE - Digital CMOS Integrated Circuits
HSPICE Toolbox for MATLAB Michael Perrott (perrott@mtl.mit.edu) Copyright 1999 by Silicon Laboratories, Inc. 7 October 1999 The Hspice toolbox for Matlab is a collection of Matlab routines that allow you to manipulate and view signals generated by Hspice
School: Stanford
Course: Advanced Analog Integrated Circuit Design
CAD BASICS STANFORD UNIVERSITY Department of Electrical Engineering EE114/EE214A & EE214B Revised: January 2015 1 About This Handout This tutorial is composed of two parts. The first part is a quick start in which you will go through all the steps you nee
School: Stanford
Course: Advanced Analog Integrated Circuit Design
EE214B Winter 2014-15 B. Murmann Page 1 of 7 DESIGN PROJECT Part I due on Monday, March 2, 2015, 5pm Part II due on Wednesday, March 11, 2015, noon Overview In this project you will work on the design of the wideband transimpedance amplifier shown in Figu
School: Stanford
Course: Mecanical
C) Determinacin del Mdulo de rigidez Probeta: D= 6 (mm) L= 76, 2 (mm) Tomando el punto 10 de la zona elstica en la grfica torque v/s giro: T= 5,9 (Nm) = 10=0,1745 (rad) El mdulo de rigidez de la probeta se calcular con la frmula: Dnde: = longitud parte ci
School: Stanford
Course: Mecanical
Procedimiento experimental - En esta experiencia prctica se analizaran dos probetas de materiales distintos: bronce y acero. - Se realiza la medicin del dimetro de las probetas, con el pie de metro. - En la mquina de ensayo de cizalle se instala el cabeza
School: Stanford
Course: Mecanical
Introduccin: El presente informe se basa en el anlisis desarrollo y compresin de los conceptos bsicos de la tolerancia y los ajustes. Se estudiara cada instrumento de medicin conociendo las caractersticas tcnicas de estos, como su divisin de escala, el ra
School: Stanford
Course: Introduction To Statistical Inference
STAT 426 Lecture 34 Fall 2012 Arash A. Amini September 13, 2012 1 / 35 Announcements My oce hours: Tue 4 5p in 470 West Hall, Wed 12 1p in 438 West Hall Yingchuans oce hours: Wed 2:30 3:30p in 274 West Hall Fri 9:30 10:30a in 274 West Hall Final exam: Wed
School: Stanford
Course: Introduction To Statistical Inference
STAT 426 Lecture 2324 Fall 2012 Arash A. Amini December 6, 2012 1 / 26 Outline Muddiest points: Those that were/are least clear throughout the course. Write down 13 muddiest points and turn them in at the end of class. NeymanPearson Paradigm Randomized de
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Handout #1 Decision Analysis I September 24, 2013 _ Course Guide to MS&E 252 Foundations of Decision Analysis Welcome to Decision Analysis I. This course guide has been developed to summarize the course logistics. Please read this handout careful
School: Stanford
Course: INVESTMENT SCIENCE
MS&E 242 Investment Science Professor: Class Location and Time: Class Description: Enrollment: Web page: Course email: Course Assistants: Staff Assistant: Required Course Text:Investment Science Prerequisites: Honor Code: Homework: 5pm418 Terman or i
School: Stanford
Course: Infrastructure Project Development
CEE241A/141A Syllabus Infrastructure Project Development Professor: Gary Griggs TA: Jorge Gonzalez Quarter: Autumn 2013 Units: 3 Grading Criteria: 50% Final Project, 25% Assignments and Quizzes, 25% Class and Project Team Participation Meeting Time: T Th
School: Stanford
Course: America In A Unipolar World
POLISCI 213S. A Post-American World: U.S. Foreign Policy in a Uni-Multipolar System Fall Quarter 2010 Tuesdays, 2:15-4:05 pm Building 200-107 Josef Joffe E 103 Encina Hall 650-724-8010 Office Hours: Tuesdays, 4:15 pm and by appointment jjoffe@stanford.edu
School: Stanford
Course: Computer Organization And Systems
CS107 Handout 02 January 9th, 2012 Winter 2012 CS107 Course Outline Rough Outline of What To Expect Introduction to Systems, Lower-level C Constructs o C basics, UNIX development tools, gcc, gdb. o C pointers and arrays, C strings, malloc, realloc, and fr
School: Stanford
Course: Chemical Principles I
CME100 Vector Calculus for Engineers V. Khayms Fall 2011 Course Information Sheet Instructor: Vadim Khayms (vadim@stanford.edu) Office hours: Tue. 6:00-8:00pm Phone: (408) 203-0822 TAs: Michael Lesnick (mlesnick@stanford.edu) Ajith Morpathi (ajithm@stanfo
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #13 10/25/2013 Homework Assignment #4 - Solutions Grade Distribution by Question On the next page you will find a breakdown of how well students did on each question. For each question you will see a bar with three dif
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Solutions to Probabilistic Problems 2001 Practice Final December 6th, 2007 1) Solution: a Alice did not violate any of the rules of actional thought. She stated that the only preference she has is that she goes somewhere from
School: Stanford
Course: DECISION ANALYSIS
MS&E252 Decision Analysis I 11/09/2013 Midterm Exam - Solutions MS&E252 Decision Analysis I 11/09/2013 MS&E252 Decision Analysis I 11/09/2013 1) Solution: c Using Deal A and Deal B and applying the substitution rule, we get: 0.6 0.5 $100 0.5 $50 0.5 0.4 0
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Midterm Nov 6th, 2007 Midterm Examination MS&E 252: Decision Analysis I Please read the following instructions carefully! 1. This exam is closed book and closed notes, except for a single sheet (1 side). You may use a calculat
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #7 10/14/2007 Homework Assignment #3 Due on Thursday Oct. 18th 11:59 pm In this assignment you are required to turn in the probabilistic section only. Again the "Food for thought" section is optional but will help you
School: Stanford
STANFORD UNIVERSITY CS 229, Autumn 2011 Midterm Examination Wednesday, November 9, 6:00pm-9:00pm Question Points 1 Generalized Linear Models /15 2 Gaussian Naive Bayes /15 3 Linear Invariance of Logistic Regression /12 4 2-Regularized SVM /18 5 Uniform Co
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 November 29th, 2001 Handout #25, page 1 of 25 SAMPLE FINAL: EES&OR 252 Final Examination (1997-1998) Please do not begin the exam until you are instructed to do so. Name (printed clearly): _ 1. Count the number of pages in this exam. There should
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Problem Session 8 What concepts do we expect you to master for the Final Exam? Medical DA The Foundations of DA PIBP, PISP Relevance The Five Rules U-Curves The Delta Property Sensitivity Analysis Information Gathering, Value
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #14 10/27/2013 Homework Assignment #5 Due on Thursday October 31th, 11:59 pm (Updated on October 27th, 10:30 am) Assigned Reading The Foundation of Decision Analysis: Chapter 10 and 11. Distinctions From the class lect
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout # 8 Due 10/17/2013 Homework Assignment #3 Due on Thursday Oct. 17th 2013, at 11:59 pm Assigned Readings: 1. The Foundation of Decision Analysis (Course Reader I): Finish reading chapter 5 to 7 2. Collection of Readings
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #6 10/4/2013 Homework Assignment #2 Due on Thursday Oct. 10th 11:59 pm Homework Submission Logistics: You can access the MS&E 252 homework submission site from Coursework at http:/coursework.stanford.edu. Click on Subm
School: Stanford
Course: Accounting
Decision Making and Relevant Information 2012 Pearson Prentice Hall. All rights reserved. Decision Models A decision model is a formal method of making a choice, often involving both quantitative and qualitative analyses. Managers often use some variatio
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #23 12/7/2007 Homework Assignment #7- Solutions Distinctions These distinctions were prepared by the teaching team and reflect our best belief of the meanings of these terms. A decision diagram shows the structure of a
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #2 9/26/2013 Homework Assignment #1 You are not required to turn in any of this assignment; however, we expect you to have full knowledge of the material included. Reading 1: Foundations of Decision Analysis (Course Re
School: Stanford
Course: DECISION ANALYSIS II
MS&E 352 Handout #23 Decision Analysis II Mar 04, 2009 _ Problem Set #3 Solutions Grade Distribution 35% 30% 25% 20% 15% 10% 5% 0% 0-5 6-10 11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 71-75 76-80 81-85 86-90 91-95 96-100 Proble
School: Stanford
Course: Database Systems Principles
CS 245 Midterm Exam Winter 2012 This exam is open book and notes. You can use a calculator and your laptop to access course notes and videos (but not to communicate with other people). You have 70 minutes to complete the exam. Print your name: The Honor C
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Midterm Solutions Nov 14th, 2006 Midterm Examination Solutions Grade Distribution 35 1.00 0.90 30 0.80 25 0.70 0.60 20 0.50 15 0.40 10 0.30 0.20 5 0.10 0 -INF - 0 5 - 10 15 - 20 25 - 30 35 - 40 45 - 50 55 - 60 Cumulative 65 -
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #22 11/30/2007 Homework Assignment #7 Due on Thursday, December 6th 11:59 pm In this assignment you are required to turn in the probabilistic section only. Again the "Food for thought" section is optional but will help
School: Stanford
Course: Statistical Methods In Finance
Hints to Assignment 2 October 31, 2012 Problem 2.9 No need to decompose the joint density as log f (Xn , . . . , X1 ) = log f (Xt |Xt1 . . .), instead use the joint density directly in your proof. Just remember all it matters is f being a density and the
School: Stanford
Course: DECISION ANALYSIS II
MS&E 352 Handout #2 Decision Analysis II January 6th, 2009 Problem Set 0 Due: January 13, 2009 _ This problem set is a gentle tutorial to the beta distribution, which we shall use extensively in this class. You will need Excel to complete the assignments.
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #17 11/9/2007 Homework Assignment #6 Due on Thursday November 15th, 11:59 pm In this assignment you are required to turn in the probabilistic section only. Again the "Food for thought" section is optional but will help
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #10 10/22/2007 Homework Assignment #3 Solutions Student Distribution: 40 1 0.9 35 0.8 30 0.7 25 0.6 20 0.5 0.4 15 0.3 10 0.2 5 0.1 0 9.5-10 0-0.5 0.5-1 1-1.5 1.5-2 2-2.5 2.5-3 3-3.5 3.5-4 4-4.5 4.5-5 5-5.5 5.5-6 6-6.5
School: Stanford
Course: MODERN APPLIED STATISTICS: LEARNING
Statistics 315a Homework 1, due Wednesday January 29, 2014. ESL refers to the course textbook, and ESL 2.4 refers to exercise 2.4 in ESL. Since the homework assignments count 70% of your nal grade, you must do them on your own. Problem 1 is computing inte
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #23 11/16/2012 Homework Assignment #6 Solutions 60 0 5 10 15 20 25 1 0.9 50 0.8 0.7 40 0.6 0.5 30 0.4 20 0.3 0.2 10 0.1 0 0 Page 1 of 20 HW#6 Solutions MS&E 252 Decision Analysis I Handout #23 11/16/2012 Page 2 of 20 H
School: Stanford
x h s w e p x h s s u w j n h x h n h x j x o x x o h h m m o h x x x r x x x j i x o j n x k x f f u i i w g i u o n x x x x i n x h o x x j o f j i w i j x x k m j k k x n m j j h x p h x f n r x n n i m n n q q w f j h k o f j w l n n k f j o j m x i
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #12 10/31/2007 Homework Assignment #4 - Solutions Students Distribution: 60 1 0.9 50 0.8 0.7 40 0.6 30 0.5 0.4 20 0.3 0.2 10 0.1 0 9.5-10 0-0.5 0.5-1 1-1.5 1.5-2 2-2.5 2.5-3 3-3.5 3.5-4 4-4.5 4.5-5 5-5.5 5.5-6 6-6.5 6.
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Handout #1 Decision Analysis I September 24, 2013 _ Course Guide to MS&E 252 Foundations of Decision Analysis Welcome to Decision Analysis I. This course guide has been developed to summarize the course logistics. Please read this handout careful
School: Stanford
Course: AI
CS229 Practice Midterm 1 CS 229, Autumn 2010 Practice Midterm Notes: 1. The midterm will have about 5-6 long questions, and about 8-10 short questions. Space will be provided on the actual midterm for you to write your answers. 2. The midterm is meant to
School: Stanford
Course: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Linear & Nonlinear Optimization Fall 2011 Prof Yinyu Ye Homework Assignment 3: SOLUTIONS Problem 1. Sensitivity Analysis: (22 points) [2 points each] You have rented a metal detector for two and a half hours. You can spend your time with it searc
School: Stanford
1 CS229 Problem Set #1 CS 229, Autumn 2011 Problem Set #1: Supervised Learning Due in class (9:30am) on Wednesday, October 19. Notes: (1) These questions require thought, but do not require long answers. Please be as concise as possible. (2) When sending
School: Stanford
Course: FINANCE FOR NON-MBAS
Finance Fall 2013 Professor Ishii Problem Set #4 Due: By 3:15 PM on Friday, October 25, 2013 in Lockbox #46 The answers may be either hand-written or typed. If you work in a group, the group should submit only one solution. Please make sure to write the n
School: Stanford
Course: MODERN APPLIED STATISTICS: LEARNING
Statistics 315a Homework 2, due Wednesday February 12, 2014. 1. ESL 3.12 & 3.30 2. (a) Suppose that we run a ridge regression with parameter on a single variable X , and get coecient a. We now include an exact copy X = X , and ret our ridge regression. Sh
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #21 11/20/2007 Homework Assignment # Solutions #6 Question Distribution: Page 1 of 20 HW#6 Solutions MS&E 252 Decision Analysis I Handout #21 11/20/2007 Student Distribution: 45 40 35 30 0.6 25 0.5 20 0.4 15 0.3 10 5 0
School: Stanford
Course: The Fourier Transform And Its Applications
EE261 Raj Bhatnagar Summer 2009-2010 EE 261 The Fourier Transform and its Applications Midterm Examination 19 July 2010 (a) This exam consists of 4 questions with 12 total subparts for a total of 50 points. (b) The questions dier in length and diculty. Do
School: Stanford
Course: MODERN APPLIED STATISTICS: LEARNING
Stats 315A HW2 Solutions February 17, 2014 If there are any questions regarding the solutions or the grades of HW 2, please contact Austen (ahead@stanford.edu) with Stats315A-hw2-grading in the subject line. Grade Distribution: Total 100 Points Problem 1:
School: Stanford
Course: Mathematical Finance
MATH 238 WINTER 2009 PROBLEM SET 1 - SOLUTIONS Problem 1: Let S be the current stock price, K the strike price of the option, T the expiration time of the option, t the current time, ST the stock price at time T , r the risk-free interest rate, c the pric
School: Stanford
Course: DECISION ANALYSIS II
MS&E 352 Handout #6 Decision Analysis II January 29th, 2009 _ Problem Set #2 Due Thursday February 5th Part I Advanced Information Gathering Problem 1 Alpha / Beta Detectors [35 points] Kim faces the Party Problem in Professor Howard's manuscript. Two sal
School: Stanford
Course: DECISION ANALYSIS II
MS&E 352 Handout #25 Decision Analysis II March 11th, 2009 _ Problem Set #4 - Solutions Grade Distribution 35% 30% 25% 20% 15% 10% 5% 0% 16-20 21-25 46-50 51-55 56-60 86-90 91-95 26-30 31-35 36-40 41-45 61-65 66-70 71-75 76-80 81-85 96-100 11-15 0-5 6-10
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I HO31 Final Examination: Part I December 3rd, 2002 Final Examination 2001-2002: Part I Please read the following instructions carefully! 1. This exam is closed book and closed notes. You may use one double-sided 8'x11' sheet of
School: Stanford
EE 284 F. Tobagi Autumn 2010-2011 EE284 Homework Assignment No. 1 Topic: Switching Techniques, Network Topologies Handed out: September 21, 2010 Due: September 30, 2010 in class (Previously September 28 but now extended by 2 days) Total Points: 45 ALL WOR
School: Stanford
Course: MODERN APPLIED STATISTICS: LEARNING
Stats 315A HW1 Solutions February, 2014 Grade Distribution: Total 100 Points Problem 1: 30 [6 + 8 + 8 + 8] Problem 2: 10 Problem 3: 15 [3 + 5 + 4 + 3] Problem 4: 20 [17 + 3] Problem 5: 10 Problem 6: 15 [4 + 3 + 4 + 4] Problem 1 Part (a): The code is provi
School: Stanford
Course: FINANCE FOR NON-MBAS
Finance Winter 2012 Professor Admati Problem Set #2 Due: Friday, January 27, 2012 1. The Quinby Corporation is a Canadian manufacturing company that is negotiating the sale of a large printing press to a French company. The sale will actually take place i
School: Stanford
Course: SUPPLY CHAIN MANAGEMENT
Rev 3/08 National Bicycle Industrial Co. National Bicycle Industrial Co. Transport Bike Mkt Position Sales Trend Variety Cost Profit Uncertainty Sport Bike The National Bicycle Supply Chain The National Bicycle Supply Chain Suppliers Factory Hansha WHs Re
School: Stanford
Course: CS229
CS229 Practice Midterm 1 CS 229, Autumn 2007 Practice Midterm Notes: 1. The midterm will have about 5-6 long questions, and about 8-10 short questions. Space will be provided on the actual midterm for you to write your answers. 2. The midterm is meant to
School: Stanford
Course: Stochastic Modeling
MS&E 221 Ramesh Johari Problem Set 2 Due: February 7, 2007, 5:00 PM outside Terman 319 Reading. Same as last week: read Section 4.4 in Ross. Problem 1. Bertsekas and Tsitsiklis, Chapter 6, Problem 11 (note that steady state means the current distribution
School: Stanford
Course: DECISION ANALYSIS II
MS&E 352 Handout #1 Decision Analysis II January 6th, 2009 _ Course Guide to MS&E 352 Decision Analysis II Professional Decision Analysis Welcome back, we are glad to see you in "Professional Decision Analysis". DA2 is the second course in the DA sequence
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Course: MODERN APPLIED STATISTICS: LEARNING
Statistics 315a Homework 2, due Wednesday February 13, 2013. 1. ESL 3.12 & 3.30 2. ESL 3.15 3. (a) Suppose that we run a ridge regression with parameter on a single variable X , and get coecient a. We now include an exact copy X = X , and ret our ridge re
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CS262 Problem Session Problem Set 1 Solutions Special thanks to Cristina Pop Problem 1, Part A (a) Optimal: Global, Ends-Free, Constant Gap penalty (due to existence of introns) Heuristic: BLAST, find regions, post-process to get the full sequence, and
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MS&E 121 Introduction to Stochastic Modeling Prof. Peter W. Glynn Assignment 5 April 24, 2013 Assignment 5 - Due Tuesday, February 19 Note: This material is for the personal use of students enrolled in MS&E 121. Any further distribution, including posting
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Homework 7 Homework Assignment #7 Due on Thursday November 28th 11:59 pm Homework Submission Logistics: You can access the MSE 252 homework submission site from Coursework at http:/coursework.stanford.edu. Click on Submit Home
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School: Stanford
Course: FINANCE FOR NON-MBAS
Finance Fall 2013 Professor Ishii Problem Set #2 Due: By 3:15 PM on Friday, October 11, 2013 in Lockbox #46 The answers may be either hand-written or typed. If you work in a group, the group should submit only one solution. Please make sure to write the n
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Course: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Fall 2011 Linear and Nonlinear Optimization Oct 11, 2011 Prof. Yinyu Ye Homework Assignment 1: Sample Solution Problem 1 Let x1j = tons of waste sent to incinerator j from Palo Alto , x2j = tons of waste sent to incinerator j from Stanford, and y
School: Stanford
Course: FINANCE FOR NON-MBAS
Finance Fall 2012 Professor Admati Problem Set #4 1. Brenda Weiss is a portfolio manager managing a portfolio worth $2,000,000. She typically constructs her portfolio using an S&P fund and a fund that invests in small stocks, as well as treasury bills. He
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CS229 Problem Set #3 Solutions 1 CS 229, Autumn 2011 Problem Set #3 Solutions: Theory & Unsupervised learning Due in class (9:30am) on Wednesday, November 16. Notes: (1) These questions require thought, but do not require long answers. Please be as concis
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Course: Data Analysis
STATS 202 Homework 1 Hao Chen July 3, 2011 In total: 40 points. Problem 2 (26 points, 2 points each) Classify the following attributes as binary, discrete, or continuous. Also classify them as qualitative (nominal or ordinal) or quantitative (interval or
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Course: Stochastic
MS&E 221 Ramesh Johari Problem Set 2 Due: Weds., February 2, 2011, 5:00 PM in the basement of HEC Reading. 4.1-4.4, 4.5.1, 4.7 in Ross. Problem 1. Ross, Chapter 4, problem 14: Problem 2. (A queueing model) Consider a queue (or a waiting room) that can hol
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1 CS229 Problem Set #2 Solutions CS 229, Autumn 2011 Problem Set #2 Solutions: and Theory Naive Bayes, SVMs, Due in class (9:30am) on Wednesday, November 2. Notes: (1) These questions require thought, but do not require long answers. Please be as concise
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MS&E 121 Introduction to Stochastic Modeling Prof. Peter W. Glynn 1.Introduction and Review December 12, 2012 Page 1 of 7 1. (Ross, EX. 1.42) There are three coins in a box. One is a two-headed coin, another is a fair coin, and the third is a biased coin
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Course: Finance For Non-MBAs
Review Session before Final Finance for non MBAs TA: Pablo Villanueva (using previous TAs notes) pvillanueva@stanford.edu December 11th, 2011 Agenda for Today 1. Practice Questions Binomial Options Pricing. Capital Structure. Currency Hedging. Put-Cal
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ProfessorPepaKraft PrinciplesofFinancialAccounting,Homework#2 Homework #2 (due on Wednesday, 2/26/14 in class) [21 POINTS IN TOTAL] Please hand in a hard copy of your assignment in class or before. Please type your homework. Put your section number, name
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Lecture Notes in Macroeconomics John C. Driscoll Brown University and NBER1 December 3, 2001 Department of Economics, Brown University, Box B, Providence RI 02912. Phone (401) 863-1584, Fax (401) 863-1970, email:John Driscoll@brown.edu, web:http:\ c
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Course: Digital Systems I
EE108A Section #1 September 29, 2011 1) Noise Margins A logic family uses signal levels relative to VDD as shown in the following table: Parameter Value VOL 0.2VDD VIL 0.4VDD VIH 0.6VDD VOH 0.8VDD We connect two logic subsystems A and B using this logic f
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Course: Introduction To VLSI Systems
Review: CMOS Logic Gates INV Schematic NOR Schematic + Vsg Vin pMOS NAND Schematic x x y g(x,y) = x + y Vout = Vin nMOS g(x,y) = x y y x + Vgs - x CMOS inverts functions CMOS Combinational Logic parallel for OR series for AND use DeMorgan relations
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Course: Intro To Databases
Midterm Review October 20 Announcements You should look on Piazza for your room assignments! There is no class on Wednesday It is being used for makeup exams. GOOD LUCK! Exam Details 90 Minutes. 7 to 8:30
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Course: Is Stanford A Religion?
Religious Studies 2 Is Stanford a Religion? Winter 2014 T/Th 11am 12:15pm Professor Kathryn Gin Lum Civilized Man can no more endure intellectual and spiritual than physical starvationLeland Stanford, c. 1889 This course seeks to introduce students to the
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Course: Linear And Nonlinear Optimization
c Benjamin Van Roy and Kahn Mason 99 PRIMAL maximize minimize DUAL bi 0 constraints bi 0 variables = bi unconstrained 0 cj variables 0 cj constraints unconstrained = cj Note, using the rules in the above table, the dual of minimize subject to cx A1 x b1
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Course: Convolutional Neural Networks For Visual Recognition
This Business of Brewing: Caffe in Practice caffe.berkeleyvision.org Evan Shelhamer from the tutorial by Evan Shelhamer, Jeff Donahue, Yangqing Jia, and Ross Girshick github.com/BVLC/caffe Deep Learning, as it is executed. What should a framework handle?
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Course: Management Of New Product Development
Instructions: 1.Press Previous and Next buttons to move between Slides. 2.Press Esc Key to Close the Presentation. SWOT Analysis S W O Copyright 2008 - 2012 managementstudyguide.com. All rights reserved. T SWOT Analysis Learning Objectives What is SWOT An
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Course: Mecanical
d) Estimacin de la resistencia a la fluencia en traccin del material. Del diagrama de momento torsor y ngulo de torsin, se tiene que: Por lo tanto, el esfuerzo cortante de fluencia del material es igual a: Donde: Es el momento de inercia, siendo D el dime
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Course: Mecanical
Version 16.2 Enhancements Several features have been added to Version 16.2 of Statgraphics at the request of users. These include: Additional summary statistics Additional operators for use in handling character data Calculation of statistical tolerance l
School: Stanford
Course: Mecanical
12.62 12.06 12.38 12.08 11.8 Resist encia [K] 2 4 6 8 10 12.7 12.6 12.5 12.4 12.3 12.2 12.1 12 11.9 11.8 11.7 11.6 11.5 11.4 11.3 1 2 3 4 5 6 Volt aje Ei [V] 7 8 9 10 11
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Course: Mecanical
Ecuacin de la elstica: Se utilizar el mtodo de funciones singulares, de modo que nuestro esquema queda de siguiente manera: + P(kp) RA RB 26,6 11,2 X Donde P= 0,404 (kp), w= 0,1164 (kp/cm) y RA= 0,888 (kp) Luego: (I) (II) + (III) Mediante condiciones de b
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Course: MACHINE LEARNING
Probability Theory Review for Machine Learning Samuel Ieong November 6, 2006 1 Basic Concepts Broadly speaking, probability theory is the mathematical study of uncertainty. It plays a central role in machine learning, as the design of learning algorithms
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Course: MACHINE LEARNING
Gaussian processes Chuong B. Do December 1, 2007 Many of the classical machine learning algorithms that we talked about during the rst half of this course t the following pattern: given a training set of i.i.d. examples sampled from some unknown distribut
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Course: MACHINE LEARNING
Linear Algebra Review and Reference Zico Kolter October 16, 2007 1 Basic Concepts and Notation Linear algebra provides a way of compactly representing and operating on sets of linear equations. For example, consider the following system of equations: 4x1
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Course: MACHINE LEARNING
Convex Optimization Overview (cnt'd) Chuong B. Do October 26, 2007 1 Recap During last week's section, we began our study of convex optimization, the study of mathematical optimization problems of the form, minimize f (x) n xR subject to gi (x) 0, i = 1,
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Course: MACHINE LEARNING
Convex Optimization Overview Zico Kolter October 19, 2007 1 Introduction Many situations arise in machine learning where we would like to optimize the value of some function. That is, given a function f : Rn R, we want to nd x Rn that minimizes (or maximi
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Course: MACHINE LEARNING
CS229 Lecture notes Andrew Ng Part V Support Vector Machines This set of notes presents the Support Vector Machine (SVM) learning algorithm. SVMs are among the best (and many believe is indeed the best) o-the-shelf supervised learning algorithm. To tell t
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Course: MACHINE LEARNING
CS229 Lecture notes Andrew Ng Part VI Learning Theory 1 Bias/variance tradeo When talking about linear regression, we discussed the problem of whether to t a simple model such as the linear y = 0 +1 x, or a more complex model such as the polynomial y = 0
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Course: Introduction To Statistical Inference
Stat 200: Additional Problems Chapter 7: 33, 51a (Hint: Use the Schwarz inequality.) Chapter 8: 22, 30, 52, 60, 65. Chapter 9: 12, 19, 22, 45. Chapter 11: 19a,b,c, 41a,b,c. Chapter 13: 1, 4. Chapter 14: 14, 21, 23, 42. 1
School: Stanford
Course: Marketing To Businesses
1. 0 LAUNCH In this course, we are principally interested in management and exchange plus the process between a firm and its clients. A good offers a product or even a service to the potential customer who has a requirement for it. The marketing process m
School: Stanford
Course: Marketing To Businesses
Nigerian economy, and also on each business as effectively as on just about every Nigerian citizen. These consequences will include a decline in exports for you to these countries along with a drop in your Nigerian Naira exchange rate against the major cu
School: Stanford
Course: Marketing To Businesses
With the aid of examples, differentiate involving needs and wants. Itemise the value of marketing in the economy. 7. 0 REFERENCES/FURTHER STUDYING Kotler, P. (2000). Promoting Management Research, Planning, Implementation along with Control, 8th Edition.
School: Stanford
Course: Marketing To Businesses
Another important purpose which marketing plays is which it helps in your discovery of entrepreneurial talent. Peter Drucker, a celebrated writer in the field of Management, makes now very succinctly as soon as he observes that marketing is really a multi
School: Stanford
Course: Marketing To Businesses
8 Marketing Finance That may be, allowing credits to customers and in addition as obtaining credit from customers, for example Banks, individuals, etc. Risk-Bearing Risk suggests uncertainty. Entering right into a business entails risks, such as decrease
School: Stanford
Course: Marketing To Businesses
1960s (Marketing Control Era) This is actually the period when your marketing department became popular and so much important inside the U. S. The. One of the authors of times, Peter Drunker states that marketing department is really complex that this can
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Course: Marketing To Businesses
3. 5. Standardisation and Grading This can be concerned with placing certain standards/levels to complete the produced things. This is performed by the creation department and regulated by some govt agencies, such because Standards Organisation regarding
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Course: Marketing To Businesses
3. 2. 3 Demands People have almost infinite wants but minimal resources. They would like to choose products that include the most price and satisfaction for money. When backed by purchasing energy, wants become require. That is, demand want intended for s
School: Stanford
Course: Marketing To Businesses
distribution. The concern ended up being to design the very best channel of distribution one of many various alternatives. Between 1940 plus the 1950s (war era), all efforts were intended for the production regarding war equipment on the expense of purcha
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Course: Marketing To Businesses
3. 5 The Evolution regarding Marketing Marketing develops because the society and its economic activities develop. The need intended for marketing arises and grows because the society moves via an economy regarding Agriculture and selfsufficiency with an
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Course: Marketing To Businesses
3. 2. 7 Markets A market pertains to a set of all actual and potential buyers of a product and service. These buyers discuss particular needs or wants that can be satisfied through trade. The size of your market depends within the need of people who have
School: Stanford
Course: Marketing To Businesses
Marketing takes area when people opt to satisfy needs along with wants through trade. Exchange is which means act of buying a desired object via someone by offering something in exchange. Exchange is only one of the many ways people can buy a desired obje
School: Stanford
Course: Marketing To Businesses
These definitions usually are better explained with the examination of these terms: needs, wants, demands, products, trade, and some other people. 3. 2 Simple Concepts Underlying Promoting 3. 2. 1 Wants The most standard concept underlying advertising and
School: Stanford
Course: Marketing To Businesses
1. 0 LAUNCH In this course, we are principally interested in management and exchange plus the process between a firm and its clients. A good offers a product or even a service to the potential customer who has a requirement for it. The marketing process m
School: Stanford
Course: Marketing To Businesses
3. 0 MAIN CONTENT 3. 1 Definitions of Marketing The term Marketing has become defined often by different specialists. It is necessary to pause for a short time and consult some definitions: (a) Marketing includes the performance regarding business activit
School: Stanford
Course: Marketing To Businesses
PresentnewsisstuffedwithexamplesincludingchangingorganizationmethodslikeMergers, Arrangedalliances,Downsizing,SpinoffsmoreovertoInternationalextension.Thisposition examinestherealidealguidanceprocedureasitrelatesfortheplanningpurpose. Managershavetocarefu
School: Stanford
Course: Marketing To Businesses
GoodprocedureDevinessstrategyandwhichmightbeeasilyelopafewHRanalyticswhich havebeenappropriateforthebusgatheredontheperiodicbasispermittingtrendevaluation GoodExerciseNoticeHumanResourceManagement16 ial, kled,anddeliveringthenecessaryresearchgoods.ften,ov
School: Stanford
Course: Marketing To Businesses
Degrees of HR metr monitoring trends, are: Employees turnover/retention Employees headroomproportilongtermworkersversusshortlivedstafonofpackedrolesforyouto totalroles;proportionsuchasrolespackedbyEmployeesqualificationsmoreovertoencounter Numbersandtypes
School: Stanford
Course: Marketing To Businesses
GoodprocedurePaperstherealHRstrategy,eitherinastandaloneevaluationorcontainedin theorganizationswholeorganizationstrategydocument,sothatitcanbecommunicatedforyou toandconfirmedbythosewhomustbringoutthetechnique WhereTIMEmethodsarerecordedwithinstandalonep
School: Stanford
Course: Marketing To Businesses
Discovering the Organizations Recent Mission, Aims, and Techniques: Every organization needsamission,thestatementoftheintentionofanorganization.Thepurposestatementdeals withtheproblem:Whatwillbetheorganizationsreasonforbeinginbusiness?Theorganization must
School: Stanford
Course: Marketing To Businesses
ssions,sothatwilltheirguidancecanbeappropriateandalsoappropriatefortheMiddle.This maybedonedifferently, robserversatMiddleguidanceteamconferencesroutinebriefingsviaMiddlemoreovertounit guidance on organization strategy speaks egional/country offices (part
School: Stanford
Course: Marketing To Businesses
for the Concept Notice to read more on applying ideal employment methods in CGIAR Facilities. The Idea Notice can be discovered at http: /www. cgpeoplepower. org/show_publications.jsp GoodprocedureEquipthehiringoperatetoprovideidealevaluationandtips Cente
School: Stanford
Course: Marketing To Businesses
high quality enhancement projects or perhaps suggestions via exterior moreover to inner opinionsattractingworkers(andfamilies)foryoutoandalsodevelo The amount of investment such as effort within HR organized planning that's deemed appropriate for the orga
School: Stanford
Course: Marketing To Businesses
Itdarleneborinanorganization.Makingchoicesaboutdigitalinitiatedoreintelligentcapital withinMiddlengscientist locationswithprotectionproblemsand/oramenitytroubles;aprefertoexpandtherealworkers choice(e.grettlethedeviceguy.sex,nationality,representationfrom
School: Stanford
Course: Marketing To Businesses
CGIARFacilitieshavemultiplegroupsofworkersinrangewiththemarketplaceswheretheyare recruited. Almost all Facilities have got internationally moreover to nationally new workers sessions,someFacilitieshaveregionallynewworkerssessions,andinthesesessions,therec
School: Stanford
Course: Marketing To Businesses
workformostofthesegroupsdevelopclassmodifications,anifestedwithinrigidlystratified professional(andattimessocial)circumstances.Theresultingareductioninmotivationforyou to comCentereveralotmorecritical alloverworkersgroups(Rajasekharan2004). ff,helpedbymod
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Course: Marketing To Businesses
workersallowstomakesurethmisionmoreovertoallowsdevelopcommitmentforyoutothose valuesandguidelinesamongohydrates Box1.OneStaffApproachSASHRprovidesdocumentedaconventionalperspectivemoreover tovaluesassociatedwithhandlingmenandladies,knownbecauseOneStaff,wh
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Course: Marketing To Businesses
onemaydiscern,fromresearchresourcesmoreovertoencounterwithinCGIARProcess,apair ofexcellentmethodswhichmightberegardedassoonasanalyzingtherealHRoperateina Middle.Applyingabestfitstrategy,thesestandardsshouldberegardedagainsttheparticular realitiesofaMiddle
School: Stanford
Course: Marketing To Businesses
notenoughworkerspreparaorperhapscoaching(communications,retooling)insufficient modifyguidancemanagement(commitment)insufficientmonitorinrenegotiationmechanism duringthemodifyeffo assist modify certainly not implemented because planned) in excess of commit
School: Stanford
Course: Marketing To Businesses
and research sites loss in crucial muscle size for fruitful expert connection, and loss in programmaticcohesioncausedbygeogdecentralizationempowermentnsufficientcoachingand prep Center workers produce research with the highest requirements of systematic r
School: Stanford
Course: Marketing To Businesses
tricktim.Submobilityation Efficiencymanagement Codeofperformsystem Staffsarenotinspired,havingsequencesassociatedwithhighqualitymoreovertoelinesssuch asitemsthreatsconsistofdemotivationyofficenuisanceinadequateinnernotifyanddevices Staffareorganized,energ
School: Stanford
Course: Marketing To Businesses
attractthemandatorytypessuchascandidates 'smarketplaces,manifestationthe ohydrates HRorganizedplanning Personnelrecruitment Specialistrecruitment Remunerationmoreovertorewards Youwilldiscoverthere'smismatchconcerningthabilitiesrequiredandwhatonearthis sta
School: Stanford
Course: Is Stanford A Religion?
Religions cannot be so minimally defined as they historically have been (lecture RS2 01/20/15, lecture RS2 01/13/15). Unlike the explorers of centuries past, today we recognize a diversity of belief and worship that extends far beyond the three monotheist
School: Stanford
Course: Is Stanford A Religion?
Archeologists have found traces of religious thinking at sites dating back longer than we have been fully human (lecture RS2 01/08/15). Religion has been with us since the beginning. But why are people religious? And as society changes in the newly high-t
School: Stanford
Course: Politics And Public Policy
PolicyAnalysisMemo1:PublicOpinioninthePassageoftheCleanAirAct In the late 1960s and early 1970s, large amounts of environmental legislation passed on a federal level, including the creation of the Environmental Protection Agency, the strengthening of the
School: Stanford
Course: Mecanical
Las centrales generadoras Son instalaciones donde se produce la energa elctrica, por conversin a partir de una fuente energtica. UNIVERSIDAD DE TARAPACA Escuela Universitaria de Proceso de generacin Ingeniera Elctrica-Electrnica Energa mecnica Fluido A
School: Stanford
Course: BACK FROM AFRICA WORKSHOP
ItseemstodaythatnothingishappeninginAfricaexceptfortheviolence, whichisplaguingeverysinglecountry.Thetypicalwesternnarrativedictatesthatviolenceis endemictoAfricaandthatitisthewestsresponsibilitytopreservewhateversemblanceof peacethatcouldbemustered.Whati
School: Stanford
Course: MACHINE LEARNING
1 Resampling Detection for Digital Image Forensics John Ho, Derek Ma, and Justin Meyer AbstractA virtually unavoidable consequence of manipulations on digital images are statistical correlations introduced between the pixels. These correlations may not be
School: Stanford
Course: MACHINE LEARNING
Structured Completion Predictors Applied to Image Segmentation Dmitriy Brezhnev, Raphael-Joel Lim, Anirudh Venkatesh December 16, 2011 Abstract Multi-image segmentation makes use of global and local features in an attempt to classify every pixel in an ima
School: Stanford
Course: MACHINE LEARNING
CS229 Project Final Report Sign Language Gesture Recognition with Unsupervised Feature Learning Justin K. Chen, Debabrata Sengupta, Rukmani Ravi Sundaram 1. Introduction The problem we are investigating is sign language recognition through unsupervised fe
School: Stanford
Course: MACHINE LEARNING
CS229/CS229A Final Project Writeup: Supervised Learning - Stock Trend Classifier Submitted: 12/16/2011 ChihChi Kao ckao@stanford.edu 0. Note for teaching staff Unfortunately my project partner, Brain Von Osdol,
School: Stanford
Course: MACHINE LEARNING
SENTIMENT-BASED MODEL FOR REPUTATION SYSTEMS IN AMAZON Milad Sharif msharif@stanford.edu Soheil Norouzi snorouzi@stanford.edu 1. INTRODUCTION When buyers purchase products from an online retailer such as Amazon, they assess and pay not only f
School: Stanford
Course: MACHINE LEARNING
Sentiment Analysis of Twitter Feeds for the Prediction of Stock Market Movement Ray Chen, Marius Lazer Abstract In this paper, we investigate the relationship between Twitter feed content and stock market movement. Specically, we wish to see if, and how w
School: Stanford
Course: MACHINE LEARNING
1 Sentiment Analysis of Occupy Wall Street Tweets Robert Chang, Sam Pimentel, Alexandr Svistunov Acknowledgements Richard Socher, Andrew Maas, and Maren Pearson. I. Introduction T HE rise of social media has changed political discourse around the world by
School: Stanford
Course: MACHINE LEARNING
Personalized News Prediction and Recommendation Abhishek Arora arorabhi@stanford.edu Dept. of Electrical Engineering Stanford University Abstract: There exist many web based news provider applications (e.g. Pulse News reader application for iPhone/iPad an
School: Stanford
Course: MACHINE LEARNING
Predicting Intraday Price Movements in the Foreign Exchange Market Noam Brown Robert Mundkowsky Sam Shiu Abstract It is commonly assumed that short-term price movements follow a random walk and cannot be predicted. However, in this project we predict next
School: Stanford
Course: MACHINE LEARNING
Scaling for Multimodal 3D Object Detection Andrej Karpathy Stanford karpathy@cs.stanford.edu Abstract We investigate two methods for scalable 3D object detection. We base our approach on a recently proposed template matching algorithm [5] for detecting 3D
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Course: MACHINE LEARNING
CS 229 Final Project Reduced Rank Regression Name : Ka Wai Tsang SID : 005589301 1. Introduction Given m observations of the predictors Xi Rp and the corresponding responses Yi Rn , let Y = [Y1 , Y2 , . . . , Ym ]T and X = [X1 , X2 , . . . , Xm ]T . Suppo
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Course: MACHINE LEARNING
Pulse Project: User-Interest-based News Prediction Yinan Na Jinchao Ye Abstract Pulse is a news recommendation app available on both iPhones and android phones. Predicting news of users interest according to their reading history has always been a hot top
School: Stanford
Course: MACHINE LEARNING
Reddit Recommendation System Daniel Poon, Yu Wu, David (Qifan) Zhang CS229, Stanford University December 11th, 2011 1. Introduction Reddit is one of the most popular online social news websites with millions of registered users. A user can submit content
School: Stanford
Course: MACHINE LEARNING
Promoting Student Success in Online Courses Chuan Yu Foo Yifan Mai Bryan Hooi Frank Chen cyfoo@stanford.edu maiyifan@stanford.edu bhooi@stanford.edu frankchn@stanford.edu 1. Introduction 2.1.3. Automatic Tagging Online education has become popular as an e
School: Stanford
Course: MACHINE LEARNING
Unsupervised Morphological Segmentation with Recursive Neural Network Minh-Thang Luong CS224N/CS229 - Final Project Report 1. Introduction parse tree for a word could be derived from the RNN. Recent works have been successful in applying Recursive Neural
School: Stanford
Course: MACHINE LEARNING
CS229 FINAL PROJECT, AUTUMN 2011 1 Predicting Dow Jones Movement with Twitter Esther Hsu (estherh@stanford.edu) Sam Shiu (bwshiu@stanford.edu) Dan Torczynski (dtor1@stanford.edu) CS229 Final Project, Autumn 2011, Stanford University AbstractThe use of mac
School: Stanford
Course: MACHINE LEARNING
Support Vector Machine Classication of Snow Radar Interface Layers Michael Johnson December 15, 2011 Abstract Operation IceBridge is a NASA funded survey of polar sea and land ice consisting of multiple instruments installed on an airborne platform. The S
School: Stanford
Course: MACHINE LEARNING
Sign Language Classication Using Webcam Images Ruslan Kurdyumov, Phillip Ho, Justin Ng December 16, 2011 Abstract Immediate feedback on sign language gestures can greatly improve sign language education. We seek to classify the English sign language alpha
School: Stanford
Course: MACHINE LEARNING
CS 229 - Project Final report Hooyeon Haden Lee; hlee0 (05382015); 12/16/2011 Title: Using Twitter to Estimate and Predict the Trends and Opinions 1 Introduction was set to zero (hence, predicting the same day trends). In another related work From Tweets
School: Stanford
Course: MACHINE LEARNING
NYC Condo Price Estimation Using NYC Open Data Hari Arul Andres Morales Introduction This project explores the structure of the New York City housing market by predicting the price of condominiums in New York City using the publicly available NYC Open Dat
School: Stanford
Course: MACHINE LEARNING
TACTICAL AND STRATEGIC GAME PLAY IN DOPPELKOPF DANIEL TEMPLETON 1. Abstract The German card game of Doppelkopf is a complex game that involves both individual and team play and requires use of strategic and tactical reasoning, making it a challenging targ
School: Stanford
Course: MACHINE LEARNING
CS 229 Project : Improving on Yelp Reviews Using NLP and Bayesian Scoring Patrick Bechon pbechon@stanford.edu 1. Lo Grimaldi Yacine Merouchi leo.grimaldi@stanford.edu merouchi@stanford.edu INTRODUCTION Yelp allows its users to share reviews of local busin
School: Stanford
Course: MACHINE LEARNING
WHAT MAKES FOR A HIT POP SONG? WHAT MAKES FOR A POP SONG? NICHOLAS BORG AND GEORGE HOKKANEN Abstract. The possibility of a hit song prediction algorithm is both academically interesting and industry motivated. While several companies currently attest to t
School: Stanford
Course: MACHINE LEARNING
Yelp+ : 10 Times More Information per View Sean Choi, Ernest Ryu, Yuekai Sun December 16, 2011 Abstract In this project, we take a dierent approach and use whats called the max-norm. In this project we investigate two machine learning methods, one supervi
School: Stanford
Course: MACHINE LEARNING
Building a Better Tour Experience with Machine Learning Alan Guo, Chanh Nguyen, and Taesung Park 1. INTRODUCTION The motivation of this project is to solve a problem that we currently face working on a project called 27bards, which seeks to revolutionize
School: Stanford
Course: MACHINE LEARNING
What It Takes To Win: A Machine Learning Analysis of the College Football Box Score John Hamann Most advanced analysis of sports focus on predicting the results for the next game based on the results of previous games. For college football, the value of p
School: Stanford
Course: MACHINE LEARNING
Machine Learning Applied to Terrain Classication for Autonomous Mobile Robot Navigation John Rogers, Andrew Lookingbill CS 229 Final Project I. I NTRODUCTION We work on the Stanford AI Lab team for the DARPA-funded Learning Applied to Ground Robotics (LAG
School: Stanford
Course: MACHINE LEARNING
GroupTime: Probabilistic Scheduling Kendra Carattini and Mike Brzozowski Introduction Perhaps one of computer-supported cooperative work (CSCW)s greatest successes of the past decade has been group scheduling. But virtually all major groupware systems ava
School: Stanford
Course: MACHINE LEARNING
Anthony S. Guerrero (SCPD n4361176) CS229 Class Project 12/15/2005 Identification of heterozygous point mutation events in DNA sequencing chromatograms. Introduction. The recent discovery of activating somatic mutations in cancer that correlate with pheno
School: Stanford
Course: MACHINE LEARNING
r e q i d n t e r s s u p i t e h ux e f g t e g e d c g ts v x gr g xw r g e d d w d xw eq i d g x i t e s s e t i t g r e g h t e yg q g e hw s p i x iw st i i t e d c x iw s gq u f e t xw x e e t f e d s sw d r g d e d d w d x w e q i d g x i r e g s
School: Stanford
Course: MACHINE LEARNING
Chest Pain in the Emergency Department: Use of Asymmetric Penalties in Sequential Minimal Optimization with Feature Selection to Improve Clinical Decision Making Accuracy Acknowledgements: I would like to extend my gratitude to Dr. Judd Hollander for prov
School: Stanford
Course: MACHINE LEARNING
Sentence Unit Detection without an Audio Signal William Morgan 1 Introduction and motivation Sentence unit (SU) detection is the task of dividing a sequence of words into individual sentences. SU detection is a close relative of sentence boundary detectio
School: Stanford
Course: MACHINE LEARNING
STAIR Subcomponent: Learning to Manipulate Objects from Simulated Images Justin Driemeyer CS229 Term Project December 15, 2005 Overview For my project, I am working with Ashutosh Saxena on a subcomponent of the STanford AI Robot, i.e. STAIR. One of the go
School: Stanford
Course: MACHINE LEARNING
1 Decoding Cognitive States from fMRI Timeseries Catie Chang catie@stanford.edu CS229 Final Project Report I. Introduction Conventional analysis of functional magnetic resonance imaging (fMRI) data follows a regression-based approach, in which one identie
School: Stanford
Course: MACHINE LEARNING
User Authentication Based On Behavioral Mouse Dynamics Biometrics Chee-Hyung Yoon Department of Computer Science Stanford University Stanford, CA 94305 chyoon@cs.stanford.edu Daniel Donghyun Kim Department of Computer Science Stanford University Stanford,
School: Stanford
Course: MACHINE LEARNING
Semantic Extensions to Syntactic Analysis of Queries Ben Handy, Rohini Rajaraman Abstract We intend to show that leveraging semantic features can improve precision and recall of query results in information retrieval (IR) systems. Nearly all existing IR s
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International Journal of Business and Social Science Vol. 2 No.10; June 2011 Comparisons of Competing Models between Attitudinal Loyalty and Behavioral Loyalty Cheng, Shih-I Assistant Professor Department of Business Administration, Shu-Te University, Tai
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International Journal of Business and Social Science Vol. 4 No. 11; September 2013 Efficient Customization of Software Applications of an Organization Rajeev Kumar Assistant Professor Department of Business Administration College of Business Kutztown Univ
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International Journal of Business and Social Science Vol. 4 No. 11; September 2013 Importance of Cross-Cultural Empathy in Selling Perspective from Asian Indians living in the U.S. Duleep Delpechitre, PhD Assistant Professor of Marketing University of Lou
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International Journal of Business and Social Science Vol. 4 No. 3; March 2013 A Study on Determining the Factors That Influence the Customer Value in the Fast Casual Restaurants H. Rafet YUNCU, PhD Anadolu University Faculty of Tourism Department of Gastr
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International Journal of Business and Social Science Vol. 2 No. 14 www.ijbssnet.com Ethics and Customer Loyalty: Some Insights into Online Retailing Services Surendra Arjoon (Corresponding author) Senior Lecturer Department of Management Studies The Unive
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International Journal of Business and Social Science Vol. 4 No. 13; October 2013 An Exploratory Study of Customers Perception of Pricing of Hotel Service Offerings in Calabar Metropolis, Cross River State, Nigeria Juliana B. Akaegbu, MBA Department of Bus
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International Journal of Business and Social Science Vol. 3 No. 21; November 2012 Implications of Service Quality on Customer Loyalty in the Banking Sector. A Survey of Banks in Homabay County, Kenya Arvinlucy Akinyi Onditi Doctoral Student Business Admin
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International Journal of Business and Social Science Vol. 5, No. 6; May 2014 Loyalty Program Factors and How do they affect Customer Behavior Alazzam, Fahad Department of Technology Management School of Engineering University of Bridgeport 126 Park Avenue
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International Journal of Business and Social Science Vol. 4 No. 9; August 2013 Conceptions of Poverty and Wealth in Ghana Christobel Asiedu Department of Social Sciences Louisiana Tech University P. O. Box 9988, Ruston, LA 71272 Vivian A. Dzokoto Departme
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International Journal of Business and Social Science Vol. 2 No. 9 [Special Issue - May 2011] Corporate Governance and Customer Satisfaction Zhe Zhang, Ph.D (Corresponding Author) Assistant Professor Management, Marketing and Administrative Communications
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International Journal of Business and Social Science Vol. 2 No. 11 [Special Issue - June 2011] Students Perspective of Service Quality in Higher Learning Institutions; An evidence Based Approach Mubbsher Munawar khan Ishfaq Ahmed Muhammd Musarrat Nawaz Ha
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International Journal of Business and Social Science Vol. 3 No. 16 [Special Issue August 2012] Assessment of the Importance Level of the Factors Affecting CS according to the Textile Consumers Ikilem Gocek Yesim Iridag Beceren Textile Engineering Departme
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International Journal of Business and Social Science Vol. 3 No. 23; December 2012 Impact of Brand Image, Service Quality and price on customer satisfaction in Pakistan Telecommunication sector Prof. Dr. Muhammad Ehsan Malik1 Muhammad Mudasar Ghafoor2 Hafi
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International Journal of Business and Social Science Vol. 2 No. 16; September 2011 Impact of Customer Satisfaction on Customer Loyalty and Intentions to Switch: Evidence from Banking Sector of Pakistan Faizan Mohsan Hailey College of Commerce, University
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International Journal of Business and Social Science Vol. 3 No. 2 [Special Issue January 2012] THE IMPACT OF CUSTOMER RELATIONSHIP MARKETING ON COSTUMERS' IMAGE FOR JORDANIAN FIVE STAR HOTELS TAREQ N. HASHEM1 Philadelphia University Amman- Jordan Abstract
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Course: Infrastructure Project Development
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Introduc)ontoInforma)onRetrieval Introduc)ontoInforma)onRetrieval Informa)onRetrieval Informa)onRetrieval(IR)isndingmaterial(usually documents)ofanunstructurednature(usuallytext) thatsa)sesaninforma)onneedfromwithinlarge collec)ons(usuallystoredoncompute
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Course: Advanced Analog Integrated Circuit Design
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ESE319 Introduction to Microelectronics BJT Biasing Cont. & Small Signal Model Bias Design Example using 1/3, 1/3, 1/3 Rule Small Signal BJT Models Small Signal Analysis Kenneth R. Laker, updated 18Sep13 KRL 1 ESE319 Introduction to Microelectronics Emi
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4/18/2011 section 5_8 BJT Internal Capacitances 1/2 5.8 BJT Internal Capacitances Reading Assignment: 485-490 BJTs exhibit capacitance between each of its terminals (i.e., base, emitter, collector). These capacitances ultimately limit amplifier bandwidth.
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EE108a Section 3 Handout Number representation Fixed point We can represent numbers that have fractional digits in binary the same way we do in decimal: 0 0 1 1 0 1 . 0 1 1 13.375 = ! ! ! 25=
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Course: Digital Systems I
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EE108a Section 2 Handout More Verilog Parameters Parameters can be declared in the module header: module module_name #( parameter name1 = default1, parameter name2 = default2, ) (
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Course: Digital Systems I
EE108a Section 1 Handout Verilog cheat sheet Datatypes When to use wires and regs: A signal changed in an always block must be delcared as a reg. A signal changed in an assign statement must be declared as a
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Course: Digital Systems I
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Course: Digital Systems I
Lecture 3 Combinational Building Blocks Subhasish Mitra Stanford University subh@stanford.edu Copyright 2014 by Subhasish Mitra With Major contributions from Bill Dally 1 Announcements HW 1 Due Now HW 2 is out Lab 0 is this Thursday You must be enrolled i
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Lecture 7 Design Example Subhasish Mitra Stanford University subh@stanford.edu Copyright 2014 by Subhasish Mitra With Major Contributions from Bill Dally 1 Announcements Debugging Verilog Code Your responsibility to debug your code, TAs may help Lab 3
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Course: Digital Systems I
Lecture 2 Combinational Logic Design Subhasish Mitra Stanford University subh@stanford.edu Copyright 2013 by Subhasish Mitra With Major Contributions from Bill Dally EE108 Lecture 2 1 Announcements Lab & Section Signup Open today! Signup on Coursework i
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Lecture 1 The Digital Abstraction Combinational Logic Representation Verilog Subhasish Mitra Stanford University subh@stanford.edu Copyright 2014 by Subhasish Mitra With Major Contributions from Bill Dally EE108A Lecture 1 1 Lecture Outline Course overvi
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Lecture 5: Design Theory October 6, 2014 Announcements Assignments come out ASAP, but we may not have all the material covered! Always feel free to ask on piazza Ill announce. Data driven: I like poll
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Transac'ons Announcement If I troll you on Piazza, take it as a sign of love. Others PS#3 combined with PS#4. Their sum will be less work. may be some Halloween themed problems s'll Midterm looked great! The
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Course: Intro To Databases
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Course: Intro To Databases
BTrees & Sor*ng 11/3 Announcements I hope you had a great Halloween. Regrade requests were due a few minutes ago Indexing If you dont nd it in the index, look very carefully through the en9re catalog -
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Course: Intro To Databases
Project Overview & Q&A Announcements Awesome talk from Google on Wednesday. Please dont miss it! Next Monday is midterm review. Send quesEons! Wednesday oce hours are canceled Why study Constraints? Prac%cal:
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The Rela(onal Model CS145 Lecture 2 September 24, 2014 Announcements Piazza works! There are links on the page Please please sign up. Almost all course announcements on Piazza. Students with documented disabili(es
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More Constraints October 8 Today Status of the class Review of MVDs Piazza Refresher Ac@vity Logis@cs Midterm Exam Logis@cs The midterm exam moved to Wednesday night, 7pm, October 22nd. 1.5 hours. No room
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Graphs! December 1, 2014 Announcements This is our last technical lecture! Thank you for all your great ques@ons and interes@ng interac@ons Next lecture is our nal review Send ques@ons! All exam logis@cs will
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Query and Join Op/miza/on 11/5 Overview Recap of Merge Join Op/miza/on Logical Op/miza/on Histograms (How Es/mates Work. Big problem!) Physical Op/mizer (if we have /me) Recap on Merge Key (Simple) Idea To
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Transac'ons Results of Audience Poll Fundamentals People: Well get enough to whet your appe'te more in 245, 345, and 346! Users People: Were focused mostly on you to eec'vely use a database and understand the nex
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Lecture 3: Introduc.on to SQL September 29, 2014 Announcements There are good videos. Watch the SQL video. There is an ac.vity next class. SQL Challenge is Due 10/6 SQL Mo.va.on Dark .mes 5 years ago.
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Course: Introduction To Human-Computer Interaction Design
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CS 147: HCI+D UI Design, Prototyping, and Evaluation, Autumn 2014 Prof. James A. Landay Stanford University HCI+D: USER INTERFACE DESIGN + PROTOTYPING + EVALUATION! Hall of Fame or Shame?! Direct translations! software telephony solution where users dial
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CS 147 HCI+D: UI Design, Prototyping, and Evaluation, Autumn 2014 Prof. James A. Landay Stanford University HCI+D: USER INTERFACE DESIGN + PROTOTYPING + EVALUATION! Interface Hall of Fame or Shame?! Design Discovery: ! Contextual Inquiry & Task Analysis!
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CS 147: HCI+D UI Design, Prototyping, and Evaluation, Autumn 2014 Prof. James A. Landay Stanford University HCI+D: USER INTERFACE DESIGN + PROTOTYPING + EVALUATION! Interface Hall of Fame or Shame?! Conceptual Models & ! Interface Metaphors! Prof. James A
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Course: Is Stanford A Religion?
UNDERSTANDING RELIGION: FREUD, DURKHEIM, GEERTZ 1.13.15 JOURNAL PART 1 Fold your paper in half lengthwise. On the left side of the paper, write 1-sentence summaries of what you took to be the key arguments of Freud, Durkheim, and Geertz (Well repeat th
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Farewell! Help yourself to a Last Journal Entry and a donut Your Take on Apocalyptic AI ! Appealing: 1 ! Frightening: 1111 ! Neither: 11 ! Both: 11 ! Religion: 1111 ! Not religion: 111111 ! Unsure/could be both: 11 Climate change and endtimes: , and/or DE
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EAST MEETS WEST IN THE SILICON VALLEY 2.19.2015 - MINDFULNESS Since John Rettger visited, we did not go over these slides in class. Nevertheless, I include them on Coursework in case you want more background for the mindfulness unit. JOURNAL ENTRY Take
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SILICON VALLEY NEW AGE 2.17.2015 Ive got to admit, I kind of want to go here. Everybody here is so friendly and smart, and its beautiful. Whats there not to like? HIGHER ED + BUSINESS Chicago John D. Rockefeller oil, Marshall Field dept stores Duke Duke
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Solutions to Probabilistic Problems 2001 Practice Final December 6th, 2007 1) Solution: a Alice did not violate any of the rules of actional thought. She stated that the only preference she has is that she goes somewhere from
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MS&E 252 Decision Analysis I Midterm Nov 6th, 2007 Midterm Examination MS&E 252: Decision Analysis I Please read the following instructions carefully! 1. This exam is closed book and closed notes, except for a single sheet (1 side). You may use a calculat
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MS&E 252 November 29th, 2001 Handout #25, page 1 of 25 SAMPLE FINAL: EES&OR 252 Final Examination (1997-1998) Please do not begin the exam until you are instructed to do so. Name (printed clearly): _ 1. Count the number of pages in this exam. There should
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CS 245 Midterm Exam Winter 2012 This exam is open book and notes. You can use a calculator and your laptop to access course notes and videos (but not to communicate with other people). You have 70 minutes to complete the exam. Print your name: The Honor C
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MS&E 252 Decision Analysis I Midterm Solutions Nov 14th, 2006 Midterm Examination Solutions Grade Distribution 35 1.00 0.90 30 0.80 25 0.70 0.60 20 0.50 15 0.40 10 0.30 0.20 5 0.10 0 -INF - 0 5 - 10 15 - 20 25 - 30 35 - 40 45 - 50 55 - 60 Cumulative 65 -
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Course: AI
CS229 Practice Midterm 1 CS 229, Autumn 2010 Practice Midterm Notes: 1. The midterm will have about 5-6 long questions, and about 8-10 short questions. Space will be provided on the actual midterm for you to write your answers. 2. The midterm is meant to
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Course: The Fourier Transform And Its Applications
EE261 Raj Bhatnagar Summer 2009-2010 EE 261 The Fourier Transform and its Applications Midterm Examination 19 July 2010 (a) This exam consists of 4 questions with 12 total subparts for a total of 50 points. (b) The questions dier in length and diculty. Do
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MS&E 252 Decision Analysis I HO31 Final Examination: Part I December 3rd, 2002 Final Examination 2001-2002: Part I Please read the following instructions carefully! 1. This exam is closed book and closed notes. You may use one double-sided 8'x11' sheet of
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STATS 217, Winter 2013, Midterm February 12, 2013 Write your name and sign the Honor code in the blue books provided. Please write your name on this question paper and hand it back together with your answer booklet. This is an open material exam. You have
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Course: Principles And Models Of Semiconductor Devices
EE 216 FINAL EXAM Duration: 3 hours Fall 2008 Total Score: 200; #Problems = 8 Make sure to STATE ALL ASSUMPTIONS you make. The following values may be helpful: Ge EG = 0.66 eV at T = 300K Si EG = 1.12 eV at T = 300K NC (Si) = 3 x 1019 cm-3 NV (Si) = 2 x 1
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Course: MODERN APPLIED STATISTICS: LEARNING
Statistics 315a Midterm Exam 4:15-5:30pm, February 19, 2014. You may use the class text, notes, calculators, and computers, but you may not use any device that is connected to the Internet. The questions below require fairly short answers and are of equal
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Course: Principles And Models Of Semiconductor Devices
c hv.jz d u e I+"1- e lec<i. cfw_ ra/ - v o l t e-19 f de '77 = *r" tr = erLlpJX J e=-# o( V = - leax ("/ q<o.bJic- fu) q I 'lea uo, l " P " 6 r^x v lN lr"u p-tL Q"wJ- conv,cts (q) tlr Qa @e Fy'h,-r. " .^*oo b/u Sr X AI , ^,.,- ^ r. lr, + h-rn "- " o", t
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STATS 217, Winter 2013, Midterm February 12, 2013 Write your name and sign the Honor code in the blue books provided. Please write your name on this question paper and hand it back together with your answer booklet. This is an open material exam. You have
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1 CS229 Practice Midterm Solutions CS 229, Autumn 2011 Practice Midterm Solutions Notes: 1. The midterm will have about 5-6 long questions, and about 8-10 short questions. Space will be provided on the actual midterm for you to write your answers. 2. The
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MS&E 120 Probabilistic Analysis Autumn 2007 Final Examination Handout #11, Page 1 of 4 Prof. Ross D. Shachter December 12, 2007 MS&E 120: Probabilistic Analysis Final Examination Three Hours. You will lose credit if you do not turn in your work whe
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Course: Dynamic Systems
MS&E 201 Dynamic Systems Spring 04-05 Final Exam, Page 1 of 8 June 8, 2005 Professor Edison Tse MS&E 201 DYNAMIC SYSTEMS FINAL EXAM 2004-2005 THREE HOURS 180 minutes, total 100 points. Open Book. Open Notes. Write your name on this page of the exam. You w
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CS107 J Zelenski Handout #5 Oct 23, 2009 Midterm practice Midterm Exam: Friday, Oct 30 11am-12:15pm Location TBA The midterm exam is next Friday in-class. Open book/notes You may bring your textbooks, notes, handouts, code printouts, etc. to refer to duri
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School: Stanford
Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2011 Solutions to Midterm Exam 1 1. (10 points) Multiplying periodic functions Let f (t) and g (t) be periodic functions with period 1 and Fourier series expansions given by n= an ei2nt , f (t) = n= n
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EE263 Dec. 56 or Dec. 67, 2008. Prof. S. Boyd Final exam This is a 24 hour take-home nal exam. Please turn it in at Bytes Cafe in the Packard building, 24 hours after you pick it up. Please read the following instructions carefully. You may use any books,
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Course: Dynamic Systems
MS&E201 Dynamic Systems Spring 2007 Professor Edison Tse Page 1 of 14 May 9, 2007 MS&E 201 DYNAMIC SYSTEMS MIDTERM EXAM 75 minutes, total 100 points Open Book. Open Notes. No computers are permitted at the examination. Calculations will be kept as simple
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MS&E 252 Decision Analysis I Midterm Nov 7th, 2006 Midterm Examination MS&E 252: Decision Analysis I Please read the following instructions carefully! 1. This exam is closed book and closed notes, except for a single sheet (1 side). You may use a calculat
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CS 276: Information Retrieval and Web Search Open Book Final Examination This examination consists of 16 pages, 10 questions, and 100 points and counts for 30 percent of your final grade. Please write your answers on the exam paper in the spaces provided.
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Course: Basic Physics For Solid State Electronics
1. Semiconductor carrier statistics (40 points) Consider a semiconductor with a face-centered cubic lattice and with cubic symmetry. The valence band has a maximum at with an energy E = 0 and with an effective mass m0 = me. (me is the mass of a free elect
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Stat 207 Practice Final Friday June 01, 2012 NAME_ SUID _ Rule: Open Book + a single sheet of notes. There are 4 Pages. Initial Every Page. 1. TRUE/FALSE (write TRUE OR FALSE in full) _ The autocorrelation sequence of an AR(1) model xt = xt-1 + wt is equa
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CS229 Practice Midterm Solutions 1 CS 229, Autumn 2010 Practice Midterm Solutions Notes: 1. The midterm will have about 5-6 long questions, and about 8-10 short questions. Space will be provided on the actual midterm for you to write your answers. 2. The
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Course: Machine Learning
STANFORD UNIVERSITY CS 229, Autumn 2012 Midterm Examination XXXX RELEASED SOLUTIONS XXXX Question Points 1 Generalized Linear Models /16 2 Ridge Regression /16 3 Naive Bayes /16 4 Kernel Median Regression /16 5 Uniform Convergence /14 6 Short Answers /34
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Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Midterm Midterm Examination MS&E 252: Decision Analysis I Please read the following instructions carefully! 1. This exam is closed book and closed notes. You may use a calculator and a foreign language dictionary. Please sit i
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Course: Compiler
CS143 Summer 2009 Handout 32 CS143 Practice Final August 8, 2009 Exam Facts The final exam is scheduled for Friday, August 14th at 3:30 p.m. in Skilling 191 and 193. Format The final exam will be a 180-minute written exam. The exam is open-note, closed-bo
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Course: Theory Of Probability
Stat 310A/Math 230A Theory of Probability Midterm Solutions Andrea Montanari November 1, 2010 The midterm was long! This will be taken into account in the grading. We will assign points proportionally to the number of questions answered (e.g. Problem 1 co
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Course: Programming Abstractions (Accelerated)
p CS106X Winter 2008 Handout 29 CS106X Midterm Examination February 19, 2008 This is an open-note, open-book, open-course-reader exam. You can refer to any course handouts, handwritten lecture notes, and printouts of any code relevant to a CS106
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Course: Computer Organization And Systems
CS107 Handout 05 February 21st, 2012 Winter 2012 CS107 Midterm Exam This is an open-note exam. You can refer to any course handouts, handwritten lecture notes, and printouts of any code relevant to a CS107 assignment. Good luck! SUNet ID: _ Last Name: _ F
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Course: Organic Monofunctional Compounds
SECOND EXAMINATION Chemistry 35 Wednesday, November 14, 2007 W. H. Huestis Name: _KEY_ Please print Stanford University ID No. TA and section number:_ I have observed the Stanford Honor Code during this examination. Signature:_ INSTRUCTIONS: This examinat
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Midterm Solutions Nov 14th, 2007 Midterm Examination Solutions Grade Distribution Page 1 of 12 Midterm MS&E 252 Decision Analysis I Midterm Solutions Nov 14th, 2007 Grade Distribution by Question Page 2 of 12 Midterm MS&E 252
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Course: MODERN APPLIED STATISTICS: LEARNING
6 4 2 0 Frequency 8 10 Histogram of Midterm Scores 60 80 100 Midterm Score 120 140
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Course: Database Systems Principles
CS 245 Midterm Exam Winter 2011 This exam is open book and notes. You have 70 minutes to complete it. Print your name: The Honor Code is an undertaking of the students, individually and collectively: 1. that they will not give or receive aid in examinatio
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Course: PROGRAMMING METHODOLOGY
CS106A Winter 2013-2014 Handout 22S February 7, 2014 CS106A Midterm Exam Solutions Problem One: Tower-Building Karel (20 Points) Here are two possible solutions: one using beepersInBag and one without: import stanford.karel.*; import stanford.karel.*; pub
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Course: Programming Abstractions (Accelerated)
CS106X Handout 27S February 5th, 2011 Winter 2011 CS106X Midterm Examination Solution Thanks to the herculean efforts of a dedicated TA and seven wonderful section leaders, your exams are graded and sitting outside my Gates 192 office door. Ill bring them
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CS161 Summer 2013 Handout 15S August 19, 2013 Final Project Solutions The overall distribution of scores on the final project was as follows: 30 25 20 15 10 5 0 0 28 29 33 34 38 39 43 44 48 The overall statistics are Mean: 37.4 / 48 (78%) Median: 38 / 48
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Course: Programming Methodology
CS 106A Handout #37A Aug 12, 2009 Answers to Additional Practice Final Problems Problem Interactors public class InteractorsSample extends GraphicsProgramcfw_ private GLine fwdslash; private GLine backslash; private static final int LINE_WIDTH = 10; priva
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Course: Dynamic Systems
Professor Tse Spring 2007 Dynamic Systems Midterm Solution Problem 1. Short Answer Questions (a) Model Matching Page 1 of 7 The fate of the Universe "Grabber-Holder Model" Reason. The grabber is "expansion push of Big Bang," and the holder is "the gravita
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Course: ECONOMIC GROWTH AND DEVELOPMENT
Economic Growth and Development Professor Olivier de La Grandville Final Answer Keys 1. (40 points) MS&E 249 Autumn 2008 y = a = a(y - ry ) dy a - 1 dr = y a r dy a-1 dr = y a r a-1 ln y = ln r + ln C, where C is the positive constant of integration. a a-
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Course: Introduction To Communication Systems
EE 279 Professor Cox Solution to Final 1. (12pt) a) ii) b) i) iii) c) i) iv) d) vi) 2. (35pt) t Winter 2005-2006 HO # In phase-acceleration modulation we have: f (t ) = f c + K ! x(" )d" . Therefore to recover the signal we should extract the phase
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Course: Computer Organization And Systems
CS107 Handout 06 February 29th, 2012 Winter 2012 CS107 Midterm Exam Solution The CS107 midterms have been graded and were [or are being] handed out during this weeks lab sessions. The exam median was a 39.5 out of 50, the average a 37.4, and the standard
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Course: Economic Analysis II
Econ 51: Final Exam Solution Friday, March 18, 2011 1 Uncertainty and GE (16 points) probability 0.99, and pays nothing with probability 0.01. If your utility function is strictly increasing in money and you are suciently risk loving, you should buy the t
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Course: INVESTMENT SCIENCE
Investment Science Practice Final Exam Problem 1: (Multiple Choice, True/False) A) You are considering a portfolio consisting of positive (>0) amounts of 2 securities with positive correlation between them. The securities have a standard deviation o
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Course: PROGRAMMING METHODOLOGY
CS106A Winter 2013-2014 Handout 18S February 5, 2014 Practice Midterm Solutions Problem 1: Karel is Lost! (24 Points) Here are a few possible solutions: public void run() cfw_ public void run() cfw_ /* Get to the nearest wall. */ /* Get to the nearest wal
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EE 261 Fourier Transform and Applications March 17, 2011 Handout #21 Final Examination Solutions 1. (15 points) Fourier series. A function f (t) with period 1 has the Fourier series coecients n 1 n<0 2 cn = 0 n=0 1n 2 n>0 These Fourier series coecients
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Course: Fourier Transform And Application
EE 261 The Fourier Transform and its Applications Fall 2012 Midterm Exam October 31, 2012 There are ve questions for a total of 85 points. Please write your answers in the exam booklet provided, and make sure that your answers stand out. Dont forget to
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Course: Programming Paradigms
CS107 J Zelenski Handout #12 Dec 11, 2009 Solutions to final exam We devoted the entirety of Friday to wielding the mighty red pens. Exam stats: median 74, mean 73 and standard deviation of 19. Full histogram below: I tried to make the final a bit less cr
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Course: Managerial Accounting
Chapter 008, Activity Based Costing: A Tool to Aid Decision-Making 8B)LO7: ABC absorption costing (Appendix LO5: Product and customer margins LO6: Action Analysis (Appendix 8A) LO4: Second-stage allocation Professional Exam Adapted LO2: First-stage alloca
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Course: Programming Methodology
CS106A Handout 35 May 20th, 2011 Spring 2011 CS106A Practice Exam Midterm exam: Thursday, May 26th, 7:00-10:00 P.M. Last Names A through L: Cubberley Auditorium Last Names M through Z: Annenberg Auditorium This handout is intended to give you practice sol
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Course: Programming Paradigms
CS107 J Zelenski Midterm Solution Handout #8 Nov 2, 2009 Exam stats: median 53, mean 51, and standard deviation 12. The full histogram is below. I was pretty happy with the exam results. There were many extraordinarily good scores, several a point or two
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Course: Introduction To Computer Science | Programming Methodology
2 Solution 1: LevelWorldKarel The most straightforward solution to this problem was to take Karel to the top of the first column above any beepers, and then have her clear all of the beepers within the rows from that level onward. There were other solutio
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Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #13 10/25/2013 Homework Assignment #4 - Solutions Grade Distribution by Question On the next page you will find a breakdown of how well students did on each question. For each question you will see a bar with three dif
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #7 10/14/2007 Homework Assignment #3 Due on Thursday Oct. 18th 11:59 pm In this assignment you are required to turn in the probabilistic section only. Again the "Food for thought" section is optional but will help you
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Problem Session 8 What concepts do we expect you to master for the Final Exam? Medical DA The Foundations of DA PIBP, PISP Relevance The Five Rules U-Curves The Delta Property Sensitivity Analysis Information Gathering, Value
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Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #14 10/27/2013 Homework Assignment #5 Due on Thursday October 31th, 11:59 pm (Updated on October 27th, 10:30 am) Assigned Reading The Foundation of Decision Analysis: Chapter 10 and 11. Distinctions From the class lect
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Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout # 8 Due 10/17/2013 Homework Assignment #3 Due on Thursday Oct. 17th 2013, at 11:59 pm Assigned Readings: 1. The Foundation of Decision Analysis (Course Reader I): Finish reading chapter 5 to 7 2. Collection of Readings
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Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #6 10/4/2013 Homework Assignment #2 Due on Thursday Oct. 10th 11:59 pm Homework Submission Logistics: You can access the MS&E 252 homework submission site from Coursework at http:/coursework.stanford.edu. Click on Subm
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #23 12/7/2007 Homework Assignment #7- Solutions Distinctions These distinctions were prepared by the teaching team and reflect our best belief of the meanings of these terms. A decision diagram shows the structure of a
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Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #2 9/26/2013 Homework Assignment #1 You are not required to turn in any of this assignment; however, we expect you to have full knowledge of the material included. Reading 1: Foundations of Decision Analysis (Course Re
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Course: DECISION ANALYSIS II
MS&E 352 Handout #23 Decision Analysis II Mar 04, 2009 _ Problem Set #3 Solutions Grade Distribution 35% 30% 25% 20% 15% 10% 5% 0% 0-5 6-10 11-15 16-20 21-25 26-30 31-35 36-40 41-45 46-50 51-55 56-60 61-65 66-70 71-75 76-80 81-85 86-90 91-95 96-100 Proble
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #22 11/30/2007 Homework Assignment #7 Due on Thursday, December 6th 11:59 pm In this assignment you are required to turn in the probabilistic section only. Again the "Food for thought" section is optional but will help
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Course: DECISION ANALYSIS II
MS&E 352 Handout #2 Decision Analysis II January 6th, 2009 Problem Set 0 Due: January 13, 2009 _ This problem set is a gentle tutorial to the beta distribution, which we shall use extensively in this class. You will need Excel to complete the assignments.
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #17 11/9/2007 Homework Assignment #6 Due on Thursday November 15th, 11:59 pm In this assignment you are required to turn in the probabilistic section only. Again the "Food for thought" section is optional but will help
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #10 10/22/2007 Homework Assignment #3 Solutions Student Distribution: 40 1 0.9 35 0.8 30 0.7 25 0.6 20 0.5 0.4 15 0.3 10 0.2 5 0.1 0 9.5-10 0-0.5 0.5-1 1-1.5 1.5-2 2-2.5 2.5-3 3-3.5 3.5-4 4-4.5 4.5-5 5-5.5 5.5-6 6-6.5
School: Stanford
Course: MODERN APPLIED STATISTICS: LEARNING
Statistics 315a Homework 1, due Wednesday January 29, 2014. ESL refers to the course textbook, and ESL 2.4 refers to exercise 2.4 in ESL. Since the homework assignments count 70% of your nal grade, you must do them on your own. Problem 1 is computing inte
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Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #23 11/16/2012 Homework Assignment #6 Solutions 60 0 5 10 15 20 25 1 0.9 50 0.8 0.7 40 0.6 0.5 30 0.4 20 0.3 0.2 10 0.1 0 0 Page 1 of 20 HW#6 Solutions MS&E 252 Decision Analysis I Handout #23 11/16/2012 Page 2 of 20 H
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x h s w e p x h s s u w j n h x h n h x j x o x x o h h m m o h x x x r x x x j i x o j n x k x f f u i i w g i u o n x x x x i n x h o x x j o f j i w i j x x k m j k k x n m j j h x p h x f n r x n n i m n n q q w f j h k o f j w l n n k f j o j m x i
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Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #12 10/31/2007 Homework Assignment #4 - Solutions Students Distribution: 60 1 0.9 50 0.8 0.7 40 0.6 30 0.5 0.4 20 0.3 0.2 10 0.1 0 9.5-10 0-0.5 0.5-1 1-1.5 1.5-2 2-2.5 2.5-3 3-3.5 3.5-4 4-4.5 4.5-5 5-5.5 5.5-6 6-6.5 6.
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Course: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Linear & Nonlinear Optimization Fall 2011 Prof Yinyu Ye Homework Assignment 3: SOLUTIONS Problem 1. Sensitivity Analysis: (22 points) [2 points each] You have rented a metal detector for two and a half hours. You can spend your time with it searc
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Course: FINANCE FOR NON-MBAS
Finance Fall 2013 Professor Ishii Problem Set #4 Due: By 3:15 PM on Friday, October 25, 2013 in Lockbox #46 The answers may be either hand-written or typed. If you work in a group, the group should submit only one solution. Please make sure to write the n
School: Stanford
Course: MODERN APPLIED STATISTICS: LEARNING
Statistics 315a Homework 2, due Wednesday February 12, 2014. 1. ESL 3.12 & 3.30 2. (a) Suppose that we run a ridge regression with parameter on a single variable X , and get coecient a. We now include an exact copy X = X , and ret our ridge regression. Sh
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #21 11/20/2007 Homework Assignment # Solutions #6 Question Distribution: Page 1 of 20 HW#6 Solutions MS&E 252 Decision Analysis I Handout #21 11/20/2007 Student Distribution: 45 40 35 30 0.6 25 0.5 20 0.4 15 0.3 10 5 0
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Course: MODERN APPLIED STATISTICS: LEARNING
Stats 315A HW2 Solutions February 17, 2014 If there are any questions regarding the solutions or the grades of HW 2, please contact Austen (ahead@stanford.edu) with Stats315A-hw2-grading in the subject line. Grade Distribution: Total 100 Points Problem 1:
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Course: DECISION ANALYSIS II
MS&E 352 Handout #25 Decision Analysis II March 11th, 2009 _ Problem Set #4 - Solutions Grade Distribution 35% 30% 25% 20% 15% 10% 5% 0% 16-20 21-25 46-50 51-55 56-60 86-90 91-95 26-30 31-35 36-40 41-45 61-65 66-70 71-75 76-80 81-85 96-100 11-15 0-5 6-10
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EE 284 F. Tobagi Autumn 2010-2011 EE284 Homework Assignment No. 1 Topic: Switching Techniques, Network Topologies Handed out: September 21, 2010 Due: September 30, 2010 in class (Previously September 28 but now extended by 2 days) Total Points: 45 ALL WOR
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Course: MODERN APPLIED STATISTICS: LEARNING
Stats 315A HW1 Solutions February, 2014 Grade Distribution: Total 100 Points Problem 1: 30 [6 + 8 + 8 + 8] Problem 2: 10 Problem 3: 15 [3 + 5 + 4 + 3] Problem 4: 20 [17 + 3] Problem 5: 10 Problem 6: 15 [4 + 3 + 4 + 4] Problem 1 Part (a): The code is provi
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Course: MODERN APPLIED STATISTICS: LEARNING
Statistics 315a Homework 2, due Wednesday February 13, 2013. 1. ESL 3.12 & 3.30 2. ESL 3.15 3. (a) Suppose that we run a ridge regression with parameter on a single variable X , and get coecient a. We now include an exact copy X = X , and ret our ridge re
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MS&E 121 Introduction to Stochastic Modeling Prof. Peter W. Glynn Assignment 5 April 24, 2013 Assignment 5 - Due Tuesday, February 19 Note: This material is for the personal use of students enrolled in MS&E 121. Any further distribution, including posting
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MS&E 252 Decision Analysis I Homework 7 Homework Assignment #7 Due on Thursday November 28th 11:59 pm Homework Submission Logistics: You can access the MSE 252 homework submission site from Coursework at http:/coursework.stanford.edu. Click on Submit Home
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School: Stanford
Course: FINANCE FOR NON-MBAS
Finance Fall 2013 Professor Ishii Problem Set #2 Due: By 3:15 PM on Friday, October 11, 2013 in Lockbox #46 The answers may be either hand-written or typed. If you work in a group, the group should submit only one solution. Please make sure to write the n
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Course: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Fall 2011 Linear and Nonlinear Optimization Oct 11, 2011 Prof. Yinyu Ye Homework Assignment 1: Sample Solution Problem 1 Let x1j = tons of waste sent to incinerator j from Palo Alto , x2j = tons of waste sent to incinerator j from Stanford, and y
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Course: Data Analysis
STATS 202 Homework 1 Hao Chen July 3, 2011 In total: 40 points. Problem 2 (26 points, 2 points each) Classify the following attributes as binary, discrete, or continuous. Also classify them as qualitative (nominal or ordinal) or quantitative (interval or
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Course: Introduction To Digital Communication
EE279 Introduction to Digital Communication Handout 13 Solutions to Homework 5 Stanford University Due February 19, 2014 Problem 1. (Average Energy of PAM). (2,2,2 marks) m Solution 1. (a) The pdf of S can be written as fS (s) = i2 m +1 (s (2i 1)a) while
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Assignment2 March 6, 2014 You may discuss homework problems with other students, but you have to prepare the written assignments yourself. Late homework will be penalized 10% per day. Please combine all your answers, the computer code and the gures into o
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Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I 9/26/2013 Homework #1 - Solutions Responsible means we have the ability to choose our response to environmental stimuli. Proactive is the recognition that we are responsible for our own lives. We are where we are today because
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Course: FINANCE FOR NON-MBAS
Finance Fall 2013 Professor Ishii Problem Set #1 Due: By 3:15 PM on Friday, October 4, 2013 in GSB Lockbox #46 WAITLISTED STUDENTS: 1. You are not required to turn in problem sets prior to enrollment in the course. If you choose not to turn in a problem s
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CS 243 Assignment 1 Assignment 1 Dataow Analysis Due: January 25, 11:00 am This is a written assignment, every student must hand in his or her homework. Bring your homework to class on January 25. SCPD students may submit their homework by e-mail via scpd
School: Stanford
Course: MODERN APPLIED STATISTICS: LEARNING
Statistics 315a Homework 2, due Wednesday February 12, 2014. 1. ESL 3.12 & 3.30 2. (a) Suppose that we run a ridge regression with parameter on a single variable X , and get coecient a. We now include an exact copy X = X , and ret our ridge regression. Sh
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Course: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Linear & Nonlinear Optimization Fall 2011 Prof Yinyu Ye Homework Assignment 3: Due Tuesday November 1st at 6:00pm Problem 1. Sensitivity Analysis: (22 points) You have rented a metal detector for two and a half hours. You can spend your time with
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #11 10/26/2007 Homework Assignment #5 Due on Thursday November 1st, 11:59 pm In this assignment you are required to turn in the probabilistic section only. Again the "Food for thought" section is optional but will help
School: Stanford
Course: Modern Applied Statistics: Learning
STATS 315A Winter 2007 Homework 1 Solutions Prob. #1 (Thanks to Wei Zhen) (a) The function mixG takes a centroid matrix mu, a vector N specifying the number of samples in each group and the noise variance v. mixG <- function (mu, N, v)cfw_ mu <- rbind(mu)
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Stanford University Management Science and Engineering Professor Chang & Timucin MS&E 260 Fall 2013/14 MS&E 260: INTRODUCTION TO OPERATIONS MANAGEMENT HOMEWORK #1 Solutions 1. (40 pts.) (a) (15 pts.) DECISION VARIABLES: yk: 1 if transfer station k is sele
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CS161 Summer 2013 Handout 09S July 31, 2013 Problem Set 4 Solutions Problem One: Insertion Sort Revisited (4 Points) Theorem: E[I] = (n2). Proof: For any pair of positions 1 i < j n, let Cij be an indicator random variable that is 1 if the elements at pos
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Course: Digital MOS Integrated Circuits
EE313 Winter 2009-10 J. Kim & M. Horowitz page 1 of 8 SOLUTIONS TO HOMEWORK #2 1. Logical Effort simulations (20 points) The spice deck and Virtuoso schematics /usr/class/ee313/HW2/sol. for this problem can be found in: Delay is measured as the average of
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Course: DECISION ANALYSIS II
MS&E 352 Handout #17 Decision Analysis II February 20th, 2009 _ Problem Set #2 Due Thursday February 5th _ 02/20/09 1 of 12 Problem Set #2 Solutions MS&E 352 Handout #17 Decision Analysis II February 20th, 2009 _ Part I Advanced Information Gathering Prob
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Course: LINEAR AND NON-LINEAR OPTIMIZATION
MS&E 211 Linear and Nonlinear Optimization Prof. Yinyu Ye Fall 2007 Oct 9, 2007 Homework Assignment 2: Sample Solution Problem 1 (a) Let p1 , p2 , p3 be the number of production cycles that Process 1, Process 2 and Process 3 finish, respectively. Then, th
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #15 11/4/2007 Homework Assignment #5 Solutions Students Distribution: 40 1 0.9 35 0.8 30 0.7 25 0.6 20 0.5 0.4 15 0.3 10 0.2 5 0.1 0 3-3.5 6.5-7 8-8.5 9.5-10 1.5-2 0-0.5 1-1.5 3.5-4 4.5-5 5-5.5 5.5-6 6-6.5 0.5-1 2.5-3
School: Stanford
Course: DECISION ANALYSIS I
MS&E 252 Decision Analysis I Handout #2 9/27/2007 Homework Assignment #1 1. Please review the entire course guide, as it contains important information regarding our expectations on the homework assignments. 2. You are not required to turn in any of this
School: Stanford
Course: MODERN APPLIED STATISTICS: LEARNING
HW 3 Solutions March 18, 2013 Grade distribution: Problems 1 - 5: 12 points each, Problem 6: 15 points for writeup, 15 points for computation. Problem 1 a) Let cfw_X, y denote the full original dataset, and let cfw_X(i) , y(i) denote the dataset with the
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Course: ECONOMIC GROWTH AND DEVELOPMENT
Economic Growth and Development Professor Olivier de La Grandville Problem Set 1 To be returned Friday, Oct 3rd , 2008 MS&E 249 Fall 2008 1. In his classic paper, Robert Solow gives the solution of the differential equation for r, corres sponding to the W
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CS161 Summer 2013 Handout 09 July 22, 2013 Problem Set 4 This problem set is all about randomness randomized algorithms, randomized data structures, random variables, etc. By the time you're done with this problem set, we hope that you have a much more nu
School: Stanford
Course: Theory Of Probability
Stat 116 Homework 3 Due Wednesday, April 23th. Please show work and justify answers. No credit for a nal answer with no explanation, even if the answer is correct. 1. Airlines nd that each passenger who books a ight fails to check-in with probability 1 in
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Course: Introduction To Digital Communication
EE279 Introduction to Digital Communication Handout Solutions to Homework 3 Stanford University Due January 29, 2014 Problem 1. (Artifacts of Suboptimality) Let H take on the values 0 and 1 equiprobably. Conditional on H = 0, the observation Y is N (1, 2
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MS&E 121 Introduction to Stochastic Modeling Prof. Peter W. Glynn Assignment 5 Solutions February 20, 2013 Assignment 5 Solutions - Due Tuesday, February 19 Note: This material is for the personal use of students enrolled in MS&E 121. Any further distribu
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Assignment 6: calculating VaR 1. It is October 2011, and you are working in the risk management division of the publicly traded company Pear Inc. Pears core business units are the production of computers, mobile phones, and tablets. Nevertheless, Pear Inc
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Stanford University Management Science and Engineering Professor Chang & Timucin MS&E 260 Fall 2013/14 MS&E 260: Introduction to Operations Management HOMEWORK #3 Due on Monday, October 21st, 2:15pm 1. (25 pts.) A racing bike manufacturer called XMB is us
School: Stanford
Course: DECISION ANALYSIS
MS&E 252 Decision Analysis I Handout #10 11/7/2013 Homework Assignment #3 - Solutions Grade Distribution by Question On the next page you will find a breakdown of how well students did on each question. For each question you will see a bar with three diff
School: Stanford
Course: MODERN APPLIED STATISTICS: LEARNING
20 15 10 5 0 Frequency 25 30 Histogram of HW2 Scores 40 60 80 HW2 Score 100 120
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Course: Circuits I
EE101A/Winter 2013 Prof. Simon Wong Homework #2 (Due Wednesday, 1/23/13) 1. Determine the equivalent resistance measured between the two terminals if all resistors are 1K. (This is a 2D hexagon, NOT a 3D cube.) R =? 2. Use Nodal Analysis to determine the
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Course: DATA STRUCTURES
E40 / Spring 2012 LAB. 1B: SERIAL AND PARALLEL CONNECTIONS The design portion of the prelab is to be done with your lab partner. Each group of two students need to turn in only one report. OBJECTIVES To examine the current, voltage and power characteristi
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Course: EE - Digital CMOS Integrated Circuits
Custom WaveView User Guide Version F-2011.09-SP1, December 2011 Copyright Notice and Proprietary Information Copyright 2011 Synopsys, Inc. All rights reserved. This software and documentation contain confidential and proprietary information that is the pr
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Course: EE - Digital CMOS Integrated Circuits
EE213 Winter 2014-15 M. Horowitz page 1 of 15 VIRTUOSO TUTORIAL Introduction Before starting on this tutorial, please read the first few paragraphs of HW#2 which provide instructions on creating the proper working directory and sourcing the correct files.
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Course: EE - Digital CMOS Integrated Circuits
HSPICE Toolbox for MATLAB Michael Perrott (perrott@mtl.mit.edu) Copyright 1999 by Silicon Laboratories, Inc. 7 October 1999 The Hspice toolbox for Matlab is a collection of Matlab routines that allow you to manipulate and view signals generated by Hspice
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Course: Advanced Analog Integrated Circuit Design
CAD BASICS STANFORD UNIVERSITY Department of Electrical Engineering EE114/EE214A & EE214B Revised: January 2015 1 About This Handout This tutorial is composed of two parts. The first part is a quick start in which you will go through all the steps you nee
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Course: Advanced Analog Integrated Circuit Design
EE214B Winter 2014-15 B. Murmann Page 1 of 7 DESIGN PROJECT Part I due on Monday, March 2, 2015, 5pm Part II due on Wednesday, March 11, 2015, noon Overview In this project you will work on the design of the wideband transimpedance amplifier shown in Figu
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Course: Mecanical
Realizado por :L adislao Ar ce L . SISTEMA AGUJERO Esquema H7 H6 0 0 -50 -50 Ej es d5 e5 f5 g5 h5 j5 j s5 k5 m5 n5 p5 r5 s5 6 -20 -14 -6 -2 0 2 2 4 6 8 10 14 18 0 -24 -18 -10 -6 -4 -2 -2 0 2 4 6 10 8 -30 -20 -10 -4 0 3 2,5 6 9 13 17 0 -35 -25 -15 -9 -5 -2
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Course: Mecanical
UNIVERSIDAD DE ANTOFAGASTA FACULTAD DE INGENIERIA DEPARTAMENTO DE INGENIERIA MECANICA PROYECTO DE DOCENCIA MANUAL DE MECANICA DE SOLIDOS VOLUMEN 2: RESISTENCIA DE MATERIALES RAUL HENRIQUEZ TOLEDO VICTOR VERGARA DIAZ AGOSTO 2010 2 PROLOGO La asignatura de
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Course: Mecanical
Realizado por :L adislao Ar ce L . S I S T E M A EJE N I C O Esquema 0 0 h5 -50 Aguj er o D6 E6 F6 G6 H6 J6 JS6 K6 M6 N6 P6 R6 S6 0 26 20 12 8 6 2 3 0 -2 -4 -6 -10 -14 -4 20 14 6 2 0 -4 -3 -6 -8 -10 -12 -16 0 38 28 18 12 8 5 4 2 -1 -5 -9 -5 30 20 10 4 0 -
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Course: Mecanical
Realizado por :L adislao Ar ce L . SISTEMA AGUJERO Esquema H7 H6 0 0 -50 -50 Ej es d5 e5 f5 g5 h5 j5 j s5 k5 m5 n5 p5 r5 s5 6 -20 -14 -6 -2 0 2 2 4 6 8 10 14 18 0 -24 -18 -10 -6 -4 -2 -2 0 2 4 6 10 8 -30 -20 -10 -4 0 3 2,5 6 9 13 17 0 -35 -25 -15 -9 -5 -2
School: Stanford
Course: Mecanical
PROCEDIMIENTO Para el ensayo de torsin se utiliza una probeta de bronce. Se mide el dimetro y longitud cilndrica de la probeta con el pie de metro. Se monta la probeta de bronce en la mquina universal de torsin mediante dados ubicados en el eje de torsin.
School: Stanford
Course: Mecanical
UNIVERSIDAD DE TARAPAC Escuela Universitaria de Ingeniera Mecnica PROCESOS DE FABRICACIN: Mecanizado Integrantes: JOAQUIN BARRA COOLEF Profesor: Segundo Tarque Fecha de entrega: 29 de octubre de 2014 ARICA - CHILE NDICE Contenido INTRODUCCIN. 3 MARCO TERI
School: Stanford
Course: Mecanical
UNIVERSIDAD DE TARAPAC Escuela Universitaria de Ingeniera Mecnica PROCESOS DE FABRICACIN: Mecanizado Integrantes: JOAQUIN BARRA COOLEF Profesor: Segundo Tarque Fecha de entrega: 29 de octubre de 2014 ARICA - CHILE NDICE Contenido INTRODUCCIN En esta exper
School: Stanford
Course: Mecanical
UNIVERSIDAD DE TARAPAC Escuela Universitaria de Ingeniera Mecnica AJUSTE Y TOLERANCIA Integrantes: JOAQUIN BARRA COOLEF BASTIAN ZIGA HIDALGO Profesor: LADISLAO ARCE LUZA Fecha de entrega: 24 de Septiembre de 2014 ARICA - CHILE NDICE Contenido INTRODUCCIN
School: Stanford
Course: Mecanical
PROBLEMAS DE TRANSFORMADORES ELECTROTECNIA y MAQUINAS ELECTRICAS (2013) Problemas de transformadores Problema 1: Un transformador tiene N1 = 40 espiras en el arrollamiento primario y N2 = 100 espiras en el arrollamiento secundario. Calcular: a. La FEM sec
School: Stanford
Course: Mecanical
UNIVERSIDAD DE TARAPAC Escuela Universitaria de Ingeniera Mecnica ENSAYO DE TRACCIN Alumnos: FRANCISCO MORAGA RAMREZ JAIME RAMREZ VSQUEZ GIUSSEPPE FOPPIANO FIGUEROA JOAQUN BARRA COOLEF SEBASTIN HERESI LORCA DIEGO LEIVA GONZLEZ Profesor: MODESTO MOLLO CALL
School: Stanford
Course: Mecanical
medida incertidumbre medida maxima medida minima D.s D.i Tolerancia Intervalo de diferencias superior e inferior Ajuste y tolerancia extremoo 1 extremo 2 pieza 2 20.011 20.015 30.035 0.002 0.002 0.003 20.013 20.017 30.038 20.009 20.013 30.032 0.013 0.017
School: Stanford
Course: Mecanical
UNIVERSIDAD DE TARAPAC Escuela Universitaria de Ingeniera Mecnica MEDICIONES DE CORRIENTE CONTINUA: Experiencia N2 Nombre: JOAQUIN BARRA COOLEF Profesor: GLORIA CASTRO Electrnica y electrotecnia Fecha de entrega: 07 de noviembre de 2014 ARICA CHILE NDICE
School: Stanford
Course: Mecanical
UNIVERSIDAD DE TARAPAC Escuela Universitaria de Ingeniera Mecnica MEDICIONES DE CORRIENTE CONTINUA Integrantes: JOAQUIN BARRA COOLEF Profesor: GLORIA CASTRO Electrnica y electrotecnia Fecha de entrega: 17 de octubre de 2014 ARICA CHILE NDICE Contenido INT
School: Stanford
Course: Mecanical
UNIVERSIDAD DE TARAPAC Escuela Universitaria de Ingeniera Mecnica MEDICIONES DE CORRIENTE CONTINUA Integrantes: JOAQUIN BARRA COOLEF Profesor: GLORIA CASTRO Electrnica y electrotecnia Fecha de entrega: 17 de octubre de 2014 ARICA CHILE NDICE Contenido INT
School: Stanford
Course: Mecanical
Calculo de (L/r): Para nuestro caso es conocido de manera general que: Situacin 1: empotrado en un extremo y articulado en el otro. En esta situacin particular se establecen los siguientes valores Reemplazando los valores conocidos se tiene que Mientras q
School: Stanford
Course: Programming Abstractions
Eric Roberts CS 106B Handout #47 March 2, 2015 Section Handout #8 Expressions 1. Convert an expression to Reverse Polish Notation Write a program that reads expressions from the user in their standard mathematical form and then writes out those same expre
School: Stanford
Course: Programming Abstractions
HashMap 3/3/15, 7:19 PM The Stanford cslib package #include "hashmap.h" class HashMap<KeyType, ValueType> This class implements an ecient association between keys and values. This class is identical to the Map class except for the fact that it uses a hash
School: Stanford
Course: Electricity And Optics Laboratory
Your Name _ TA Name _ Partner's Name _ Section day/time _ Lab 2: Potentials and Electric Fields The purpose of this lab is to a) Explore electric fields and potentials b) Map electric potentials for various charge configurations and c) Develop an intuitiv
School: Stanford
Course: Electricity And Optics Laboratory
Your Name _TA Name _ Partners Name _Section day/time _ Lab 1: An Introduction to Instruments Welcome to PH24, where you will explore concepts from electricity, magnetism, and optics. In addition, the purpose of the lab is to make you familiar with the phy
School: Stanford
Course: Electricity And Optics Laboratory
Stanford University Introductory Physics Laboratories Last Updated 12/16/14 Introductory Physics Laboratories Welcome to PH24, Electricity and Optics Laboratory. The labs in this course are designed to complement material presented in PH23. You will condu
School: Stanford
Course: Electricity And Optics Laboratory
Your Name _ TA Name _ Partner's Name _ Section day/time _ Lab 6: Transformers 50 km Figure 1. Power transmission Why are high voltage power lines used to bring electricity from power plants when you, the end-user, use 120V alternating current (AC) at your
School: Stanford
Course: Electricity And Optics Laboratory
Date/Time _ Name _ Lab Partner(s) _ TA_ LAB 9: LENSES AND OPTICAL INSTRUMENTS This lab follows up on material you have covered in lecture on spherical lenses and optical instruments. The pre-lab questions are designed to prepare you for the lab and are
School: Stanford
Course: Electricity And Optics Laboratory
Your Name _ TA Name _ Partners Name _ Section day/time _ Lab 7: Interference of Light Wave Optics Introduction Light can behave like a wave or a particle. As a wave, it can combine either constructively or destructively with other waves to give rise to in
School: Stanford
Course: Electricity And Optics Laboratory
Your Name _TA Name _ Partners Name _Section day/time _ Lab 4: Magnetism The goal of this lab is to understand the basics of magnetic fields. This lab has five parts: The first two are pre-labs that must be completed before coming to lab. The next two par
School: Stanford
Course: Electricity And Optics Laboratory
Your Name _ Partner's Name _ TA Name _ Section day/time _ LAB 5: FARADAY'S LAW Moving electric charges cause magnetic elds. Most readily this was seen in the magnetic elds around current-carrying wires. We next explore the symmetric question that can be s
School: Stanford
Course: Electricity And Optics Laboratory
Lab 3: Simple DC Circuits The purpose of this lab is to give you a better understanding of basic circuits with resistors. You will then use your understanding to design circuits for specific functions. This lab has in-lab activities and a pre-lab that mus
School: Stanford
Course: Electricity And Optics Laboratory
LAB 8: GEOMETRICAL OPTICS Reflected light follows the laws of reflection: (a) the incident and reflected beams, and the normal to the surface, are all on one plane; and (b) the angle of incidence is equal to the angle of reflection where the two angles ar
School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Programming Abstractions
Eric Roberts CS 106B Handout #32 February 9, 2015 Section Handout #5 The EditorBuffer Class Problem 1. The gap-buffer form of the stack model (Chapter 13, exercise 2, page 610) Even though the stacks in the stackbuf.cpp implementation of the EditorBuffer
School: Stanford
Course: Programming Abstractions
Eric Roberts CS 106B Handout #32A February 9, 2015 Solutions to Section Handout #5 Problem 1. Implementing the array-with-gap form of the two stack model 2 3 4 Problem 2: Doubly linked lists 5 6
School: Stanford
Course: Programming Abstractions
Eric Roberts CS 106B Handout #38 February 18, 2015 Section #6Trees For problems 1, 2, and 3, assume that BSTNode is defined as follows: struct BSTNode cfw_ string key; BSTNode *left, *right; ; 1. Tracing binary tree insertion (Chapter 16, review question
School: Stanford
Course: Data Mining And Analysis
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School: Stanford
Course: Data Mining And Analysis
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School: Stanford
Course: Data Mining And Analysis
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School: Stanford
Course: Data Mining And Analysis
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School: Stanford
Course: Data Mining And Analysis
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School: Stanford
Course: Elliptic Curves In Cryptography
lab10sol November 10, 2014 1 Virtual Lab 10 Solution: Biased Coin 1.0.1 EECS 70: Discrete Mathematics and Probability Theory, Fall 2014 Due Date: Monday, November 10th, 2014 at 12pm Login: cs70-ta Instructions: Name: EECS 70 Please ll out your name and l
School: Stanford
Course: Elliptic Curves In Cryptography
lab9sol November 2, 2014 1 Virtual Lab 9 Solution: Intro to Randomness (cont.) 1.0.1 EECS 70: Discrete Mathematics and Probability Theory, Fall 2014 Due Date: Monday, November 3rd, 2014 at 12pm Login: cs70Instructions: 1.1 Name: Please ll out your name an
School: Stanford
Course: Elliptic Curves In Cryptography
lab8sol October 27, 2014 1 Virtual Lab 8 Solution: Intro to Randomness 1.0.1 EECS 70: Discrete Mathematics and Probability Theory, Fall 2014 Due Date: Monday, October 27th, 2014 at 12pm Login: cs70Instructions: 1.1 Name: Please ll out your name and login
School: Stanford
Course: Elliptic Curves In Cryptography
lab7sol October 13, 2014 1 Virtual Lab 7 Solution: Polynomials, Secret Sharing, and Histograms 1.0.1 EECS 70: Discrete Mathematics and Probability Theory, Fall 2014 Due Date: Monday, October 20th, 2014 at 12pm Login: cs70-ta Instructions: Name: EECS 70 P
School: Stanford
Course: Elliptic Curves In Cryptography
lab6sol October 9, 2014 1 Virtual Lab 6 Solution: Public Key Cryptography and Lagrange Interpolation 1.0.1 EECS 70: Discrete Mathematics and Probability Theory, Fall 2014 Due Date: Monday, October 13th, 2014 at 12pm Instructions: Complete this lab by lli
School: Stanford
Course: Elliptic Curves In Cryptography
VirtualLab5Solution:ChineseRemainderTheoremand Euler'sTheorem EECS70:DiscreteMathematicsandProbabilityTheory,Fall2014 DueDate:Monday,October6th,2014at12pm Instructions: Completethislabbyfillinginalloftherequiredfunctions,markedwith" O R C D H R " YU OE EE
School: Stanford
Course: Elliptic Curves In Cryptography
lab14sol December 8, 2014 1 Virtual Lab 14 Solution: Random Variables and Distributions 1.0.1 EECS 70: Discrete Mathematics and Probability Theory, Fall 2014 Due Date: Monday, December 8th, 2014 at 12pm Login: cs70Instructions: Name: Please ll out your n
School: Stanford
Course: Elliptic Curves In Cryptography
lab12sol November 17, 2014 1 Virtual Lab 12 Solution: Hashing & Drunk Man 1.0.1 EECS 70: Discrete Mathematics and Probability Theory, Fall 2014 Due Date: Monday, November 24th, 2014 at 12pm Login: cs70-ta Instructions: Name: EECS 70 Please ll out your na
School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Elliptic Curves In Cryptography
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School: Stanford
Course: Mecanical
C) Determinacin del Mdulo de rigidez Probeta: D= 6 (mm) L= 76, 2 (mm) Tomando el punto 10 de la zona elstica en la grfica torque v/s giro: T= 5,9 (Nm) = 10=0,1745 (rad) El mdulo de rigidez de la probeta se calcular con la frmula: Dnde: = longitud parte ci
School: Stanford
Course: Mecanical
Procedimiento experimental - En esta experiencia prctica se analizaran dos probetas de materiales distintos: bronce y acero. - Se realiza la medicin del dimetro de las probetas, con el pie de metro. - En la mquina de ensayo de cizalle se instala el cabeza
School: Stanford
Course: Mecanical
Introduccin: El presente informe se basa en el anlisis desarrollo y compresin de los conceptos bsicos de la tolerancia y los ajustes. Se estudiara cada instrumento de medicin conociendo las caractersticas tcnicas de estos, como su divisin de escala, el ra
School: Stanford
Course: Introduction To Statistical Inference
STAT 426 Lecture 34 Fall 2012 Arash A. Amini September 13, 2012 1 / 35 Announcements My oce hours: Tue 4 5p in 470 West Hall, Wed 12 1p in 438 West Hall Yingchuans oce hours: Wed 2:30 3:30p in 274 West Hall Fri 9:30 10:30a in 274 West Hall Final exam: Wed
School: Stanford
Course: Introduction To Statistical Inference
STAT 426 Lecture 2324 Fall 2012 Arash A. Amini December 6, 2012 1 / 26 Outline Muddiest points: Those that were/are least clear throughout the course. Write down 13 muddiest points and turn them in at the end of class. NeymanPearson Paradigm Randomized de
School: Stanford
Course: Introduction To Statistical Inference
STAT 426 Lecture 25 Fall 2012 Arash A. Amini December 11, 2012 1 / 12 Outline Final will focus on the material after midterm: Everything in Chapter 8, except: 8.6.2 on large sample normal approx. to the posterior, 8.6.3 on computation aspects of the Bayes
School: Stanford
Course: Introduction To Statistical Inference
STAT 426 Lecture 22 Fall 2012 Arash A. Amini November 29, 2012 1 / 15 Outline Hypothesis testing Bayesian formulation Maximum a posteriori probability (MAP) test Likelihood ratio test Example: Bernoulli trials Terminology Bayesian optimality Bayes risk 2
School: Stanford
Course: Introduction To Statistical Inference
Properties of conditional expectation Suppose that we have two random variables X and Y . Then: (a) E[h(X ) | X ] = h(X ). (b) E[Y h(X ) | X ] = h(X ) E[Y |X ]. (c) If X and Y are independent, we have E[X |Y ] = E[X ]. Here is an example of how to use the
School: Stanford
Course: Introduction To Statistical Inference
STAT 426 Lecture 21 Fall 2012 Arash A. Amini November 29, 2012 1 / 34 Outline Bayesian inference Decision-theoretic setup Prior, Bayes risk Posterior Example 1: Bernoulli trials Posterior mean as a point estimate Concentration of posterior around true par
School: Stanford
Course: Accounting Information
ACCOUNTING for LABOR and PAYROLL ACCOUNTING Learning Objectives At the end of this module, the students are expected to be able to: Define labor and payroll terms Compute payroll and prepare payroll sheets Compute withholding taxes due, SSS, Philhealth
School: Stanford
Course: Accounting Information
Table of Contents PART 1. Costs: Concepts and Objectives Chapter 1. Management, the Controller, and Cost Accounting Chapter 2. Cost Concepts and the Cost Accounting Information System Chapter 3. Cost Behavior Analysis PART 2. Cost Accumulation Chapter 4.
School: Stanford
Course: Accounting Information
1 Accounting for Labor Cost Objectives SEQUARELL 2009/USED UNDER LICENSE FROM SHUTTERSTOCK.COM After completing this chapter, you should be able to answer the following questions: 1 LO.1 LO.2 LO.3 LO.4 LO.5 What are the procedures in controlling labor co
School: Stanford
Course: Accounting Information
Lei Boar Inc. in Davao City, is raising hogs and selling them to public market merchants. The following are payroll policies and assumptions: 1. Working days Monday to Saturday; 2. Daily working hours ( 6 am to 12 noon; 1 pm to 6 pm ) 3. Tardiness is dedu
School: Stanford
Course: Accounting Information
DEDUCTIONS DAILY HOLIDAY RATE BASIC PAYPREMIUM OT PAY UNDERTIME GROSS PAY SSS LOAN CASH ADV HDMF NAME WTAX TOTAL NET PAY ART TEE 394.52 6,000.00 6,000.00 100.00 332.92 432.92 5,567.08 LEE SUD 295.89 4,500.00 473.42 2,189.22 (46.23) 7,116.41 100.00 766.70
School: Stanford
Course: Information Systems
Chapter 15 Running Case Solution 1. Which countries would provide the best markets for Dirt Bikes products? Your analysis should consider factors such as: In which countries are dirt bikes popular? What is the per capita income of these countries? Which c