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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: Introductory Economics B
Chapter 24 Notes The Aggregate Demand Curve 1. Relationship between two economic variables: real GDP and the inflation rate a. Real GDP is related negatively to inflation along the curve b. Movements of real GDP away
School: Stanford
Course: Mathematics Of Sports
STATS 50 2014 How Do Injuries in the NFL Affect The Outcome of the Game Andy Sun Background: Jenny Vrentas from the MMQB points out, the NFLs injury surveillance data shows a slow upward trend in the total number of injuries sustained in all practices and
School: Stanford
Course: Mathematics Of Sports
Anthony Tsodikov NFL Overtime-Is an Onside Kick Worth It? Its the NFC championship and the 49ers are facing the Seahawks. The game has just gone into overtime and the Seahawks win the coin toss. The Seahawks choose to receive and immediately the odds slid
School: Stanford
Course: Mathematics Of Sports
Evaluating RPI: Ranking College Basketball Teams Saj Sri-Kumar and Sarah Rosston The Ratings Percentage Index, or RPI, is one of the most important tools that the NCAA Division I Mens Basketball Tournament Selection Committee uses to determine which teams
School: Stanford
Course: Mathematics Of Sports
Wu 1 STATS 50 Final Project Tony Wu NFL Power Rankings through Bradley-Terry In this project, I apply the Bradley-Terry model to the NFL using results from the 208 games that have been played up through Week 14. Ive developed two models, one purely based
School: Stanford
Course: Mathematics Of Sports
Matt Halper 12/10/14 Stats 50 The Batting Pitcher: A Statistical Analysis based on NL vs. AL Pitchers Batting Statistics in the World Series and the Implications on their Teams Success in the Series Matt Halper In this extended abstract, I will examine th
School: Stanford
Course: Mathematics Of Sports
Casey Tucker M08 100 Abstract Evolution of the Physicality in Football Physics of Football: How much has the game of football changed since 1950? How much force would Ngata generate with a 10 yard get off? Determining the force of the hit measured by one
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: Data Mining And Analysis
Lab 26: Missing data In this lab, we will work with the NLSY79 dataset. This is a longitudinal study from the Bureau of Labor Statistics, which followed a cohort of a few thousand baby boomers from 1979 until 2010, recording hundreds of variables every th
School: Stanford
Course: Data Mining And Analysis
Lab 1: Illustration of the bias-variance decomposition library(ggplot2) library(splines) set.seed(1) Define a true function f. f = function(x) cfw_ x^2 - 0.2*x^2.3333 Now, we sample a random observation of the function at 10 input points with normal erro
School: Stanford
Course: Data Mining And Analysis
Lab 11: The wrong way and the right way to do cross-validation In this lab, we simulate the wrong way and the right way to perform cross validation, as explained in Lecture 11 and in Section 7.10 of the Elements of Statistical Learning. We will work under
School: Stanford
Course: Data Mining And Analysis
Lab18:Analysisofopinioneditorialsfrom twoStanfordstudentnewspapers Stanford has two large student newspapers. The Stanford Daily is the main campus tabloid, while the Stanford Review publishes conservative-leaning political articles on a biweekly basis. E
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: Solid State Physics II
TutorialonPC1D MohitMehta ProgramDescription PC1Dsolvesthefullycouplednonlinearequationsforthe quasi1dtransportofelectrons&holesincrystalline semiconductordevices,withemphasisonphotovoltaic devices. OnlyfilerequiredtoruntheprogramisPC1D.exe. PC1D.hlppro
School: Stanford
Course: Theory Of Probability
Stat 116: Practice Final December, 2014 The exam consists of 8 problems. As a resource you may use a calculator and two pages of notes. 1. Two points are selected randomly on a line segment of length L, so as to be on opposite sides of the mid-point of th
School: Stanford
Course: Theory Of Probability
Two More Problems Here are two more practice problems. Number 6 (essentially 4.13 in the text) is a substitute for Number 2 of the original practice exam, since that problem already appeared in a homework assignment. Number 5 is included for your amusemen
School: Stanford
CME 193 1 Introduction to Python Exercises Basics Exercise 1.1: The interpreter Open the Python interpeter. What happens when you input the following statements: (a) 3 + 1 (b) 3 * 3 (c) 2 * 3 (d) "Hello, world!" Exercise 1.2: Scripts Now copy the above to
School: Stanford
Course: Consulting Club Guides
The MIT Sloan School of Management Management Consulting Club Case Book and Interview Guide October 2001 The MIT Sloan School of Management Management Consulting Club Many thanks to the MCC Case Book and Interview Guide Sponsors: Gold Sponsor: Case Contri
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
School: Stanford
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
School: Stanford
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
School: Stanford
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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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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grad.) ZISL hwZ Sample. Sk/M foluk05 ' .- Text Problem 10.1 .r' A. I L.) Using (10.10), we dene Gm = argmin wÂ§")1(yi gÃ© G(zi)). Then the optimization problem for solving for ,6, is: N lm = argminp Z wgm)ewp[~inm($i)l it->- ' We take the derivative
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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: 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: Introductory Economics B
Chapter 24 Notes The Aggregate Demand Curve 1. Relationship between two economic variables: real GDP and the inflation rate a. Real GDP is related negatively to inflation along the curve b. Movements of real GDP away
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Course: Introductory Economics B
Ch. 30 Notes Exchange Rates Important Definitions 1. Exchange Rate: a number of units of foreign currency that are needed to purchase one unit of the domestic currency the price of a unit of domestic currency
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Course: Introductory Economics B
Chapter 23 Notes Changes in Aggregate Demand Lead to Changes in Production 1. Economic Fluctuations occur simultaneously with long-term growth 2. Economic Fluctuations have common features a. After a departure of real GDP from potential GDP, the econo
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Course: Introductory Economics B
Chapter 28 Notes Catching Up or Not? 1. Growth Accounting Formula a. An increase in the growth of technology leads to an increase in productivity growth b. Productivity Growth Rate = 1/3 (Growth Rate of Capital/Per Hour of Work) + (Growth Rate of Tech
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Course: Introductory Economics B
Chapter 17 Notes 1. Real GDP a. Measure of production in an economy b. Real: means that we adjust the measure of production to account for changes in prices (inflation) over time c. Most comprehensive measure of how
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Course: Introductory Economics B
Ch. 26 Notes Fiscal Policy The Government Budget: 1. Federal Budget: the major document describing fiscal policy in the US a. Includes the estimates of the surplus or deficit that get so much attention as well a
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Course: Introductory Economics B
Ch. 27 Notes Vocab 1. Discount Rate: the interest rate that the FED charges commercial banks when they borrow from the FED 2. Monetary Base: currency plus reserves a. Measure of the size of the FEDs balance sheet
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Course: Introductory Economics B
Three Ways to Measure GDP 1. Spending Approach a. I.e. Aggregate Spending b. Four Components i. Consumption (Consumers) 1. Purchases of final goods and services by individuals 2. Accounts for 71% of GDP in US ii. I
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Course: Introductory Economics B
Ch. 25 Notes Changes in Government Purchases Real GDP and Inflation Over Time 1. Short Run: initial depart of real GDP from potential GDP 2. Medium Run: the recover period 3. Long Run: time at which real GDP is
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Course: Introductory Economics B
Ch. 20 Notes How is Unemployment Measured? 1. Current Population Survey: a monthly survey of a sample of US households done by the US Census Bureau, Measures employment, unemployment, and labor force Who is Empl
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Course: Introductory Economics B
Chapter 19 Notes The Spending Shares 1. Y=C+I+G+X a. Y: GDP, C: Consumption, I: Investment, X: Net Exports Defining the Spending Shares 1. Consumption Share a. The proportion of GDP that is used for consumption: equals
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Course: Introductory Economics B
Ch. 22 Notes What Is Money? a. To an economist, money includes the portion of a persons wealth that can be used easily for transactions Three Functions of Money 1. Medium of Exchange: a. An item that people ar
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Course: Introductory Economics B
Chapter 21 Notes 1. Diminishing Returns To Labor a. A situation in which successive increases in the use of an input, holding other inputs constant, eventually will cause a decline in the additional production derive
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Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2014 Lecture 8 April 18, 2014 Prof. Emmanuel Candes Scribe: Feng Ruan 1 Outline Agenda: 1. Hochbergs procedure 2. False Discovery Rate (FDR) 3. Properties of FDR 4. Procedures for Controlling FDR (BH(q) 2 False Disc
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Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2015 Lecture 18 May 11, 2015 Prof. Emmanuel Candes Scribe: Alexandra Chouldechova and E. Candes 1 Outline 1. Cp statistic 2. Model selection with Cp /AIC? 2 The Cp statistic Last time, we considered the linear model
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Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2015 Lecture 19 May 13, 2015 Prof. Emmanuel Candes Scribe: Will Fithian and E. Candes 1 Outline Today we will put Cp in a broader context by discussing the Akaike information criterion (AIC) and discuss an alternati
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Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2015 Lecture 15 May 4, 2015 Prof. Emmanuel Candes Scribe: Alexandra Chouldechova and E. Candes 1 Outline Agenda: Estimation of a Multivariate Normal Mean 1. Steins Phenomenon 2. James-Stein Estimate 3. Steins Unbias
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Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2015 Lecture 17 May 8 2015 Prof. Emmanuel Candes Scribe: Will Fithian and E. Candes 1 Outline Today, we will consider model selection and estimation in linear models. 1. Linear Model 2. Model Selection 3. Prediction
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Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2014 Lecture 10 April 20, 2014 Prof. Emmanuel Candes Scribe: Emmanuel Candes, Pragya Sur 1 Outline Agenda: FDR 1. FDR control under dependence 2. FDR control under PRDS 3. Examples of PRDS distributions Recall that
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Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2015 Lecture 9 April 17, 2015 Prof. Emmanuel Candes Scribe: Emmanuel Candes 1 Outline Agenda: 1. Asymptotic properties of BHq 2. FDR control under dependence Last time, we introduced the BHq procedure and showed tha
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Course: Theory Of Statistics
Spring 2015 Stats 300C: Theory of Statistics Lecture 11 April 22, 2015 Prof. Emmanuel Candes Scribe: Emmanuel Candes 1 Outline Agenda: Multiple Testing Problems 1. FDR Control 2. Empirical Process View Point 3. Martingale Proof of FDR (BH(q) The material
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Course: Theory Of Statistics
Spring 2015 Stats 300C: Theory of Statistics Lecture 13 April 29, 2015 Prof. Emmanuel Candes Scribe: Evan Patterson, Lexi Guan, and E. Cand`s e Agenda: FDR control for regression problems 1. FDR control under correlation 2. FDR control for linear regressi
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Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2015 Lecture 12 April 27, 2015 Prof. Emmanuel Candes Scribe: A. Chouldechova, S. Wager, and E. Candes 1 Outline Agenda: An empirical Bayes interpretation of multiple testing 1. The Bayesian hypothesis testing proble
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Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2015 Lecture 3 April 3, 2015 Prof. Emmanuel Candes Scribe: Will Fithian (2012) and E. Candes 1 Outline Agenda: Global testing 1. 2 test 2. Detection Thresholds for Small Distributed Eects Global Testing: Recall that
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Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2015 Lecture 4 April 6, 2015 Prof. Emmanuel Candes Scribe: Jeha Yang 1 Outline Agenda: Global testing 1. Numerical illustration 2. Simes Test 3. Tukeys Second-Level Signicance Testing 4. Proof of Proposition 1 from
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Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2015 Lecture 5 April 8, 2015 Prof. Emmanuel Candes Scribe: Yu Bai 1 Outline Agenda: Global testing 1. Tukeys Higher Criticism 2. Detecting Sparse Heterogeneous Mixtures 3. Threshold Phenomenon Goals: 1. Understand t
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Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2015 Lecture 7 April 13 2015 Prof. Emmanuel Candes Scribe: S. Wager, E. Candes 1 Outline Agenda: From global testing to multiple testing 1. Testing the global null vs. FWER 2. The closure principle: using global tes
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Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2015 Lecture 6 April 10 2015 Prof. Emmanuel Candes Scribe: E. Candes 1 Outline Agenda: Mutiple testing/comparison problems 1. The problem of multiple testing 2. Procedure for controlling FWER 3. Weak/strong control
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Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2015 Lecture 2 April 1, 2015 Prof. Emmanuel Candes Scribe: Paulo Orenstein 1 Outline Agenda: Global testing 1. Needle in a Haystack Problem 2. Threshold Phenomenon 3. Optimality of Bonferronis Global Test Last time:
School: Stanford
Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2015 Lecture 16 May 6, 2015 Prof. Emmanuel Candes Scribe: Will Fithian and E. Candes 1 Outline 1. Steins Phenomenon 2. Empirical Bayes Interpretation of James-Stein (JS) 3. Extensions 4. A Famous Baseball Example Th
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Course: Theory Of Statistics
Stats 300C: Theory of Statistics Spring 2015 Lecture 1 March 30, 2015 Prof. Emmanuel Candes Scribe: Gene Katsevich 1 Introduction: Multiple Hypothesis Testing The multiple hypothesis testing problem is the situation when we wish to consider many hypothese
School: Stanford
Course: Mathematics Of Sports
STATS 50 2014 How Do Injuries in the NFL Affect The Outcome of the Game Andy Sun Background: Jenny Vrentas from the MMQB points out, the NFLs injury surveillance data shows a slow upward trend in the total number of injuries sustained in all practices and
School: Stanford
Course: Mathematics Of Sports
Anthony Tsodikov NFL Overtime-Is an Onside Kick Worth It? Its the NFC championship and the 49ers are facing the Seahawks. The game has just gone into overtime and the Seahawks win the coin toss. The Seahawks choose to receive and immediately the odds slid
School: Stanford
Course: Mathematics Of Sports
Evaluating RPI: Ranking College Basketball Teams Saj Sri-Kumar and Sarah Rosston The Ratings Percentage Index, or RPI, is one of the most important tools that the NCAA Division I Mens Basketball Tournament Selection Committee uses to determine which teams
School: Stanford
Course: Mathematics Of Sports
Wu 1 STATS 50 Final Project Tony Wu NFL Power Rankings through Bradley-Terry In this project, I apply the Bradley-Terry model to the NFL using results from the 208 games that have been played up through Week 14. Ive developed two models, one purely based
School: Stanford
Course: Mathematics Of Sports
Matt Halper 12/10/14 Stats 50 The Batting Pitcher: A Statistical Analysis based on NL vs. AL Pitchers Batting Statistics in the World Series and the Implications on their Teams Success in the Series Matt Halper In this extended abstract, I will examine th
School: Stanford
Course: Mathematics Of Sports
Casey Tucker M08 100 Abstract Evolution of the Physicality in Football Physics of Football: How much has the game of football changed since 1950? How much force would Ngata generate with a 10 yard get off? Determining the force of the hit measured by one
School: Stanford
Course: Mathematics Of Sports
Stanford Mens Varsity Golf Team Performance Analysis: Informing Putting and Approach Shot Practice and Strategy on a Risk-Reward Basis Lindsey Kostas, Luke Lefebure, Sam Sarpong, Chris Sebastian INTRODUCTION Two of the most important shots in golf are the
School: Stanford
Course: Mathematics Of Sports
AdamWells,ZachDammel,BradleyKnox Stats50FinalProject December10,2014 ValueofCoursePosition Inclass,welearnedaboutthevalueoffieldpositioninfootballabouthowthe expectedoutcomevariesdependingontheyardage.Forourfinalproject,wedecidedtoapply thatsameconcepttog
School: Stanford
Course: Mathematics Of Sports
Walker Szurek Stats 50: The Mathematics of Sports Project Abstract 2 December 2014 Assessing NBA General Manager Performance The purpose of this project is to determine a methodology for assessing general manager performance in the National Basketball Ass
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Course: Mathematics Of Sports
Pairwise Comparison Models: A Two-Tiered Approach to Predicting Wins and Losses for NBA Games Tony Liu Introduction The broad aim of this project is to use the Bradley Terry pairwise comparison model as the basis for finding strong predictive models for N
School: Stanford
Course: Mathematics Of Sports
Daniel Allen 12/2/14 MCS 100 Leonid Pekelis The Single to Right Field: Why Left-handed batters may be undervalued one Mike Trout Unit Abstract This paper presents a scrappy but (Id argue) coherent analysis of how a specic play in baseball is unaccounted f
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Course: Mathematics Of Sports
Nicky Sullivan 5-Page Extended Abstract Who Wins in the MLB Playoffs? Comparing Pythagorean record to actual record to see which is a better predictor of success in the playoffs Every year when the MLB playoffs roll around, articles upon articles are publ
School: Stanford
Course: Mathematics Of Sports
Okereke 1 Michelle Okereke Mathematics of Sports Leonid Pekelis 11 December 2014 Extended Abstract: First Server Advantage in Tennis Unlike football, where each team starts one half kicking and the other half receiving, there is no guarantee in tennis tha
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Course: Mathematics Of Sports
Project Title: Overtime Rules in Soccer and their Effect on Winning Percentages Group Members: Elliot Chanen, Lenny Bronner, Daniel Ramos Introduction: We will examine the overtime rules of soccer to evaluate the randomness and fairness of different rules
School: Stanford
Course: Mathematics Of Sports
Sean Duggan and Thomas Stephens Professor Pekelis MCS 100/Stats 50 December 9th, 2014 Modeling El Clsico: Real Madrids Passing Trends and Corresponding Insights As the name suggests, soccer aficionados and casual fans alike hold El Clsico (translation: Th
School: Stanford
Course: Mathematics Of Sports
Rating Soccer Defenders Jason van der Merwe jasonvdm@stanford.edu Jack Craddock jwcrad@stanford.edu Bridge Eimon beimon@stanford.edu MCS100 Stanford University December 4, 2014 Abstract The project is motivated by a deciency in the status quo of soccer st
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Course: Mathematics Of Sports
The Rise in Ineld Hits Parker Phillips Harry Simon December 10, 2014 Abstract For the project, we looked at ineld hits in major league baseball. Our rst question was whether or not ineld hits have been on the rise since 2005. We found through analyzation
School: Stanford
Course: Mathematics Of Sports
MCS100/Stats50 Final Project - Extended Abstract Antonino Abundes & Alec Powell December 2, 2014 The Effect of Rookie Age and College Experience on Major League Baseball Career Performance (1985-2010) Introduction Does going to college have an impact on a
School: Stanford
Course: Principles And Models Of Semiconductor Devices
Esquire Magazine, December 1983, pp. 346-374. America is today in the midst of a great technological revolution. With the advent of the silicon chip, information processing, communications, and the national economy have been strikingly altered. The new te
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Course: Archaeology Of Food: Production, Consumption And Ritual
Sistem Reproduksi Wanita Kelompok 2 : Felia Nanda F / 17 / IX.6 Haditha Miftakhul S / 19 / IX.6 Ovilia Intan D / 27 / IX.6 Sheva Nuura A / 31 / IX.6 Sistem Reproduksi Perempuan Sistem reproduksi perempuan terdiri atas empat bagian : 1. Indung telur / ovar
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Course: Archaeology Of Food: Production, Consumption And Ritual
Pengertian Kebijakan moneter adalah kebijakan pemerintah menyangkut perilaku bank sentral dalam penawaran uang dan pengaturan uang yang beredar pada suatu negara. Kebijakan moneter pada dasarnya merupakan suatu kebijakan yang bertujuan untuk mencapai kese
School: Stanford
Course: Archaeology Of Food: Production, Consumption And Ritual
Persembahan Prestasi Internasional untuk NKRI Buah jatuh tidak akan jauh dari pohonnya. Pepatah itu barangkali bisa menggambarkan sosok dan prestasi yang diraih Syarif Hidayatullah Suhaimi. Sebab, nama besar ayahandanya yaitu Suhaimi Salam yang dikenal se
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
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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,
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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
School: Stanford
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: 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
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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
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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: 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
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Course: Analog Integrated Circuit Design
Lecture 6 Design Example 2 Extrinsic Capacitance Boris Murmann Stanford University murmann@stanford.edu Copyright 2004 by Boris Murmann B. Murmann EE 214 Lecture 6 (HO#9) 1 Overview Reading 1.6.7 (Parasitic Elements) 7.1, 7.2.0, 7.2.1 (Mille
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Course: Introduction To Time Series Analysis
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Course: Economic Analysis II
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Course: INTRODUCTION TO OPTIMIZATION
MS&E111 Introduction to Optimization Prof. Amin Saberi Lecture 8 May 1-3, 2006 1 Two player Zero-Sum games In this section, we consider games in which each of two opponents selects a strategy and receives a payoff contingent on both his own and his oppone
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Course: Analog Integrated Circuit Design
Lecture 24 kT/C Noise Boris Murmann Stanford University murmann@stanford.edu Copyright 2004 by Boris Murmann B. Murmann EE 214 Lecture 24 (HO#32) 1 Overview Introduction Having established the basic noise mechanisms in MOSFETS, today's lectur
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Course: Paradigms For Computing With Data
Understanding R Three Principles OBJECT Everything that exists is an object. FUNCTION Everything that happens is a function call. INTERFACE Use the best software, with an effective interface. Everything that happens is a function call 1. Programming in th
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Course: Paradigms For Computing With Data
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Course: Marketing To Businesses
Seenisoftenathreewaywhammyharmuponrecordkeepingstrategies.Insideoutdatedearth, thesefolkswerecentralizedandidiosyncratic(inthenopejorativesense).Particularssupervisors dealt with particulars in different ways and their own strategies have been determined
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Course: Marketing To Businesses
Another.Whenidon'tthinkiamalone,however,inseeingthattherearequiteanumberof classesfromasawhichiwouldnotbeexpectingtoseefromramiandviceversa.Furthermore, when i typically come across myself dealing with using just like minded coworkers the modificationsbet
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Course: Marketing To Businesses
Certificationswhichweremadeautomated.Nowthelanguageunraveled.Thereisahugefight (whichappearedtoleadnowhere)involvingautomatedpaperworksupervisionandparticulars supervision.Atthereallycorewiththisdiscussionhadbeentheoneness,apparentaboutthe demonstrate ter
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Course: Marketing To Businesses
Accuratewithinmyremembrancehowtheasanalongwiththe.H.to.Departmentontherami tookplaceinmanyweeksofeachother.Fromsufficienttime,icouldtakeintoaccountstaying educated,withoutconsternation,howtheramiwerenotregardingthelikesofme.Thedatabases supervision staff
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Course: Marketing To Businesses
Boxing,shelving,everything,insimple,relatedtotherealcopingwithassociatedwithrealparts, theirownspaceforstoring,maintenance,andcollection.Insiderecordearth,thefinancialdrivers regardingdeletionhadbeenthebuyingpriceofspaceforstoringplace.Intheonlineworld,th
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Course: Marketing To Businesses
Beseen.theideatranspiresonautopilotdeterminedbyprocessdesignjudgmentswhichheor shedoesnotneedtoalsoconsiderobserve.,thecompanyfeatureschoseinwhichanytimean actingprofessionaloccupyingthiskindofspecifiedsituation,bearsoutandaboutinwhichform of method, util
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Course: Marketing To Businesses
recordkeepingstrategies,alternatively,dealtwithparticulars(usuallycreatedinsideorperhaps offeredprivately)whichusuallyrecordedactivities,activities,orperhapsproblemsconducted throughtheorganization(orthatorganizationhadbeeninvolved).thisbecamereadilyavail
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Course: Marketing To Businesses
Managerhastocarryoutcanbesurethatalmostallsmallbusinessinformationaremigrated without the to get lost auditing particulars to decide what exactly need to and mustn't be producedalongside.ifspaceforstoringplaceisoftenadifficultytosuityourneeds,youmay,of co
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Course: Marketing To Businesses
Knowing(untilthestrategiesdeterminedbyinwhichtheoryhavebeeninvalidatedthroughthe idearevolution)wouldn'thavesustainedthismethod.Currently,systemspermitspeopletocreate andmaintainorganizationstrategiesinwhichincorporaterecordkeepingproductivityunitedarea o
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Course: Marketing To Businesses
Below).Recordkeepingproductivity(doingwhatexactlyyouneedtodosoastohaveevidence ofwhateveryouaredoing)isoftenapureorganizationthought.Veryeasyneedtohaveexpert recordkeepingspetstohelplevelthiskindofoutandaboutormaybetohelpundertakeit. Allofusspeciali
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Course: Marketing To Businesses
Separatingoutandaboutthejobsoffolksclientsandthoseassociatedwithsmallbusiness supervision.Responsivenesstohelpbuyerneedsbringsversatility,butresultsintheideatough to perform small business supervision. Small business governance technical specs prohibit ve
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Course: Marketing To Businesses
Presumedhowtheotherspecialeventreceivedtheideawhen.Theexactsamedifficultycomes up using email and surfaces also have to help reinvent (or reapply) the policies made for personastofaceallthefeaturesofautomateddistribution.Intheveryfirstmanyyearsassociated
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Course: Marketing To Businesses
Associatedwithrefocusing,associatedwithreengineeringandassociatedwith"our"technical specs,however,iambeggingtheproblem.EverythingIvementioneduptonowconfirmsin whichtherenolongerisouttherethehomogeneousandrecognizablypersonpositionforjusta particularsmanag
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Course: Marketing To Businesses
thegooftheexpertagencyforreasonsofsectionofheadlineprogramcode.subtitledother responsibilitiessec.responsibilitiesregardingefficiencysecurityandprivacyoffederal computersystems.astandardsandguidelines.authority.theassistantofbusinessshallonthe reasonsfors
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Course: Marketing To Businesses
thatexpertagencyorinthesituationofanexpertagencywithoutachieffinancialofficerany similarformalshallsetuprecommendationsandtechniquesthatcreatesurethatthe accountingeconomicalandassetmanagementsystemsandotherdetailssystemsoftheexpert agencyaredesigneddesig
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Course: Marketing To Businesses
ofheadlineprogramcodeandtheprioritiesrecognizedbythegooftheexpertagency developingmaintainingandassistingtheimplementationofasoundandincorporateddetails technologystructurefortheexpertagencyandpromotingtheefficientandeffectivestyleand operationofallimport
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Course: Marketing To Businesses
regardstocostabilityofthesystemtofulfillspecifiedspecificationstimelinessandtopquality. sec.performanceandresultsbasedmanagement.insatisfyingtheresponsibilitiesundersection hofheadlineprogramcodethegoofanexpertagencyshallsetupgoalsforhelpingthe performanc
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Course: Marketing To Businesses
technologyarchitecturedefined.initthephrasedetailstechnologystructurewithregardtoan expertagencyindicatesaframeworkforevolvingormaintainingcurrentdetailstechnologyand acquiringnewdetailstechnologytoachievetheagencysidealgoalsanddetailsresources management
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Course: Marketing To Businesses
technologytobeusedinassistanceofthosetasksanddcreatesurethatthedetailsprotection recommendationstechniquesandpracticesaresufficient.guidanceformultiagency investments.theroutereleasedunderpassageshallinvolveguidanceforundertakingwisely interagencyandgover
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Course: Marketing To Businesses
thegovtgovernmenttodosotostartamultiagencyagreementforpurchasingofcompanypieces ofdetailstechnologythatneedseachexpertagencyprotectedbytheagreementwhenacquiring suchitemseithertoprocurethosethingsunderthatagreementortorationalizeansubstitute purchasingint
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Course: Marketing To Businesses
gettingdetailstechnology.subtitlecexecutiveagenciessec.responsibilities.insatisfyingthe responsibilitiesallocatedunderchapterofheadlineprogramcodethegoofeachexpertagency shalladheretothissubtitleinaccordancewiththeparticularmattersprotectedbythissubtitle.
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Course: Marketing To Businesses
acquisitionsofinformationtechnology.thehomeshallorganizethegrowthandreviewbythe manageroftheworkplaceofinformationandregulatingmattersofpolicyassociatedwithgovt gettingdetailstechnologywiththeworkplaceofgovtprocurementplan.sec.performancebased andresultsb
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Course: Marketing To Businesses
title.thisdepartmentmaybecitedastheinformationtechnologicalinnovationmanagement changeactof.sec.definitions.inthisdepartmentdirector.thephrasehomeindicatesthe homeoftheworkplaceofmanagementandbudget.executiveagency.thephraseexpert agencyhasthesignificance
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Course: Marketing To Businesses
performanceadvantagesachievedduetoimportantcapitalinvestmentopportunitiescreatedby expertreports.communitydetails.usc.usc.verdatefebmarjktpofrmfmtsfmtpubl. applicationspubliclawfeb.stat.companiesindetailssystemsandhowtheadvantages correspondwiththesuccess
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Course: Marketing To Businesses
headlineprogramcode.commercialitem.thephrasecommercialproducthasthe significancegiventhatphraseinsectionoftheworkplaceofgovtprocurementplanactu.s. titleliresponsibilityforacquisitionsofinformationtechnologysubtitleacommonpowersec. repealofcentralauthority
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Course: Marketing To Businesses
proposedrevisionstothegovtbuymanagementandsuchothersuggestedrulesorrevisionsto currentrulesasmaybenecessarytoapplythisactshallbereleasedinthegovtsignupnotlater thantimesafterplentyofperiodoftimeoftheenactmentofthisact.bpubliccomment.the suggestedrulesdesc
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Course: Marketing To Businesses
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Course: Marketing To Businesses
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Course: Marketing To Businesses
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Course: Marketing To Businesses
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Course: Marketing To Businesses
asdescribedinsectionoftheworkplaceofgovtprocurementplanactu.s.areacis improvedabyplacingexception.aftercandbbystunningoutdrinkthefirstandthird placesthisindicatesinthesecondphraseandplacingratherthanthereofbeverage.areais improvedainsubsectionbibystunning
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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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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
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Course: DECISION ANALYSIS I
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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
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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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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
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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
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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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Course: PROB ANALYSIS
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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Course: Programming Paradigms
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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Course: DECISION ANALYSIS I
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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Course: Introduction To Time Series Analysis
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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Course: Machine Learning
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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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: 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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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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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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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
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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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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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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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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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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
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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
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: 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: 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: 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
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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
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: 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
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: 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: 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
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: 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
School: Stanford
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
School: Stanford
grad.) ZISL hwZ Sample. Sk/M foluk05 ' .- Text Problem 10.1 .r' A. I L.) Using (10.10), we dene Gm = argmin wÂ§")1(yi gÃ© G(zi)). Then the optimization problem for solving for ,6, is: N lm = argminp Z wgm)ewp[~inm($i)l it->- ' We take the derivative
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
School: Stanford
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: 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
School: Stanford
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
School: Stanford
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
School: Stanford
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
School: Stanford
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
School: Stanford
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
School: Stanford
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)
School: Stanford
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
School: Stanford
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
School: Stanford
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
School: Stanford
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
School: Stanford
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
School: Stanford
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
School: Stanford
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
School: Stanford
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
School: Stanford
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
School: Stanford
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
School: Stanford
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
School: Stanford
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
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: Data Mining And Analysis
Lab 26: Missing data In this lab, we will work with the NLSY79 dataset. This is a longitudinal study from the Bureau of Labor Statistics, which followed a cohort of a few thousand baby boomers from 1979 until 2010, recording hundreds of variables every th
School: Stanford
Course: Data Mining And Analysis
Lab 1: Illustration of the bias-variance decomposition library(ggplot2) library(splines) set.seed(1) Define a true function f. f = function(x) cfw_ x^2 - 0.2*x^2.3333 Now, we sample a random observation of the function at 10 input points with normal erro
School: Stanford
Course: Data Mining And Analysis
Lab 11: The wrong way and the right way to do cross-validation In this lab, we simulate the wrong way and the right way to perform cross validation, as explained in Lecture 11 and in Section 7.10 of the Elements of Statistical Learning. We will work under
School: Stanford
Course: Data Mining And Analysis
Lab18:Analysisofopinioneditorialsfrom twoStanfordstudentnewspapers Stanford has two large student newspapers. The Stanford Daily is the main campus tabloid, while the Stanford Review publishes conservative-leaning political articles on a biweekly basis. E
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
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
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
School: Stanford
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 -
School: Stanford
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: Solid State Physics II
TutorialonPC1D MohitMehta ProgramDescription PC1Dsolvesthefullycouplednonlinearequationsforthe quasi1dtransportofelectrons&holesincrystalline semiconductordevices,withemphasisonphotovoltaic devices. OnlyfilerequiredtoruntheprogramisPC1D.exe. PC1D.hlppro
School: Stanford
Course: Theory Of Probability
Stat 116: Practice Final December, 2014 The exam consists of 8 problems. As a resource you may use a calculator and two pages of notes. 1. Two points are selected randomly on a line segment of length L, so as to be on opposite sides of the mid-point of th
School: Stanford
Course: Theory Of Probability
Two More Problems Here are two more practice problems. Number 6 (essentially 4.13 in the text) is a substitute for Number 2 of the original practice exam, since that problem already appeared in a homework assignment. Number 5 is included for your amusemen
School: Stanford
CME 193 1 Introduction to Python Exercises Basics Exercise 1.1: The interpreter Open the Python interpeter. What happens when you input the following statements: (a) 3 + 1 (b) 3 * 3 (c) 2 * 3 (d) "Hello, world!" Exercise 1.2: Scripts Now copy the above to
School: Stanford
Course: Consulting Club Guides
The MIT Sloan School of Management Management Consulting Club Case Book and Interview Guide October 2001 The MIT Sloan School of Management Management Consulting Club Many thanks to the MCC Case Book and Interview Guide Sponsors: Gold Sponsor: Case Contri
School: Stanford
Course: Consulting Club Guides
The 2005 Consulting Club Case Book 14 December 2004 1 Table of Contents I. Introduction.2 II. The Consulting Interview Process.3 a. Introduction to the Process.3 b. What Firms are Testing for with Case and Fit Interviews. 5 c. The Interview Challenge10 II
School: Stanford
Course: Consulting Club Guides
CONSULTING CASE INTERVIEW PREPARATION GUIDE 2006- 2007 Recruiting Season September 16, 2006 CONSULTING CLUB Xxxxx-xx/Footer -0- Contents Editors note Introduction to cases Administering cases Receiving cases The cases Xxxxx-xx/Footer -1- Editors Note Dear
School: Stanford
Course: Archaeology Of Food: Production, Consumption And Ritual
PEKMA Perdana STAN 2014 Minggu lalu, tepatnya tanggal 14 dan 15 Maret 2015, Senat kita mengadakan acara yang bernama Pekan Mahasiswa. Singkatnya bisa disebut Pekma, dimana pekma kali ini bertemakan kebersamaan untuk hari ini dan masa depan. Tentunya, selu
School: Stanford
Course: Archaeology Of Food: Production, Consumption And Ritual
A.Pengertian 1.Taubat TaubatdalambahasaArabbermaknaarruju()yaitukembali.Maksudnyakembalidaridosadosa. DansecaraistilahdidalamkitabKifayahAtThalibArRabbanidanjugakitabLisanulArab,taubahitu didefinisikansebagai: Kembalidariberbagaiperbuatanyangter
School: Stanford
Course: Archaeology Of Food: Production, Consumption And Ritual
MARKETING AGENTproperty JOB DESCRIPTION Pekerjaan marketing property dan pekerjaan lain di perusahaan property yang tertata manajemennya memiliki job description. Berisi jenis pekerjaan rinci yang jadi tanggung jawab masing-masing staf. Tak boleh hanya me
School: Stanford
Course: Archaeology Of Food: Production, Consumption And Ritual
Keberatan SKP Wajib pajak bisa melakukan keberatan atau banding terhadap SKP yang diterbitkan oleh DJP dengan persyaratan yang telah ditentukan pada UU KUP Pasal 25 26. Syaratnya antara lain : 1. Mengajukan surat keberatan kepada Direktur Jenderal Pajak a
School: Stanford
Course: Archaeology Of Food: Production, Consumption And Ritual
Nama Kelompok : 1. Firda Risnayu (13) 2. Hadi Prasetiyanto (14) 3. Hendi Kurniawan (16) 4. I Wy Gd Prayuda Abisena(19) 5. Ni Kd Diah Puspita Dewi (24) Perusahaan Gethuk gedhang, dengan satu pusat dan 3 cabang, 2 cabang digunakan tempat penjualan. Kemudian
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