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School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Homework 2 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, Y
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 13 Reminder: Midterm on March 4 in class Closed book, closed notes. Bring your calculator and a double-sided letter-sized cheat-sheet. Formulas of common distributions will be provided. If for any r
School: Duke
Course: Modern Statistical Modeling
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 110 Homework 1 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, Y
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): KEY Statistics 110 Homework 3 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, YOU MUST ATTACH SEPARATE PAGES, NEATLY WRITTEN, THAT SHOW YOU
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): KEY HONOR PLEDGE (Please Sign): Statistics 110 Homework 4 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, YOU MUST ATTACH SEPARATE PAGES, N
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): HONOR PLEDGE (Please Sign): KEY Statistics 110 Homework 2 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, YOU MUST ATTACH SEPARATE PAGES, N
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Homework 2 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, Y
School: Duke
Course: Probability & Measure Theory
School: Duke
Course: Probability & Measure Theory
School: Duke
Course: Probability & Measure Theory
N R Z R
School: Duke
Course: Probability & Measure Theory
School: Duke
Is The Relationship Really Linear? How About Residuals? Inuential Observations Example STA113 Regression Diagnostics Artin Armagan Department of Statistical Science November 18, 2009 Armagan Is The Relationship Really Linear? How About Residuals? Inuentia
School: Duke
Simple Linear Regression Analysis Multiple Linear Regression STA113: Probability and Statistics in Engineering Linear Regression Analysis - Chapters 12 and 13 in Devore Artin Armagan Department of Statistical Science November 18, 2009 Armagan Simple Linea
School: Duke
Course: Special Topics
%!PSAdobe2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: cluster_lecture.dvi %Pages: 22 %PageOrder: Ascend %Orientation: Landscape %BoundingBox: 0 0 612 792 %EndComments %DVIPSWebPage: (www.radicaleye.com) %DVIPSCommandLine: d
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): KEY HONOR PLEDGE (Please Sign): Statistics 110 Midterm 1 This is a closed-notebook, closed laptop exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at your own exam. S
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): KEY HONOR PLEDGE (Please Sign): Statistics 110 Midterm 2 This is a closed-notebook, closed laptop exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at your own exam. S
School: Duke
Course: Statistics
STA 114/MTH 136 Intro to Mathematical Statistics Lecture 12 1 / 28 Central limit theorem allows to approximate the sampling distribution of an estimator if it can be written as the sum of i.i.d. random variables. This scenario occurs very often. For examp
School: Duke
Course: Statistics
STA 114/MTH 136 Intro to Mathetical Statistics Lecture 3 1 / 43 Last class Bayes Theorem and the ideal approach to inference. Example: A political poll (a binomial experiment). 2 / 43 Political poll example revisited An organization randomly selected 100
School: Duke
Course: Statistics
STA 114: Statistics Notes 17. Hypotheses testing Hypotheses about model parameters A new soporic drug is tried on n = 10 patients with sleep disorder, and the average increase in sleep hours is found to be 2.33 hours (with standard deviation 2 hours). Is
School: Duke
Course: Modern Statistical Modeling
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 110 Homework 1 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, Y
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): KEY Statistics 110 Homework 3 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, YOU MUST ATTACH SEPARATE PAGES, NEATLY WRITTEN, THAT SHOW YOU
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): KEY HONOR PLEDGE (Please Sign): Statistics 110 Homework 4 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, YOU MUST ATTACH SEPARATE PAGES, N
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): HONOR PLEDGE (Please Sign): KEY Statistics 110 Homework 2 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, YOU MUST ATTACH SEPARATE PAGES, N
School: Duke
Course: Probability
Homework 3 MATH230 Due: October 1, 2012 1 Let Xi = the number on the ith roll, i = 1, 2, ., 10 10 E 10 Xi = i=1 E (Xi ) i=1 10 (1 + 2 + 3 + 4 + 5 + 6)/6 = i=1 = 10 3.5 = 35 2 This problem can also be solved using the tail probability formula for expectati
School: Duke
Course: Probability
Homework 4 MATH230 Due: October 12, 2012 Problem 1 a Xi [0, 1, 4, 9, 6, 5, 6, 9, 4, 1] E (Xn ) = E (Xi ) = (0 + 1 + 4 + 9 + 6 + 5 + 6 + 9 + 4 + 1)/10 = 4.5 b According to the Central Limit Theorem, as n approaches innite, Xn should be approximately normal
School: Duke
Lab Assignment #6 STA 113 November 18, 2009 This is a real data set which was used in Leo Breiman and Jerome H. Friedman (1985), Estimating optimal transformations for multiple regression and correlation, JASA, 80, pp. 580-598. The problem is to predict t
School: Duke
Condence and credible intervals Artin Armagan Lab assignment #5 October 20, 2009 Artin Armagan Condence and credible intervals Lab outline The lab will be due on October 30th. You will be expected to write a short: 1-2 page lab report and also turn in cod
School: Duke
Inference: MLE and Bayesian Sayan Mukherjee Lab assignment Four October 1, 2009 Sayan Mukherjee Inference: MLE and Bayesian Lab outline The lab will be due on the 9th of October. You will be expected to write a short: 1-2 page lab report. The 1-2 pages do
School: Duke
Inference: MLE Sayan Mukherjee Lab assignment Three September 24, 2009 Sayan Mukherjee Inference: MLE Lab outline The lab will be due on the 2th of October. You will be expected to write a short: 1-2 page lab report. The 1-2 pages do not include plots or
School: Duke
Random variables: sampling and plotting Sayan Mukherjee and Artin Armagan Lab assignment Two September 10, 2009 Sayan Mukherjee and Artin Armagan Random variables: sampling and plotting Lab outline The lab will be due on the 18th of September. You will be
School: Duke
STA113 - Probability and Statistics in Engineering, Fall09 Instructors: Sayan Mukherjee and Artin Armagan e-mail: sayan@stat.duke.edu, artin@stat.duke.edu Oce: Old Chem 112, Old Chem 217 Oce hours: TBA Textbook: Jay L. Devore, Probability and Statistics f
School: Duke
Course: Intro Biostatistics
Syllabus STA102, Stangl, Spring 2005 Time and Location: Class M/W 1:15, Social Sciences 136 Instructor: Dalene Stangl Office: 212 Old Chemistry, dalene@stat.duke.edu, 684-4263 Texts: Fundamentals of Biostatistics (latest edition) by Rosner JMP IN S
School: Duke
Course: Data Analy Stat Infer
Data Analysis and Statistical Inference STA 101 Sec 2 Instructor: SYLLABUS AND COURSE POLICIES Fall, 2004 Teaching Assistants: TA Location: Course Web Page: Lectures: Computer Labs: Text: Woncheol Jang Old Chem 223B wjang@stat.duke.edu (919) 684-
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Homework 2 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, Y
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 13 Reminder: Midterm on March 4 in class Closed book, closed notes. Bring your calculator and a double-sided letter-sized cheat-sheet. Formulas of common distributions will be provided. If for any r
School: Duke
Course: Modern Statistical Modeling
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 110 Homework 1 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, Y
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): KEY Statistics 110 Homework 3 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, YOU MUST ATTACH SEPARATE PAGES, NEATLY WRITTEN, THAT SHOW YOU
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): KEY HONOR PLEDGE (Please Sign): Statistics 110 Homework 4 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, YOU MUST ATTACH SEPARATE PAGES, N
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): HONOR PLEDGE (Please Sign): KEY Statistics 110 Homework 2 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, YOU MUST ATTACH SEPARATE PAGES, N
School: Duke
Course: Probability
Homework 3 MATH230 Due: October 1, 2012 1 Let Xi = the number on the ith roll, i = 1, 2, ., 10 10 E 10 Xi = i=1 E (Xi ) i=1 10 (1 + 2 + 3 + 4 + 5 + 6)/6 = i=1 = 10 3.5 = 35 2 This problem can also be solved using the tail probability formula for expectati
School: Duke
Course: Probability
Homework 4 MATH230 Due: October 12, 2012 Problem 1 a Xi [0, 1, 4, 9, 6, 5, 6, 9, 4, 1] E (Xn ) = E (Xi ) = (0 + 1 + 4 + 9 + 6 + 5 + 6 + 9 + 4 + 1)/10 = 4.5 b According to the Central Limit Theorem, as n approaches innite, Xn should be approximately normal
School: Duke
Course: Probability
Homework 2 MATH230 Due: September 14, 2012 Problem 1 Let B1 = rst ball, B2 = second ball P (B2 = white) = P (B1 = white, B2 = white) + P (B1 = black, B2 = white) = P (B2 = white|B1 = white)P (B1 = white) + P (B2 = white|B1 = black)P (B1 = black) = 7 13 4
School: Duke
Course: Probability
Homework 1 MATH230 Due: September 10, 2012 Part A P (rst ticket drawn is 1 and the second ticket drawn is 2) = P (rst ticket drawn is 1)P (second ticket drawn is 2) = 1 n = 1 n 1 n2 Part B Many students interpreted this question to mean that the second dr
School: Duke
Random variables: sampling and plotting Sayan Mukherjee and Artin Armagan Lab assignment Two September 10, 2009 Sayan Mukherjee and Artin Armagan Random variables: sampling and plotting Lab outline The lab will be due on the 18th of September. You will be
School: Duke
Inference: MLE Sayan Mukherjee Lab assignment Three September 24, 2009 Sayan Mukherjee Inference: MLE Lab outline The lab will be due on the 2th of October. You will be expected to write a short: 1-2 page lab report. The 1-2 pages do not include plots or
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 7 1 / 50 More general criteria for selecting good estimators Recall from last time the goal of constructing an estimator (X) so that (X) will likely to be close to . Again, we need a notion of dista
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 10 The sample variance Last time we see that for i.i.d. data from a normal distribution with unknown mean and variance, the estimator 2 = 1 n n (Xi X )2 i=1 is the MLE. We see that this is biased, a
School: Duke
Course: Statistics
STA 114: Statistics Notes 1. Statistical Inference in Real World Examples 1. Medicine. Immediate care to victims with stab injuries[1] . Description. To study[2] whether early injection of IV uids could be harmful to patients with penetrating injuries to
School: Duke
STA 214: Probability & Statistical Models Fall Semester 2004 Mike West January 19, 2006 Orientation MATERIAL OMITTED: THIS VERSION DOES NOT HAVE EXERCISES AND SOLUTIONS 1 1 1.1 AR(1) Models Introduction Time series: Stochastic process in (di
School: Duke
Is The Relationship Really Linear? How About Residuals? Inuential Observations Example STA113 Regression Diagnostics Artin Armagan Department of Statistical Science November 18, 2009 Armagan Is The Relationship Really Linear? How About Residuals? Inuentia
School: Duke
Simple Linear Regression Analysis Multiple Linear Regression STA113: Probability and Statistics in Engineering Linear Regression Analysis - Chapters 12 and 13 in Devore Artin Armagan Department of Statistical Science November 18, 2009 Armagan Simple Linea
School: Duke
Course: Special Topics
%!PSAdobe2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: cluster_lecture.dvi %Pages: 22 %PageOrder: Ascend %Orientation: Landscape %BoundingBox: 0 0 612 792 %EndComments %DVIPSWebPage: (www.radicaleye.com) %DVIPSCommandLine: d
School: Duke
Lab Assignment #6 STA 113 November 18, 2009 This is a real data set which was used in Leo Breiman and Jerome H. Friedman (1985), Estimating optimal transformations for multiple regression and correlation, JASA, 80, pp. 580-598. The problem is to predict t
School: Duke
Condence and credible intervals Artin Armagan Lab assignment #5 October 20, 2009 Artin Armagan Condence and credible intervals Lab outline The lab will be due on October 30th. You will be expected to write a short: 1-2 page lab report and also turn in cod
School: Duke
Inference: MLE and Bayesian Sayan Mukherjee Lab assignment Four October 1, 2009 Sayan Mukherjee Inference: MLE and Bayesian Lab outline The lab will be due on the 9th of October. You will be expected to write a short: 1-2 page lab report. The 1-2 pages do
School: Duke
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School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): KEY HONOR PLEDGE (Please Sign): Statistics 110 Midterm 2 This is a closed-notebook, closed laptop exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at your own exam. S
School: Duke
Course: Statistics
STA 114/MTH 136 Intro to Mathematical Statistics Lecture 12 1 / 28 Central limit theorem allows to approximate the sampling distribution of an estimator if it can be written as the sum of i.i.d. random variables. This scenario occurs very often. For examp
School: Duke
Course: Statistics
STA 114/MTH 136 Intro to Mathetical Statistics Lecture 3 1 / 43 Last class Bayes Theorem and the ideal approach to inference. Example: A political poll (a binomial experiment). 2 / 43 Political poll example revisited An organization randomly selected 100
School: Duke
Course: Statistics
STA 114: Statistics Notes 17. Hypotheses testing Hypotheses about model parameters A new soporic drug is tried on n = 10 patients with sleep disorder, and the average increase in sleep hours is found to be 2.33 hours (with standard deviation 2 hours). Is
School: Duke
7.1 a. z/2 = 2.81 suggests that /2 = 1 (2.81) = 0.0025. Thus = 0.005 and the corresponding condence level is 99.5%. b. z/2 = 1.44 suggests that /2 = 1 (2.81) = 0.075. Thus = 0.15 and the corresponding condence level is 85%. c. 99.7% implies an value of 0.
School: Duke
School: Duke
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): KEY HONOR PLEDGE (Please Sign): Statistics 110 Midterm 1 This is a closed-notebook, closed laptop exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at your own exam. S
School: Duke
Course: Probabil Statis In Egr
Aug 30, Sep 1 : Probability Reading: chap 2 HW: 26, 30, 33, 48, 59, 61, 69, 78, 80. 26. A certain system can experience three different types of defects. Let Ai (i=1, 2, 3) denote the event that the system has a defect of type i. Suppose that P(A1
School: Duke
STAT113 HW3 For Quiz 09/18/09 1. (Chapter 4: 100 ) Let X denote the time to failure (in years) of a certain hydraulic component. Suppose the pdf of X is f (x) = 32/(x + 4)3 for x > 0. (a) (b) (c) (d) (e) Verify that f (x) is a legitimate pdf. Determine th
School: Duke
STAT113 HW3 Solutions 1. (Chapter 4: 100 ) Solution: (a) Two conditions are required for f (x) to be a legitimate pdf: i. f (x) > 0, x > 0. 0 1 ii. f (x)dx = 32/(x + 4)3 dx = 32 =1 2(x + 4)2 0 0 (b) Let F (x) denote the cdf of X. x x 09/18/09 F (x) = 0 f
School: Duke
Course: Probabil Measure Theory
STA 205: Probability & Measure Theory Homework #1 solutions February 8, 2005 1. Suppose = {0,1} and C = {0}.Enumerate ,the class of all -fields containing C Sol: Note that C is a collection of subsets of ,and (C) = {0}, {1}, {0, 1}, } But also noti
School: Duke
Course: Linear Models
%!PS-Adobe-3.0 %Title: Microsoft Word - HW1.doc %Creator: PScript5.dll Version 5.2 %CreationDate: 1/11/2006 17:53:25 %For: Administrator %BoundingBox: (atend) %Pages: (atend) %Orientation: Portrait %PageOrder: Special %DocumentNeededResources: (atend
School: Duke
Course: Probabil Statis In Egr
Reading: chap 6 & 7 6.22 Let X denote the proportion of allotted time that a randomly selected student spends working on a certain aptitude test. Suppose the pdf of X is f (x; ) = ( + 1)x 0 x 1 0 otherwise where 1 < . A random sample of ten st
School: Duke
Course: Probabil Statis In Egr
Reading: chap 4.14.3, pp177-179, 4.6 HW: 5, 33, 42, 44, 51 (use normal approximation), 117 4.5 A college professor never finishes his lecture before the end of the hour and always finishes his lectures within 2 min after the hour. Let X=the time th
School: Duke
Course: Probabil Statis In Egr
Aug 30, Sep 1 : Probability Reading: chap 2 HW: 26, 30, 33, 48, 59, 61, 69, 78, 80. 26. A certain system can experience three different types of defects. Let Ai (i=1, 2, 3) denote the event that the system has a defect of type i. Suppose that P(A1
School: Duke
Course: Appl Data Analy Env Sci
STA 240 Homework 6 1. MARIJUANA AND TESTOSTERONE 1. Equality of means among controls, mild users, and heavy users . H0: control= mild= heavy HA: not all j are equal [20(742 - 579) 2 + 11( 503 - 579 ) + 9 ( 309 - 579 )
School: Duke
Course: Statistical Inference
STAT215: Homework 1 Due: Wednesday, Feb 1 Each problem is worth ten points, for a total of 100 points. Bickel & Doksum problems from pages 66-71: 1.1.3, 1.1.15 Bickel & Doksum problems from pages 71-74: 1.2.3, 1.2.8, 1.2.15 Bickel & Doksum proble
School: Duke
Course: Probabil Statis In Egr
STA 113 HW 6 (Provided by Andrew Dreher) October 31, 2004 6.9 Each of 150 newly manufactured items is examined and the number of scratches per item is recorded (the items are supposed to be free of scratches), yielding the following data: Number of
School: Duke
Using Normal Distribution Using Exponential Distribution Examples 1 Artin Armagan Sta. 113 February 3, 2009 Artin Armagan Examples 1 Using Normal Distribution Using Exponential Distribution Table of contents 1 Using Normal Distribution 2 Using Exponential
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 12 Central limit theorem allows us to directly approximate the sampling distribution of an estimator if it can be written as the sum of i.i.d. random variables. This scenario occurs very often. For
School: Duke
STA113 - Probability and Statistics in Engineering, Fall09 Instructors: Sayan Mukherjee and Artin Armagan e-mail: sayan@stat.duke.edu, artin@stat.duke.edu Oce: Old Chem 112, Old Chem 217 Oce hours: TBA Textbook: Jay L. Devore, Probability and Statistics f
School: Duke
Course: Intro Biostatistics
Connecting Mean, Median and Mode. Mean, Median and Mode We start with a set of 21 numbers, Lecture 2 - Introduction to Probability # [1] -2.2 -1.6 -1.0 -0.5 -0.4 -0.3 -0.2 # [12] 0.4 0.5 0.6 0.7 0.7 0.9 1.2 0.1 1.2 0.1 1.7 0.2 1.8 0.4 Statistics 102 mean(
School: Duke
Course: Intro Biostatistics
Announcements Announcements HW1 and Lab 1 have been graded and your scores are posted in Gradebook on Sakai (it is good practice to always double check your scores). Lecture 5 - Continuous Distributions You should have picked up Lab 1 last week, HW1 will
School: Duke
Course: Intro Biostatistics
Evaluating nearly normalness Normal probability plot Lecture 6 - Assessing Normality, Normal Approximation to Binomial A histogram and normal probability plot of a sample of 100 male heights. q q male heights (in.) qq q q Sta102/BME102 Colin Rundel Februa
School: Duke
Course: Intro Biostatistics
Random Variables Random variables Lecture 4 - Random Variables and Discrete Distributions A random variable is a numeric quantity whose value depends on the outcome of a random event We use a capital letter, like X , to denote a random variables The value
School: Duke
Course: Intro Biostatistics
Lecture 8 - Hypothesis Testing Sta102/BME102 Colin Rundel February 12, 2014 Hypothesis testing Hypothesis testing framework Hypothesis testing framework We start with a null hypothesis (H0 ) that represents the status quo. We develop an alternative hypoth
School: Duke
Course: Intro Biostatistics
Bootstrapping and Randomization Testing Example - Rent in Durham 20 apartments here in Durham were randomly sampled and their rents obtained. The dot plot below shows the distribution of the rents of these apartments. Can we apply the methods we have lear
School: Duke
Course: Intro Biostatistics
Dierence of two means Condence intervals for dierences of means Example - GSS The General Social Survey (GSS) conducted by the Census Bureau contains a standard core of demographic, behavioral, and attitudinal questions, plus topics of special interest. M
School: Duke
Course: Intro Biostatistics
Announcements Announcements Homework 1 - Out 1/15, due 1/22 Lecture 1 - Data and Data Summaries Lab 1 - Tomorrow Statistics 102 RStudio accounts created this evening Try logging in at http:/ beta.rstudio.org Colin Rundel January 13, 2013 Practice Quiz - I
School: Duke
Course: Intro Biostatistics
Review Arbuthnot Lab 1 - Extra Credit Lecture 3 - More Conditional Probability Statistics 102 Colin Rundel January 22, 2013 Statistics 102 (Colin Rundel) Review Lecture 3 - More Conditional Probability Probability Review Basic Probability Review January 2
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Homework 2 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, Y
School: Duke
Course: Probability & Measure Theory
School: Duke
Course: Probability & Measure Theory
School: Duke
Course: Probability & Measure Theory
N R Z R
School: Duke
Course: Probability & Measure Theory
School: Duke
Course: Probability & Measure Theory
School: Duke
Course: Probability & Measure Theory
R R R
School: Duke
Course: Probability & Measure Theory
N N
School: Duke
Course: Probability & Measure Theory
School: Duke
Course: Probability & Measure Theory
P P P
School: Duke
Course: Intro Biostatistics
Connecting Mean, Median and Mode. Mean, Median and Mode We start with a set of 21 numbers, Lecture 2 - Introduction to Probability # [1] -2.2 -1.6 -1.0 -0.5 -0.4 -0.3 -0.2 # [12] 0.4 0.5 0.6 0.7 0.7 0.9 1.2 0.1 1.2 0.1 1.7 0.2 1.8 0.4 Statistics 102 mean(
School: Duke
Course: Intro Biostatistics
Announcements Announcements HW1 and Lab 1 have been graded and your scores are posted in Gradebook on Sakai (it is good practice to always double check your scores). Lecture 5 - Continuous Distributions You should have picked up Lab 1 last week, HW1 will
School: Duke
Course: Intro Biostatistics
Evaluating nearly normalness Normal probability plot Lecture 6 - Assessing Normality, Normal Approximation to Binomial A histogram and normal probability plot of a sample of 100 male heights. q q male heights (in.) qq q q Sta102/BME102 Colin Rundel Februa
School: Duke
Course: Intro Biostatistics
Random Variables Random variables Lecture 4 - Random Variables and Discrete Distributions A random variable is a numeric quantity whose value depends on the outcome of a random event We use a capital letter, like X , to denote a random variables The value
School: Duke
Course: Intro Biostatistics
Lecture 8 - Hypothesis Testing Sta102/BME102 Colin Rundel February 12, 2014 Hypothesis testing Hypothesis testing framework Hypothesis testing framework We start with a null hypothesis (H0 ) that represents the status quo. We develop an alternative hypoth
School: Duke
Course: Intro Biostatistics
Bootstrapping and Randomization Testing Example - Rent in Durham 20 apartments here in Durham were randomly sampled and their rents obtained. The dot plot below shows the distribution of the rents of these apartments. Can we apply the methods we have lear
School: Duke
Course: Intro Biostatistics
Dierence of two means Condence intervals for dierences of means Example - GSS The General Social Survey (GSS) conducted by the Census Bureau contains a standard core of demographic, behavioral, and attitudinal questions, plus topics of special interest. M
School: Duke
Course: Intro Biostatistics
Announcements Announcements Homework 1 - Out 1/15, due 1/22 Lecture 1 - Data and Data Summaries Lab 1 - Tomorrow Statistics 102 RStudio accounts created this evening Try logging in at http:/ beta.rstudio.org Colin Rundel January 13, 2013 Practice Quiz - I
School: Duke
Course: Intro Biostatistics
Review Arbuthnot Lab 1 - Extra Credit Lecture 3 - More Conditional Probability Statistics 102 Colin Rundel January 22, 2013 Statistics 102 (Colin Rundel) Review Lecture 3 - More Conditional Probability Probability Review Basic Probability Review January 2
School: Duke
Course: Intro Biostatistics
Normal Appoximation to the Binomial Example - Airline booking An airline knows that over the long run, 90% of passengers who reserve seats show up for ight. On a particular ight with 300 seats, the airline accepts 324 reservations. Lecture 7 - Sampling Di
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Homework 4 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, Y
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Midterm 1 This is a closed book exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at your ow
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Midterm 1 This is a closed book exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at your ow
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): FIRST NAME (Please Print): KEY HONOR PLEDGE (Please Sign): Statistics 111 Homework 3 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, Y
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Homework 5 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, Y
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 110 Homework 1 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, Y
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Midterm 1 This is a closed book exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at you
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Midterm 1 This is a closed book exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at you
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Midterm 2 This is a closed book exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at you
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Midterm 3 This is a closed book exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at your ow
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Midterm 3 This is a closed book exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at your ow
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Midterm 2 This is a closed book exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at your ow
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Midterm 3 This is a closed book exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at you
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Midterm 2 This is a closed book exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at your ow
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Midterm 3 This is a closed book exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at you
School: Duke
Course: Probability And Statistical Inference
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 111 Midterm 2 This is a closed book exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at you
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 1 What is statistics and why study it? What is statistics and why study it? Statistics is the prime information science. What is statistics and why study it? Statistics is the prime information scie
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 4 First in-class close-book, close-notes quiz next Tuesday. You may bring a scientic calculator. Policies on quizzes and the midterm. Policies on homeworks and labs. Last class The three-step proced
School: Duke
Course: Intro To Mathematical Statistics
STA 114/MTH 136 Intro to Mathematical Statistics Lecture 3 Last class Bayes Theorem and the ideal approach to inference. Example: A political poll (a binomial experiment). Political poll example revisited An organization randomly selected 100 democrats an
School: Duke
Course: Intro To Mathematical Statistics
STA 114/MTH 136 Intro to Mathematical Statistics Lecture 6 1 / 28 The sampling (or frequentist) view point Both Bayesians and frequentists agree on the need to build models for the distribution of the data given certain parametersf (x| ) or p(x| ). Freque
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 2 1 / 35 Probability modeling and statistical inference Probability models are assumptions (or hypotheses) that characterize the randomness that arises in the data Statistical inference goes the oth
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 5 Point estimation A very common statistical problem is to guess the value of a parameter based on observed data X = (X1 , X2 , . . . , Xn ). Functions of the data that are used for guessing the val
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 8 Reminder Quiz next Tuesday (2/11). Summary of what we have learned so far The general inference procedure based on Bayes Theorem. How to construct point estimates/estimators based on the posterior
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 9 Two interpretations of the likelihood function From Bayes Theorem ( |x) ( )f (x| ) we know that if our prior ( ) is at, then ( |x) f (x| ) = L( ). Under this interpretation, the MLE, , is the po
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 11 If we have n i.i.d. N (0, 1) random variables U1 , U2 , . . . , Un , then the probability distribution of their sum 2 2 2 Z = U1 + U2 + + Un is called the 2 (or Chi-square) distribution with n de
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 12 Central limit theorem allows us to directly approximate the sampling distribution of an estimator if it can be written as the sum of i.i.d. random variables. This scenario occurs very often. For
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 13 Reminder: Midterm on March 4 in class Closed book, closed notes. Bring your calculator and a double-sided letter-sized cheat-sheet. Formulas of common distributions will be provided. If for any r
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 7 1 / 50 More general criteria for selecting good estimators Recall from last time the goal of constructing an estimator (X) so that (X) will likely to be close to . Again, we need a notion of dista
School: Duke
Course: Intro To Mathematical Statistics
STA 250/MTH 342 Intro to Mathematical Statistics Lecture 10 The sample variance Last time we see that for i.i.d. data from a normal distribution with unknown mean and variance, the estimator 2 = 1 n n (Xi X )2 i=1 is the MLE. We see that this is biased, a
School: Duke
Course: Statistics
STA 114: Statistics Notes 1. Statistical Inference in Real World Examples 1. Medicine. Immediate care to victims with stab injuries[1] . Description. To study[2] whether early injection of IV uids could be harmful to patients with penetrating injuries to
School: Duke
STA 214: Probability & Statistical Models Fall Semester 2004 Mike West January 19, 2006 Orientation MATERIAL OMITTED: THIS VERSION DOES NOT HAVE EXERCISES AND SOLUTIONS 1 1 1.1 AR(1) Models Introduction Time series: Stochastic process in (di
School: Duke
Is The Relationship Really Linear? How About Residuals? Inuential Observations Example STA113 Regression Diagnostics Artin Armagan Department of Statistical Science November 18, 2009 Armagan Is The Relationship Really Linear? How About Residuals? Inuentia
School: Duke
Simple Linear Regression Analysis Multiple Linear Regression STA113: Probability and Statistics in Engineering Linear Regression Analysis - Chapters 12 and 13 in Devore Artin Armagan Department of Statistical Science November 18, 2009 Armagan Simple Linea
School: Duke
Course: Special Topics
%!PSAdobe2.0 %Creator: dvips(k) 5.86 Copyright 1999 Radical Eye Software %Title: cluster_lecture.dvi %Pages: 22 %PageOrder: Ascend %Orientation: Landscape %BoundingBox: 0 0 612 792 %EndComments %DVIPSWebPage: (www.radicaleye.com) %DVIPSCommandLine: d
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): KEY HONOR PLEDGE (Please Sign): Statistics 110 Midterm 1 This is a closed-notebook, closed laptop exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at your own exam. S
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): KEY HONOR PLEDGE (Please Sign): Statistics 110 Midterm 2 This is a closed-notebook, closed laptop exam. You may use your calculator and a single page of notes. The room is crowded. Please be careful to look only at your own exam. S
School: Duke
Course: Statistics
STA 114/MTH 136 Intro to Mathematical Statistics Lecture 12 1 / 28 Central limit theorem allows to approximate the sampling distribution of an estimator if it can be written as the sum of i.i.d. random variables. This scenario occurs very often. For examp
School: Duke
Course: Statistics
STA 114/MTH 136 Intro to Mathetical Statistics Lecture 3 1 / 43 Last class Bayes Theorem and the ideal approach to inference. Example: A political poll (a binomial experiment). 2 / 43 Political poll example revisited An organization randomly selected 100
School: Duke
Course: Statistics
STA 114: Statistics Notes 17. Hypotheses testing Hypotheses about model parameters A new soporic drug is tried on n = 10 patients with sleep disorder, and the average increase in sleep hours is found to be 2.33 hours (with standard deviation 2 hours). Is
School: Duke
7.1 a. z/2 = 2.81 suggests that /2 = 1 (2.81) = 0.0025. Thus = 0.005 and the corresponding condence level is 99.5%. b. z/2 = 1.44 suggests that /2 = 1 (2.81) = 0.075. Thus = 0.15 and the corresponding condence level is 85%. c. 99.7% implies an value of 0.
School: Duke
School: Duke
School: Duke
Using Normal Distribution Using Exponential Distribution Examples 1 Artin Armagan Sta. 113 February 3, 2009 Artin Armagan Examples 1 Using Normal Distribution Using Exponential Distribution Table of contents 1 Using Normal Distribution 2 Using Exponential
School: Duke
Midterm Examination # 1 Sta 113: Probability and Statistics in Engineering Tuesday, 2008 Sep. 23, 1:15 2:30 pm This is a closed-book exam so do not refer to your notes, the text, or any other books (please put them on the oor). You may use a single sheet
School: Duke
Course: Probabil Statis In Egr
Final Examination Sta 113: Probability and Statistics in Engineering Thursday, 2004 Dec. 9, 7:00 10:00 pm This is a closed-book exam so do not refer to your notes, the text, or any other books (please put them on the floor). You may use a single sh
School: Duke
QUIZ 8 - SOLUTION A horticultural experiment was designed to test the conjecture that the treatment of the soil about peach tree seedlings with an anti-nematode chemical would speed the growth of seedlings by preventing nematode damage. Of the trees
School: Duke
Course: Intro Biostatistics
One potential risk factor for stroke is the F12 level, a hemostatic factor in blood. In a previous study, in a group of 63 stroke patients treated with placebo, the mean F12 level was 1.57 with a standard deviation of b) (2 points) What is the diffe
School: Duke
Course: Probability Stat Infer
Statistics 103: Practice Problems for Final Exam This sheet contains practice problems for the final exam. It is longer than the actual final so that you have extra problems. Other material in the text and from lectures may appear on the final exam
School: Duke
Course: Modern Statistical Modeling
LAST NAME (Please Print): KEY FIRST NAME (Please Print): HONOR PLEDGE (Please Sign): Statistics 110 Homework 1 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, Y
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): KEY Statistics 110 Homework 3 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, YOU MUST ATTACH SEPARATE PAGES, NEATLY WRITTEN, THAT SHOW YOU
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): KEY HONOR PLEDGE (Please Sign): Statistics 110 Homework 4 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, YOU MUST ATTACH SEPARATE PAGES, N
School: Duke
Course: Modern Statistical Modeling
NAME (Please Print): HONOR PLEDGE (Please Sign): KEY Statistics 110 Homework 2 You are allowed to discuss problems with other students, but the nal answers must be your own work. For all problems that require calculation, YOU MUST ATTACH SEPARATE PAGES, N
School: Duke
Course: Probability
Homework 3 MATH230 Due: October 1, 2012 1 Let Xi = the number on the ith roll, i = 1, 2, ., 10 10 E 10 Xi = i=1 E (Xi ) i=1 10 (1 + 2 + 3 + 4 + 5 + 6)/6 = i=1 = 10 3.5 = 35 2 This problem can also be solved using the tail probability formula for expectati
School: Duke
Course: Probability
Homework 4 MATH230 Due: October 12, 2012 Problem 1 a Xi [0, 1, 4, 9, 6, 5, 6, 9, 4, 1] E (Xn ) = E (Xi ) = (0 + 1 + 4 + 9 + 6 + 5 + 6 + 9 + 4 + 1)/10 = 4.5 b According to the Central Limit Theorem, as n approaches innite, Xn should be approximately normal
School: Duke
Course: Probability
Homework 2 MATH230 Due: September 14, 2012 Problem 1 Let B1 = rst ball, B2 = second ball P (B2 = white) = P (B1 = white, B2 = white) + P (B1 = black, B2 = white) = P (B2 = white|B1 = white)P (B1 = white) + P (B2 = white|B1 = black)P (B1 = black) = 7 13 4
School: Duke
Course: Probability
Homework 1 MATH230 Due: September 10, 2012 Part A P (rst ticket drawn is 1 and the second ticket drawn is 2) = P (rst ticket drawn is 1)P (second ticket drawn is 2) = 1 n = 1 n 1 n2 Part B Many students interpreted this question to mean that the second dr
School: Duke
4. a FFFF FFFV FFVF FVFF VFFF FFVV FVFV FVVF VVFF VFVF VFFV VVVF VVFV VFVV FVVV VVVV 4.b FFFV FFVF FVFF VFFF 4.c FFFF VVVV 4.d FFFF FFFV FFVF FVFF VFFF 4.e Union FFFF VVVV FFFV FFVF FVFF VFFF Intersection FFFF 4.f Union FFFF VVVV FFFV FFVF FVFF VFFF Inter
School: Duke
STAT113 HW3 For Quiz 09/18/09 1. (Chapter 4: 100 ) Let X denote the time to failure (in years) of a certain hydraulic component. Suppose the pdf of X is f (x) = 32/(x + 4)3 for x > 0. (a) (b) (c) (d) (e) Verify that f (x) is a legitimate pdf. Determine th
School: Duke
STAT113 HW3 Solutions 1. (Chapter 4: 100 ) Solution: (a) Two conditions are required for f (x) to be a legitimate pdf: i. f (x) > 0, x > 0. 0 1 ii. f (x)dx = 32/(x + 4)3 dx = 32 =1 2(x + 4)2 0 0 (b) Let F (x) denote the cdf of X. x x 09/18/09 F (x) = 0 f
School: Duke
Course: Probabil Measure Theory
STA 205: Probability & Measure Theory Homework #1 solutions February 8, 2005 1. Suppose = {0,1} and C = {0}.Enumerate ,the class of all -fields containing C Sol: Note that C is a collection of subsets of ,and (C) = {0}, {1}, {0, 1}, } But also noti
School: Duke
Course: Linear Models
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School: Duke
Course: Probabil Statis In Egr
Reading: chap 6 & 7 6.22 Let X denote the proportion of allotted time that a randomly selected student spends working on a certain aptitude test. Suppose the pdf of X is f (x; ) = ( + 1)x 0 x 1 0 otherwise where 1 < . A random sample of ten st
School: Duke
Course: Probabil Statis In Egr
Reading: chap 4.14.3, pp177-179, 4.6 HW: 5, 33, 42, 44, 51 (use normal approximation), 117 4.5 A college professor never finishes his lecture before the end of the hour and always finishes his lectures within 2 min after the hour. Let X=the time th
School: Duke
Course: Probabil Statis In Egr
Aug 30, Sep 1 : Probability Reading: chap 2 HW: 26, 30, 33, 48, 59, 61, 69, 78, 80. 26. A certain system can experience three different types of defects. Let Ai (i=1, 2, 3) denote the event that the system has a defect of type i. Suppose that P(A1
School: Duke
Course: Appl Data Analy Env Sci
STA 240 Homework 6 1. MARIJUANA AND TESTOSTERONE 1. Equality of means among controls, mild users, and heavy users . H0: control= mild= heavy HA: not all j are equal [20(742 - 579) 2 + 11( 503 - 579 ) + 9 ( 309 - 579 )
School: Duke
Course: Statistical Inference
STAT215: Homework 1 Due: Wednesday, Feb 1 Each problem is worth ten points, for a total of 100 points. Bickel & Doksum problems from pages 66-71: 1.1.3, 1.1.15 Bickel & Doksum problems from pages 71-74: 1.2.3, 1.2.8, 1.2.15 Bickel & Doksum proble
School: Duke
Course: Probabil Statis In Egr
STA 113 HW 6 (Provided by Andrew Dreher) October 31, 2004 6.9 Each of 150 newly manufactured items is examined and the number of scratches per item is recorded (the items are supposed to be free of scratches), yielding the following data: Number of
School: Duke
Course: Probabil Statis In Egr
Aug 30, Sep 1 : Probability Reading: chap 2 HW: 26, 30, 33, 48, 59, 61, 69, 78, 80. 26. A certain system can experience three different types of defects. Let Ai (i=1, 2, 3) denote the event that the system has a defect of type i. Suppose that P(A1
School: Duke
Lab Assignment #6 STA 113 November 18, 2009 This is a real data set which was used in Leo Breiman and Jerome H. Friedman (1985), Estimating optimal transformations for multiple regression and correlation, JASA, 80, pp. 580-598. The problem is to predict t
School: Duke
Condence and credible intervals Artin Armagan Lab assignment #5 October 20, 2009 Artin Armagan Condence and credible intervals Lab outline The lab will be due on October 30th. You will be expected to write a short: 1-2 page lab report and also turn in cod
School: Duke
Inference: MLE and Bayesian Sayan Mukherjee Lab assignment Four October 1, 2009 Sayan Mukherjee Inference: MLE and Bayesian Lab outline The lab will be due on the 9th of October. You will be expected to write a short: 1-2 page lab report. The 1-2 pages do
School: Duke
Inference: MLE Sayan Mukherjee Lab assignment Three September 24, 2009 Sayan Mukherjee Inference: MLE Lab outline The lab will be due on the 2th of October. You will be expected to write a short: 1-2 page lab report. The 1-2 pages do not include plots or
School: Duke
Random variables: sampling and plotting Sayan Mukherjee and Artin Armagan Lab assignment Two September 10, 2009 Sayan Mukherjee and Artin Armagan Random variables: sampling and plotting Lab outline The lab will be due on the 18th of September. You will be
School: Duke
STA113 - Probability and Statistics in Engineering, Fall09 Instructors: Sayan Mukherjee and Artin Armagan e-mail: sayan@stat.duke.edu, artin@stat.duke.edu Oce: Old Chem 112, Old Chem 217 Oce hours: TBA Textbook: Jay L. Devore, Probability and Statistics f
School: Duke
Course: Intro Biostatistics
Syllabus STA102, Stangl, Spring 2005 Time and Location: Class M/W 1:15, Social Sciences 136 Instructor: Dalene Stangl Office: 212 Old Chemistry, dalene@stat.duke.edu, 684-4263 Texts: Fundamentals of Biostatistics (latest edition) by Rosner JMP IN S
School: Duke
Course: Data Analy Stat Infer
Data Analysis and Statistical Inference STA 101 Sec 2 Instructor: SYLLABUS AND COURSE POLICIES Fall, 2004 Teaching Assistants: TA Location: Course Web Page: Lectures: Computer Labs: Text: Woncheol Jang Old Chem 223B wjang@stat.duke.edu (919) 684-