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School: N.C. State
Course: Introduction To Statistical Inference And Regression
ST 372 Midterm Exam 1 Sept 23, 2010 Instructor: Yichao Wu Your Name (Print): Your ID: Notes: 1. There are totally 4 problems. 2. Be sure to show all your work; your partial credit might depend on it. 3. NO CREDIT will be given without supporting work. 4.
School: N.C. State
Course: St Pr Clin Tri Epi
ST520, Fall 2012 Homework 2, due: Wednesday, 9/12/2012 1. (5 pts) Show the calculation on slide 64 of the probability that the trial will stop at the 3rd dose level given the true toxicity probabilities and the results at the rst 2 dose levels. 2. (20 pts
School: N.C. State
Course: Stat Quality Prod
WrittenAssignment7 1. 8.1 A process is in statistical control with x =20 and s =12. Specifications are at LSL =16 and USL =24. (a) Estimate the process capability with an appropriate process capability ratio. Cp = =.1111 Cpk = = min(.11111, .11111)=.11111
School: N.C. State
Course: Statistics 311
Statistics 311 Final Exam Definitions Parameter: a summary measure for an entire population Statistic: a summary measure computed from sample data Population: the entire group of units about which inferences will be made Sample: the group of units th
School: N.C. State
Course: Sampling
Surveys are systems for collecting information to describe, compare, and predict attitudes, opinions, values, knowledge, and behavior. Matt Campbell, Richard Couchon, Robert Garland, Rheuben Herbert April 9, 2009 Planning of the Questionnaire Question Wr
School: N.C. State
Solutions to ST 370 Online Second Exam, Fall 2005 1. .22 = Pr(BC is beaten)* Pr(MTS is beaten)*Pr(M is beaten)= .4*.9*.6=.216 2. 6/9 (9 possible values: (1,1)->X=1, (1,2)->X=2, etc.: 1,2,3,2,4,6,3,6,9 each with prob. 1/9) 3. .874 = Pr(-1.23 < Z < 2.13)=
School: N.C. State
Course: Applied Longitudinal Data Analysis
CHAPTER 1 ST 732, M. DAVIDIAN 1 Introduction and Motivation 1.1 Purpose of this course OBJECTIVE: The goal of this course is to provide an overview of statistical models and methods that are useful in the analysis of longitudinal data; that is, data in th
School: N.C. State
Course: Statistic Theory I
Chapter 4: Multiple Random Variables We study the joint distribution of more than two random variables, called a random vector, such that (X, Y ), (X, Y, Z), (X1 , , Xn ), and the distribution of their functions like X + Y , XY Z, or X1 + X2 + + Xn . 1 Bi
School: N.C. State
Course: Statistic Theory I
Common Families of Distributions The family of distributions: a class of pmfs/pdfs indexed by one or more parameters. For example, N(,1), Unif(a,b). Distributions in one family have a common pdf/pmf form but dierent parameter values. For each distributio
School: N.C. State
Course: Statistic Theory I
Chapter 5 1 Properties of A Random Sample. Basic Concepts of Random Sample Def: The random variables X1 , , Xn are called a random sample of size n from the population f (x), if X1 , , Xn are mutually independent random variables and the marginal pdf or p
School: N.C. State
Course: Statistic Theory I
ST521: Chapter 1 1 Overview Of Probability Probability theory is a branch of mathematics that deals with uncertainty. A random experiment is an experiment for which the outcome can not be predicted with certainty. Probability = chance or the likelihood
School: N.C. State
Course: Introduction To Statistics For Engineers
ST361: Ch1.1 Population and Samples I. What is Statistics? Statistics is the science of _ _. Usually it involves collecting partial information (a sample) about a population, and using it to make generalizations (inference) about the population. Ex. Sue w
School: N.C. State
Course: BUCKNER
ST 810J Term Paper A Survey of 2007 Subprime Financial Crisis Jiangdian Wang Introduction In the summer of 2007, a global financial crisis shocked private banks, hedge funds and other financial institutes around the world. Its negative effect has i
School: N.C. State
Course: Sampling
James Hedges James Jeff Jackson Sarah Likshis Devon Sheppard Introduction Telephone surveying is defined as a systematic collection of data from a sample population using a standardized questionnaire. Today well discuss the History Use of RDD to attain a
School: N.C. State
Course: Sampling
SENSITIVETOPICS NicoleMack,NathanSmith,KrystalStrader,ChristineWu Introduction WhatareSensitiveSubjects? SensitiveSubjects/Topicsarethosedealingwith issuesinwhichwewishtokeepprivate includingreceiptofwelfare,income,alcohol anddruguse,criminalhistory,andso
School: N.C. State
Course: Sampling
432 Sampling Lecture 1 Kenneth H. Pollock Biology, Statistics and Biomathematics North Carolina State University, My Introduction: Australia Rural New South Wales Sydney University: B Sc. Cornell University, Ithaca NY: MS & Ph D. MY SCIENCE PHILOSOPHY Dev
School: N.C. State
Course: Sampling
Lecture 19 Double and Two Phase Sampling Introduction Ratio Estimator Regression Estimator (Very Brief) Stratification and Adjusting for Non Response Cluster Sampling (Very Brief) Ecological Examples Summary Remarks Sampling Rare and Clustered Populations
School: N.C. State
Course: Sampling
Introduction Example on a Transect Small Population Example Relationship to Cluster Sampling Problems with Systematic Random Sampling Variances Cyclic patterns Replicated Systematic Random Sampling Simple random sampling is the basis of our sampling theor
School: N.C. State
Course: Sampling
Lecture 15-16: Cluster and Multi-Stage Sampling Designs Important Group Meeting-Time is getting Short for the first two groups especially. Lecture 15-16 Outline Examples of Nested Multi-Level Sampling Units Cluster and Two-Stage Sampling -Cluster- all sec
School: N.C. State
Course: Introduction To Statistical Inference And Regression
ST 372 Midterm Exam 1 Sept 23, 2010 Instructor: Yichao Wu Your Name (Print): Your ID: Notes: 1. There are totally 4 problems. 2. Be sure to show all your work; your partial credit might depend on it. 3. NO CREDIT will be given without supporting work. 4.
School: N.C. State
Course: Statistics 311
Statistics 311 Final Exam Definitions Parameter: a summary measure for an entire population Statistic: a summary measure computed from sample data Population: the entire group of units about which inferences will be made Sample: the group of units th
School: N.C. State
Solutions to ST 370 Online Second Exam, Fall 2005 1. .22 = Pr(BC is beaten)* Pr(MTS is beaten)*Pr(M is beaten)= .4*.9*.6=.216 2. 6/9 (9 possible values: (1,1)->X=1, (1,2)->X=2, etc.: 1,2,3,2,4,6,3,6,9 each with prob. 1/9) 3. .874 = Pr(-1.23 < Z < 2.13)=
School: N.C. State
Statistics 311 Exam 1 Practice Exam NOTE to students: This is the actual exam from a previous semester. It is intended to give you an idea of the type questions the instructor asks and the approximate length of the exam. It does NOT indicate the e
School: N.C. State
Course: STAT
ST 311-651 Sum '09 MIDTERM EXAM SOLUTIONS Reiland 1. Age of employee, quantitative, years; amount contributed monthly, quantitative, dollars; type of contribution, categorical. 2. MUV U$ U" $&) #!& "&$; "&MUV "&"&$ #*&; U" "&MUV #!& #*& #%& U$ "&MUV $&) #
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, TEST SOLUTIONS, FALL 2009 Please sign the following pledge certifying that the work on this test is your own: I have neither given nor received aid on this test. Signature: Printed Name: This test covers material in Chapters 1 12 of the class note
School: N.C. State
Course: St Pr Clin Tri Epi
ST520, Fall 2012 Homework 2, due: Wednesday, 9/12/2012 1. (5 pts) Show the calculation on slide 64 of the probability that the trial will stop at the 3rd dose level given the true toxicity probabilities and the results at the rst 2 dose levels. 2. (20 pts
School: N.C. State
Course: Stat Quality Prod
WrittenAssignment7 1. 8.1 A process is in statistical control with x =20 and s =12. Specifications are at LSL =16 and USL =24. (a) Estimate the process capability with an appropriate process capability ratio. Cp = =.1111 Cpk = = min(.11111, .11111)=.11111
School: N.C. State
ST 370 HW 4 11/27/12 9:30 PM Emily Gaye ST 370, section 001, Fall 2012 Instructor: Renee Moore WebAssign ST 370 HW 4 (Homework) Current Score : 10 / 10 Due : Thursday, September 13 2012 11:59 PM EDT The due date for this assignment is past. Your work can
School: N.C. State
ST 370 HW 5 11/27/12 9:31 PM Emily Gaye ST 370, section 001, Fall 2012 Instructor: Renee Moore WebAssign ST 370 HW 5 (Homework) Current Score : 10 / 10 Due : Thursday, September 20 2012 11:59 PM EDT The due date for this assignment is past. Your work can
School: N.C. State
ST 370 HW 2 11/27/12 9:28 PM Emily Gaye ST 370, section 001, Fall 2012 Instructor: Renee Moore WebAssign ST 370 HW 2 (Homework) Current Score : 10 / 10 Due : Thursday, August 30 2012 11:59 PM EDT The due date for this assignment is past. Your work can be
School: N.C. State
ST 370 HW 3 11/27/12 9:29 PM WebAssign ST 370 HW 3 (Homework) Current Score : 9 / 10 Emily Gaye ST 370, section 001, Fall 2012 Instructor: Renee Moore Due : Thursday, September 6 2012 11:59 PM EDT The due date for this assignment is past. Your work can be
School: N.C. State
Course: Statistic Theory I
ST 521 LAB Solution #10 Prepared by Dong wang and Chen-Yen Lin FALL 2011 5.3 Since Y i =1 or 0 and they are independent and with the same probability p( y i 1 )= 1 F ( ) . Thus n y i is binomial distribution ( n, p 1 F ( ) ) i 1 5.6 a, let Z=X-Y, W=X, the
School: N.C. State
Course: Statistic Theory I
ST 521: Statistical Theory I Solution to Lab Exercise - 9 Prepared by Chen-Yen Lin and Dong Wang Fall, 2011 4.51 (a) P (X/Y t) = P (X tY ) = P (XY t) t 2 if t < 1 if t 1 = EI (XY t) 1 1 2t = EX EY |X I (Y < t/x)|X = EX P (Y t/x) t = EX I (0 < t/x < 1) + 1
School: N.C. State
Course: Statistic Theory I
ST 521 LAB Solution #8 Prepared by Dong wang and Chen-Yen Lin FALL 2011 4.27 Since X and Y are independent normal distribution, the linear combination of them is also normally distributed. By Theorem 4.2.14, U N ( ,2 2 ) , V N ( ,2 2 ) f X ,Y ( x, y ) f X
School: N.C. State
Course: Statistic Theory I
ST 521: Statistical Theory I Solution to Lab Exercise - 7 Prepared by Chen-Yen Lin and Dong Wang Fall, 2011 4.9 P (a X b, c Y d) = P (X b, Y d) P (X b, Y c) P (X a, Y d) + P (X a, Y c) = FX (b)FY (d) FX (b)FY (c) FX (a)FY (d) + FX (a)FY (c) = FX (b)[FY (d
School: N.C. State
Course: Statistic Theory I
ST 521 LAB Solution #6 Prepared by Dong wang and Chen-Yen Lin FALL 2011 3.21 f ( x) 1 1 M X (t ) 2 (1 x ) e tx 1 x 2 dx e tx x 1 x 2 dx 1 x 2 dx 0 0 Thus the moment generating function does not exist. If x is positive, we have e tx x 3.22 (a) E (
School: N.C. State
Course: Statistic Theory I
ST 521: Statistical Theory I Solution to Lab Exercise - 5 Prepared by Chen-Yen Lin and Dong Wang Fall, 2011 1 Let X Hyp(N, M, K ), P (X = x) = K EX = x x=0 K EX (X 1) = x(x x=0 M N M Cx CK x N CK K M N M Cx CK x = N CK x=1 M N M Cx CK x 1) N CK , M (M 1)
School: N.C. State
Course: STAT
ST 311 Su '09 PRACTICE PROBLEMS FOR FINAL EXAM Reiland Exam dates: Wednesday, August 5 - Friday, August 7 Material covered on exam: chapters 18 - 27 the topics in chapters 18 -27 are covered in webassign homework #5 through #8 Needed for exam: 8 " " 11" h
School: N.C. State
ST 370-003 Probability and Statistics for Engineers Fall 2013 Professor: Dr. Justin Post - jbpost2@ncsu.edu - (919) 515-0637 Meeting Place/Time: 124 Dabney T/Th 11:45 - 1:00 Course Goals: Office/Hours: Construct basic numeric and graphical summaries of da
School: N.C. State
Course: Intro To Statistic
Dhruv Sharma ST311 Sum-II ST311 Introduction to Statistics Section 001 Summer II 2007, NC State University Instructor: Dhruv Sharma Email: dbsharma@ncsu.edu Website: www4.ncsu.edu/~dbsharma/st311 Course Webpage: http:/courses.ncsu.edu/st311/lec/001
School: N.C. State
Course: Analy Surviv Data
ST 745001: Analysis of Survival Data Spring, 2005 Textbook Lecture notes 1. Survival Analysis: Techniques for Censored and Truncated Data (2nd edition) by John P. Klein and Melvin L. Moeschberger (the website http:/www.biostat.mcw.edu/homepgs/klei
School: N.C. State
ST 372 Spring 2007 Introduction to Statistical Inference and Regression Instructor: Email: Office: Phone: Office hour: Dr. Judy Huixia Wang wang@stat.ncsu.edu Patterson Hall Rm 209 F (919) 513-1661 Wednesdays, 3pm-5pm (or by appointment) Lectur
School: N.C. State
Course: Introduction To Statistical Inference And Regression
ST 372 Midterm Exam 1 Sept 23, 2010 Instructor: Yichao Wu Your Name (Print): Your ID: Notes: 1. There are totally 4 problems. 2. Be sure to show all your work; your partial credit might depend on it. 3. NO CREDIT will be given without supporting work. 4.
School: N.C. State
Course: St Pr Clin Tri Epi
ST520, Fall 2012 Homework 2, due: Wednesday, 9/12/2012 1. (5 pts) Show the calculation on slide 64 of the probability that the trial will stop at the 3rd dose level given the true toxicity probabilities and the results at the rst 2 dose levels. 2. (20 pts
School: N.C. State
Course: Stat Quality Prod
WrittenAssignment7 1. 8.1 A process is in statistical control with x =20 and s =12. Specifications are at LSL =16 and USL =24. (a) Estimate the process capability with an appropriate process capability ratio. Cp = =.1111 Cpk = = min(.11111, .11111)=.11111
School: N.C. State
Course: Statistics 311
Statistics 311 Final Exam Definitions Parameter: a summary measure for an entire population Statistic: a summary measure computed from sample data Population: the entire group of units about which inferences will be made Sample: the group of units th
School: N.C. State
Course: Sampling
Surveys are systems for collecting information to describe, compare, and predict attitudes, opinions, values, knowledge, and behavior. Matt Campbell, Richard Couchon, Robert Garland, Rheuben Herbert April 9, 2009 Planning of the Questionnaire Question Wr
School: N.C. State
Solutions to ST 370 Online Second Exam, Fall 2005 1. .22 = Pr(BC is beaten)* Pr(MTS is beaten)*Pr(M is beaten)= .4*.9*.6=.216 2. 6/9 (9 possible values: (1,1)->X=1, (1,2)->X=2, etc.: 1,2,3,2,4,6,3,6,9 each with prob. 1/9) 3. .874 = Pr(-1.23 < Z < 2.13)=
School: N.C. State
Course: Sampling
4/14/2009 Presentation Agenda E-MAIL AND INTERNET SURVEYS April 16, 2009 Brief Introduction Construction of E-mail and Internet Surveys Response Rate and Other Problematic Issues Innovations in E-mail and Internet Surveys Privacy Issues in E-mail and Inte
School: N.C. State
ST 370 HW 4 11/27/12 9:30 PM Emily Gaye ST 370, section 001, Fall 2012 Instructor: Renee Moore WebAssign ST 370 HW 4 (Homework) Current Score : 10 / 10 Due : Thursday, September 13 2012 11:59 PM EDT The due date for this assignment is past. Your work can
School: N.C. State
Statistics 311 Exam 1 Practice Exam NOTE to students: This is the actual exam from a previous semester. It is intended to give you an idea of the type questions the instructor asks and the approximate length of the exam. It does NOT indicate the e
School: N.C. State
ST 370 HW 5 11/27/12 9:31 PM Emily Gaye ST 370, section 001, Fall 2012 Instructor: Renee Moore WebAssign ST 370 HW 5 (Homework) Current Score : 10 / 10 Due : Thursday, September 20 2012 11:59 PM EDT The due date for this assignment is past. Your work can
School: N.C. State
ST 370 HW 2 11/27/12 9:28 PM Emily Gaye ST 370, section 001, Fall 2012 Instructor: Renee Moore WebAssign ST 370 HW 2 (Homework) Current Score : 10 / 10 Due : Thursday, August 30 2012 11:59 PM EDT The due date for this assignment is past. Your work can be
School: N.C. State
ST 370 HW 3 11/27/12 9:29 PM WebAssign ST 370 HW 3 (Homework) Current Score : 9 / 10 Emily Gaye ST 370, section 001, Fall 2012 Instructor: Renee Moore Due : Thursday, September 6 2012 11:59 PM EDT The due date for this assignment is past. Your work can be
School: N.C. State
9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-002 Smith Section SPRING 2004 HW # 3 Due Tues Feb 17, 2004 In Devore unless noted otherwise. 1. Riddle sheet #31 passed out in class 2. text, p. 90, # 74 3. tex
School: N.C. State
9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-002 Smith Section SPRING 2004 WEEK 1 Readings: Cartoon Guide Chapters 1 and 2 Devore: Chapter 1 URL for football data HW # 1 Due Tuesday Jan 27, 2004 1. journal
School: N.C. State
9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-002 Smith Section SPRING 2004 READINGS: Devore: 4.1, 4.2, 4.3, 4.4,4.5 cartoon guide: chapters 4 and 5 HW # 5 Due Thursday March 18, 2004 ST PATRICK's DAY In De
School: N.C. State
9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-002 Smith Section Spring 2004 HW # 2 Due Tuesday Feb 10,12 2004 In Devore unless noted otherwise. problems 1 to 7 passed out in class they are also at D H Hill
School: N.C. State
Course: STAT
ST 311 Su '09 PRACTICE PROBLEMS FOR FINAL EXAM Reiland Exam dates: Wednesday, August 5 - Friday, August 7 Material covered on exam: chapters 18 - 27 the topics in chapters 18 -27 are covered in webassign homework #5 through #8 Needed for exam: 8 " " 11" h
School: N.C. State
Course: STAT
ST 311-651 Sum '09 MIDTERM EXAM SOLUTIONS Reiland 1. Age of employee, quantitative, years; amount contributed monthly, quantitative, dollars; type of contribution, categorical. 2. MUV U$ U" $&) #!& "&$; "&MUV "&"&$ #*&; U" "&MUV #!& #*& #%& U$ "&MUV $&) #
School: N.C. State
9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-001 Smith Section FALL 2001 HW # 8 Due Tuesday NOV 27, 2001 In Devore unless noted otherwise. 1. text, p.286 , # 5 2. text, p.303, # 35 3. text, p.306, # 42 4.
School: N.C. State
9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-001 Smith Section FALL 2002 HW # 7 Due Thursday Nov 14, 2002 In Devore unless noted otherwise. 1. text, p. 232 , # 41 2. text, p. 243 , # 65 3. text, p. 247 , #
School: N.C. State
9/18/2014 www4.stat.ncsu.edu/~bmasmith/371S04/371finA.txt 1 a . 2 c . 3 b . 4 b . 5 a . 6 c . 7 d . 8 e . 9 b . 1.badd 0 n 1.c 1 1.b 2 1.a 3 1.d 4 1.d 5 1.a 6 1.d 7 1.c 8 1.c 9 2.d 0 2.c 1 2.b 2 2.a 3 2.c 4 2.d 5 2.c 6 2.e 7 2.a 8 2.a 9 3.b 0 3.c 1 3.a 2
School: N.C. State
9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-002 Smith Section SPRING 2004 HW # 4 Due Thursday Feb 26, 2004 1. Assume that for single lanches of a space shuttle, that there is a constant probability = 0.10
School: N.C. State
9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-002 Smith Section SPRING 2004 HW # 6A Due Thursday April 1, 2004 In Devore unless noted otherwise. 1. text, p.188 , # 75 2. text, p.188 , # 78 3. text, p.216, #
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 1 EXTRA PROBLEM SOLUTIONS, FALL 2009 1. (a) The loglikelihood is n log L = cfw_Yj log f (xj , ) f (xj , ) log Yj !. j=1 Taking derivatives with respect to and setting equal to zero gives the estimating equation n / log L = cfw_Yj f (xj ,
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 1 EXTRA PROBLEMS, FALL 2009 These problems are from previous years and are for you to work on or not as you choose; they are not to be turned in. You should be familiar with the concepts covered by these problems for the midterm test. Sol
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 5, FALL 2009 These problems are to be turned in on the due date. 1. Does how one estimates really matter? Consider the usual mean-variance model var(Yj |xj ) = 2 g2 (, , xj ), E(Yj |xj ) = f (xj , ), (1) where the (Yj , xj ), j = 1, . . .
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 6, FALL 2009 These problems are to be turned in on the due date. 1. Recall the data in Homework 5, Problem 3, from a clinical trial studying the eectiveness of a treatment for patients with respiratory illness. See that problem for a desc
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 5 SOLUTIONS, FALL 2009 1. You should have written one program that computes all of the estimators. It is not necessarily appropriate to have a separate program for each estimator; in order that the comparison be sound, all estimators must
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 6 SOLUTIONS, FALL 2009 1. (a) Here, with the subject-specic model (2), rather than modeling the probabilities of having good (Y = 1) respiratory status in the population of subjects over time on the two treatments, the probabilities that
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 4, FALL 2009 1. Do the folklore properties hold in nite samples? Consider our usual mean-variance model var(Yj |xj ) = 2 g2 (, , xj ), E(Yj |xj ) = f (xj , ), (1) where the (Yj , xj ), j = 1, . . . , n are independent, and suppose that bo
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 4 SOLUTIONS, FALL 2009 1. Results of my simulation with S = 1000 are as follows. SIMULATION RESULTS FROM 1000 MONTE CARLO DATA SETS beta1 OLS Bias = -1e-04 Rel Bias = 0 Rel Bias SD = -0.0288 Mean beta1, SD beta1, Mean estimated SE beta1,
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 3 SOLUTIONS, FALL 2009 1. (a) Here is the plot weve also superimposed the GLS-PL t, as requested in part (g), where a dierent line type is used for each vibration condition. 1 1 1 60 1 1 50 0 0 0 0 0 0 1 0 30 dissolution 40 1 20 1 0 0 1 1
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 3, FALL 2009 These problems are to be turned in on the due date. 1. Using standard nonlinear regression software to implement GLS and normal theory ML with estimation of . For drugs intended to be administered orally, such as solid tablet
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 2, FALL 2009 These two problems are to be turned in on the due date. 1. Using standard nonlinear regression software to implement GLS. The data in the le trees.dat, available on the class web page, were collected by forest science researc
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 1 SOLUTIONS, FALL 2009 1. We demonstate two ways to solve the system of ODEs. (a) First, we use the standard method of Laplace transforms. It is often the case that nding the solution of a complicated system of dierential equations may be
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 1, FALL 2009 These problems are to be turned in on the due date. 1. Many nonlinear (in parameters) functions used to describe biological and physical phenomena arise as the solution to a system of ordinary dierential equations (ODEs). Thi
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 2 SOLUTIONS, FALL 2009 1. (a) Here are plots of the data by site preparation treatment (with the ts for part (c) superimposed, too). 20 1 1 0 1 1 1 0 0 15 0 16 18 0 1 0 1 1 0 0 1 1 10 dominant height (m) 0 0 0 0 1 1 5 0 0 1 0 0 1 1 4 6 8
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, TEST SOLUTIONS, FALL 2009 Please sign the following pledge certifying that the work on this test is your own: I have neither given nor received aid on this test. Signature: Printed Name: This test covers material in Chapters 1 12 of the class note
School: N.C. State
Course: Applied Longitudinal Data Analysis
CHAPTER 1 ST 732, M. DAVIDIAN 1 Introduction and Motivation 1.1 Purpose of this course OBJECTIVE: The goal of this course is to provide an overview of statistical models and methods that are useful in the analysis of longitudinal data; that is, data in th
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 5, SPRING 2007 1. Recall the lead exposure study from Homework 3, Problem 3. Consider model (1) in the statement of that problem, which is repeated here for convenience: Let Yij denote the jth lead level measurement on the ith child at
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 5, SOLUTIONS, SPRING 2007 1. We obtain Yij = (0 + 0a ai + 0g gi + 0ag ai gi ) + (k + ka ai + kg gi + kag ai gi )tij + (b0i + b1i tij + eij ). Because all of b0i , b1i and eij have mean zero, is it straightforward to see that E(Yij ) = (
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 6, SOLUTIONS, SPRING 2007 1. (a) (i) For such a subject, tj = 1. The expected tumor response E(Yj ) for such a subject (which is the same as the probability that the subject develops a new tumor under these conditions, P (Yj = 1), is e0
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 3, SPRING 2007 1. A study was conducted in which m = 40 devices were randomized to be operated under 4 dierent sets of conditions, 10 devices per set of conditions. A response reecting performance level of such devices was measured on e
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 6, SPRING 2007 1. A study was conducted to compare two treatments for patients with bladder cancer. Each of the n = 100 subjects recruited into the study had recently had surgery to remove the tumor; at baseline, each was then randomize
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 4, SOLUTIONS, SPRING 2007 1. (a) Here, the dimension of i is (2 1), so Z i this matrix becomes 1 1 Zi = 1 1 1 is (ni 2) matrix in general. When ni = 5, ti1 ti2 ti3 ti4 ti5 , so that sweeping the jth row of Z i down i yields the expres
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 3, SOLUTIONS, SPRING 2007 1. (a) The model that seems reasonable to capture the possibility of curvature of the mean proles as in criterion (ii) is one that allows the mean as a function of time in each group to be quadratic in time. Fu
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 4, SPRING 2007 1. Consider a straight line model for individual behavior as in Equation (9.1) of the notes, which for unit i is of the form Yij = 0i + 1i tij + eij , (1) where Yij is the random variable representing the observation that
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 1, SPRING 2007 The rst few exercises are meant to familiarize you with some operations that we will summarize using matrix notation throughout the course. Use of SAS to carry out the analyses we will discuss requires familiarity with th
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 2, SOLUTIONS, SPRING 2007 1. (a) We have M = 11 21 31 41 12 22 32 42 13 23 33 43 . (b) We have a = (1, 0, 0, 0), so that a M = (11 , 12 , 13 ). i i (c) We have = 1 2 3 4 1 2 3 ( )11 ( )12 ( )13 ( )21 ( )22 ( )23 ( )31 ( )32 ( )33 ( )41
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 1, SOLUTIONS, SPRING 2007 1. (a) First, it is clear that, using the result at the top of p. 35, E(c1 Y1 + c2 Y2 ) = c1 1 + c2 2 . Thus, using (3.2), var(c1 Y1 + c2 Y2 ) = Ecfw_(c1 Y1 + c2 Y2 c1 1 c2 2 )2 . This may be rewritten as, usin
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 2, SPRING 2007 1. Suppose that we have a situation in which units have been randomized into q = 4 groups and each unit is observed at the same n = 3 times. As in the notation in the notes, let j be the mean for the th group at the jth t
School: N.C. State
Course: Statistic Theory I
Chapter 4: Multiple Random Variables We study the joint distribution of more than two random variables, called a random vector, such that (X, Y ), (X, Y, Z), (X1 , , Xn ), and the distribution of their functions like X + Y , XY Z, or X1 + X2 + + Xn . 1 Bi
School: N.C. State
Course: Statistic Theory I
Common Families of Distributions The family of distributions: a class of pmfs/pdfs indexed by one or more parameters. For example, N(,1), Unif(a,b). Distributions in one family have a common pdf/pmf form but dierent parameter values. For each distributio
School: N.C. State
Course: Statistic Theory I
Chapter 5 1 Properties of A Random Sample. Basic Concepts of Random Sample Def: The random variables X1 , , Xn are called a random sample of size n from the population f (x), if X1 , , Xn are mutually independent random variables and the marginal pdf or p
School: N.C. State
Course: Statistic Theory I
ST521: Chapter 1 1 Overview Of Probability Probability theory is a branch of mathematics that deals with uncertainty. A random experiment is an experiment for which the outcome can not be predicted with certainty. Probability = chance or the likelihood
School: N.C. State
Course: Applied Longitudinal Data Analysis
CHAPTER 1 ST 732, M. DAVIDIAN 1 Introduction and Motivation 1.1 Purpose of this course OBJECTIVE: The goal of this course is to provide an overview of statistical models and methods that are useful in the analysis of longitudinal data; that is, data in th
School: N.C. State
Course: Statistic Theory I
Chapter 4: Multiple Random Variables We study the joint distribution of more than two random variables, called a random vector, such that (X, Y ), (X, Y, Z), (X1 , , Xn ), and the distribution of their functions like X + Y , XY Z, or X1 + X2 + + Xn . 1 Bi
School: N.C. State
Course: Statistic Theory I
Common Families of Distributions The family of distributions: a class of pmfs/pdfs indexed by one or more parameters. For example, N(,1), Unif(a,b). Distributions in one family have a common pdf/pmf form but dierent parameter values. For each distributio
School: N.C. State
Course: Statistic Theory I
Chapter 5 1 Properties of A Random Sample. Basic Concepts of Random Sample Def: The random variables X1 , , Xn are called a random sample of size n from the population f (x), if X1 , , Xn are mutually independent random variables and the marginal pdf or p
School: N.C. State
Course: Statistic Theory I
ST521: Chapter 1 1 Overview Of Probability Probability theory is a branch of mathematics that deals with uncertainty. A random experiment is an experiment for which the outcome can not be predicted with certainty. Probability = chance or the likelihood
School: N.C. State
Course: Introduction To Statistics For Engineers
ST361: Ch1.1 Population and Samples I. What is Statistics? Statistics is the science of _ _. Usually it involves collecting partial information (a sample) about a population, and using it to make generalizations (inference) about the population. Ex. Sue w
School: N.C. State
Course: Introduction To Statistics For Engineers
ST361: Ch1.4 Distribution of Continuous R.V.: Normal Distribution Topics: 1.4 Normal Distribution, and its density function, mean, variance Standard Normal Distribution: (a) Calculating Probability (b) Calculating Percentile General Normal Distribution: (
School: N.C. State
Course: Introduction To Statistics For Engineers
ST361: Ch1.2 Graphical Methods for Describing Data Topics: Types of variables: o Categorical variables o Numerical variables: discrete variable, continuous variable Methods for visual displaying data Categorical variable Pie chart Bar chart Numerical va
School: N.C. State
Course: Introduction To Statistics For Engineers
ST361: Ch1.6 Distribution of Discrete R.V. : Binomial Distribution Topics: What is Binomial distribution? Probability function Mean, variance and standard deviation - Binomial Distribution A binomial distribution is one useful distribution to describe _,
School: N.C. State
Course: Statistical Theory II
Lt\b4 @y fu(.ft)rl-l-c.\+ictl To c.heC-1( -H11A+ 1-t i!. )1r/.-hue11+ "' J.hrn, /-e.i Slt4'tTLll?.n+ C.vn,pl-e.-+-e q Por- all B +na+ I) t'\"1-I I'\ bewuse. lJ iJfld- J_ I a.e it 1.s (o i-ollq I- [()-1 .lJ c.i Ecfw_ '( J cfw_) it, liI I I =:; 8 h.inlt-iOl
School: N.C. State
Course: Statistical Theory II
ST522 '~ .~J E ( X.J. J =- V-"rl Xl -t (t:IX) 1 ~ X.l (1 \~ 6.1 e ~-tlflllt+or- 111n bi.<.\Sed Gin J -:. of- t5 ~ -712 e. ~ tl 6 a .i.1c~L. - a 6 . "fhi,i~ I 6 -: _, - -t rv 64. :.x:z. J) 1(4" 6-3 - ?:.-112-0.,_ :. o" I 1L1 is Becaw~e Cc + 2i1 . -=- 0 "V
School: N.C. State
Course: Introduction To Mathematical Statistics II
Contents 1 Contents 7 Random Samples, Statistics and Sampling distributions 2 7.1 Sampling Distributions . . . . . . . . . . . . . . . . . . 2 7.2 Sampling Distributions Related to the Normal Distribution 10 7.3 Central Limit Theorem . . . . . . . . . . .
School: N.C. State
Course: Introduction To Mathematical Statistics II
Contents 1 Contents 8 Parameter Estimation 2 8.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . 2 8.2 Properties of Estimators . . . . . . . . . . . . . . . . . . 6 8.3 Some Common Point Estimators . . . . . . . . . . . . . 17 8.4 Error of E
School: N.C. State
Course: Statistical Theory II
1 ST522-002 Statistical Theory IIStatistical Inference Spring 2014 Huixia Judy Wang hwang3@ncsu.edu Oce: 4270 SAS Hall Acknowledgement: part of the slides are modications of the handouts of Drs. Howard Bondell and Donald Martin. 2 Course Outline: Chapter
School: N.C. State
Course: Statistical Theory II
5 Order Statistics ( ) 5 Order Statistics ( ) 6 Principles of Data Reduction 6.1 Statistical Inference & Data Reduction Suppose data X = (X1 , . . . , Xn ) are from a probability distribution P , which is either completely or partially unknown, e.g. Poiss
School: N.C. State
Course: Statistical Theory II
5 Order Statistics (X) 5 Order Statistics (X) 6 Principles of Data Reduction (X) 7 Point Estimation 7.1 Basics Main goal: nd a point estimator (a function of the sample) to estimate either or some function of . Knowledge of the parameter yields knowledge
School: N.C. State
Course: Statistical Theory II
ST 522 FINAL EXAM REVIEW SOLUTIONS and 2 Pr(0 < nT < na ) + Pr(nT >nb) = , where nT n
School: N.C. State
POSTER PROJECT Group emails hwu11@ncsu.edu Questions to Note What is the main thesis of our project? What are the sub-topics do we need to include? What pictures/charts would we need? Topics to Note Improving Drivers Education Use of technology Statistic
School: N.C. State
Course: Sampling
ST 432 Kenneth H. Pollock 1-23-09. Overview: The Estimation of Subpopulation (or Domain) Parameters in Finite Population Sampling based on Simple Random Sampling without replacement of the Population. In virtually all surveys interest will not be only in
School: N.C. State
Course: Sampling
Regression Methods Improve Precision if Auxiliary Variable x is available Linear Regression thru Origin Ratio Estimator (7) Estimators in Text Model yi = xi +i y x Regression thru origin with errors increasing with x. Discussed in class and text. r = N r
School: N.C. State
Course: Sampling
ST 432 Kenneth H Pollock Draft Notes January 23, 2009 NC Wildlife Commission 2007-2008 Hunter Survey Design and Analysis Kenneth H. Pollock, Professor, and Zhi Wen, Graduate Student Departments of Biology (and Statistics) North Carolina State University B
School: N.C. State
Course: Sampling
ST 432 Dual and Multiple Frame Sampling. Class Notes for your Information April 2009 Introduction List Frames I have tried to emphasize the crucial importance of having a good sampling frame for sound application of probability sampling. In many examples
School: N.C. State
Qualitative Data: data that come in terms of categories like ethnicity and sex and brand name. Quantitative Data: data that consists of numbers. o o o Nominal: this type of data is just like qualitative data; the only difference is that th
School: N.C. State
ST 520 Statistical Principles of Clinical Trials Lecture Notes (Modied from Dr. A. Tsiatis Lecture Notes) Daowen Zhang Department of Statistics North Carolina State University c 2008 by Anastasios A. Tsiatis and Daowen Zhang TABLE OF CONTENTS ST
School: N.C. State
Chapter 2: Heating Earth's Surface and Atmosphere Earth-Sun Relationships Solar radiation accounts for more than 99.9% of the energy that heats the earth and its atmosphere This energy is not distributed evenly, however, as it varies by: It is this u
School: N.C. State
Course: BUCKNER
ST 810J Term Paper A Survey of 2007 Subprime Financial Crisis Jiangdian Wang Introduction In the summer of 2007, a global financial crisis shocked private banks, hedge funds and other financial institutes around the world. Its negative effect has i
School: N.C. State
Course: Sampling
James Hedges James Jeff Jackson Sarah Likshis Devon Sheppard Introduction Telephone surveying is defined as a systematic collection of data from a sample population using a standardized questionnaire. Today well discuss the History Use of RDD to attain a
School: N.C. State
Course: Sampling
SENSITIVETOPICS NicoleMack,NathanSmith,KrystalStrader,ChristineWu Introduction WhatareSensitiveSubjects? SensitiveSubjects/Topicsarethosedealingwith issuesinwhichwewishtokeepprivate includingreceiptofwelfare,income,alcohol anddruguse,criminalhistory,andso
School: N.C. State
Course: Sampling
432 Sampling Lecture 1 Kenneth H. Pollock Biology, Statistics and Biomathematics North Carolina State University, My Introduction: Australia Rural New South Wales Sydney University: B Sc. Cornell University, Ithaca NY: MS & Ph D. MY SCIENCE PHILOSOPHY Dev
School: N.C. State
Course: Sampling
Lecture 19 Double and Two Phase Sampling Introduction Ratio Estimator Regression Estimator (Very Brief) Stratification and Adjusting for Non Response Cluster Sampling (Very Brief) Ecological Examples Summary Remarks Sampling Rare and Clustered Populations
School: N.C. State
Course: Sampling
Introduction Example on a Transect Small Population Example Relationship to Cluster Sampling Problems with Systematic Random Sampling Variances Cyclic patterns Replicated Systematic Random Sampling Simple random sampling is the basis of our sampling theor
School: N.C. State
Course: Sampling
Lecture 15-16: Cluster and Multi-Stage Sampling Designs Important Group Meeting-Time is getting Short for the first two groups especially. Lecture 15-16 Outline Examples of Nested Multi-Level Sampling Units Cluster and Two-Stage Sampling -Cluster- all sec
School: N.C. State
Course: Sampling
Lecture 2 Sampling Theory Last Lecture Recap Introductory Remarks (Ch 1) Finite Populations and Samples Important Basic Sampling Designs Simple Random Sampling Estimation of Population Mean and Total Review of Some Properties of the Sample Mean The sample
School: N.C. State
Course: Sampling
The World of Complex Surveys Contact G. Gordon Brown TEL: 919-485-5647 Email: ggbrown@rti.org 2 Complex Surveys Complex Outline Outline Starting Issues Identify Target Population Hypotheses of Interest Sample Design Data Collection Data Preprocessin
School: N.C. State
Course: Sampling
ST 432 Notes on Weighting, Imputation and Variance Calculations April 2009 Weighting Methods Basic Form of Population Total Estimator The Horvitz Thompson estimator of the population total can be represented by = ( yi i ), where i is the inclusion probab
School: N.C. State
Course: Introduction To Statistical Inference And Regression
ST 372 Midterm Exam 1 Sept 23, 2010 Instructor: Yichao Wu Your Name (Print): Your ID: Notes: 1. There are totally 4 problems. 2. Be sure to show all your work; your partial credit might depend on it. 3. NO CREDIT will be given without supporting work. 4.
School: N.C. State
Course: Statistics 311
Statistics 311 Final Exam Definitions Parameter: a summary measure for an entire population Statistic: a summary measure computed from sample data Population: the entire group of units about which inferences will be made Sample: the group of units th
School: N.C. State
Solutions to ST 370 Online Second Exam, Fall 2005 1. .22 = Pr(BC is beaten)* Pr(MTS is beaten)*Pr(M is beaten)= .4*.9*.6=.216 2. 6/9 (9 possible values: (1,1)->X=1, (1,2)->X=2, etc.: 1,2,3,2,4,6,3,6,9 each with prob. 1/9) 3. .874 = Pr(-1.23 < Z < 2.13)=
School: N.C. State
Statistics 311 Exam 1 Practice Exam NOTE to students: This is the actual exam from a previous semester. It is intended to give you an idea of the type questions the instructor asks and the approximate length of the exam. It does NOT indicate the e
School: N.C. State
Course: STAT
ST 311-651 Sum '09 MIDTERM EXAM SOLUTIONS Reiland 1. Age of employee, quantitative, years; amount contributed monthly, quantitative, dollars; type of contribution, categorical. 2. MUV U$ U" $&) #!& "&$; "&MUV "&"&$ #*&; U" "&MUV #!& #*& #%& U$ "&MUV $&) #
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, TEST SOLUTIONS, FALL 2009 Please sign the following pledge certifying that the work on this test is your own: I have neither given nor received aid on this test. Signature: Printed Name: This test covers material in Chapters 1 12 of the class note
School: N.C. State
Course: Experimental Design
Quiz 1, St 711 Question 1: An experiment is run on hamsters, each in its own cage with an exercise wheel connected to a timer. Average time per hour on the wheel is the response. The treatments are diets, assigned to the cages at random with unequal repli
School: N.C. State
Course: Experimental Design
Test 1, St711, Fall 2010, Dickey v1 Throughout, make the usual assumptions (independent, N(0,) ) on the error terms, but make no other arbitrary assumptions on model parameters. 1. I ran a completely randomized design experiment with three treatment group
School: N.C. State
Course: Experimental Design
Quiz 1, St711 (Experimental Design) Dickey 1. You plan to lay out an experiment on growth of 5 varieties of wheat using plots in a square field divided into 25 square plots. Because the field is on a slant, plots near the south edge are likely more fertil
School: N.C. State
Course: Experimental Design
St 711 Quiz 1, Fall 2009 Dickey 1. Below is a vector Y of responses to fertilizer treatments A, B, C, and D (in order) from a completely randomized design. Ive also listed the data in a table to the right. You also see a design matrix X having an intercep
School: N.C. State
Course: Experimental Design
Test 2, St 711, Dickey Question 1: I ran a Latin square design with k rows, k columns, and k treatments. Row and column effects are considered random and treatments are fixed. The response is Y and I issued this code: proc glm; class row col trt; model Y
School: N.C. State
Course: Experimental Design
Test 2 St 711 Fall 2008, Dickey I have 8 pens with 3 pigs in each (24 pigs total). Each pen has a feed trough. At the beginning, each pig is injected with a drug (A, B, or C) to reduce infection and promote growth. In each pen I randomly assign the three
School: N.C. State
Course: Experimental Design
Quiz II, St 711, Dickey 1. I have a balanced incomplete block (BIB) design. I ran the code PROC GLM; CLASS BLOCK TRT; MODEL Y = BLOCK TRT; RANDOM BLOCK; getting these results: Sum of Squares 533 700 140 Type III Mean Square Source df Expected Mean Square
School: N.C. State
Course: Experimental Design
Quiz 3 St 711 1. (24 pts.) Here is a two way table of treatment means in a 22 factorial arrangement. The experimental design was a randomized complete block with 8 blocks so each mean is an average of 8 observations. Means of 8 observations: B low B high
School: N.C. State
Course: Experimental Design
Quiz 3 , St 711, Fall 2007, Dickey 1. These data are from a 1/4 replicate of a 25 factorial. Columns 2-6 are labeled for the factors (A, B, C, D, E) with whose main effects they are associated. Y 18 20 12 10 16 8 14 6 A -1 -1 -1 -1 1 1 1 1 B -1 -1 1 1 -1
School: N.C. State
Course: Experimental Design
Test 3 Fall 2003 Dickey 1. (36 pts.) An experiment with 3 fertilizers and 4 herbicides is run on 6 fields. Each field is divided into 12 squares arranged in three rows and four columns as shown. In each field, the fertilizers are randomly assigned to the
School: N.C. State
Course: Experimental Design
Test2,St711,Fall2009,Dickey (1)IconstructaYoudenSquarefor7treatmentsfromthefirst4rowsofaLatinSquare. (A)(16pts.)IntheANOVAtableblanksbelow,writeoutasmanydegreesoffreedomasyoucan fromthegiveninformation. Sourcedf Rows_ Columns_ Trts_ Error_ (B)(5pts.)Howm
School: N.C. State
Course: Experimental Design
Quiz 3, St 711, 2006 (Dickey) (1) I used the autoregressive order 1 structure, AR(1), in a REPEATED statement in PROC MIXED. From the REPEATED statement's RCORR option, a 4x4 autocorrelation matrix results, having 0.125 (=1/8) in the upper right corner. (
School: N.C. State
Course: Introduction To Statistics For Engineers
ST 361 Practice Problems for Final (2009 Fall) Multiple Selections: _ 1. Which of the following is a property of r, the sample correlation coefficient of two numerical variables X and Y? (a) r depends on the units of Y or X (b) r is always between 0 and 1
School: N.C. State
Course: Introduction To Mathematical Statistics II
ST422, Spring, 2014 Diagnostic Quiz (counts as one homework assignment) Full credit assigned with evidence of eort on each problem. If you do not know how to solve the problem, please indicate this with DK for (Dont know, or Cant remember.). 1. Suppose a
School: N.C. State
Course: Introduction To Mathematical Statistics II
ST422 Spring, 2014 Quiz 2 - Solutions Directions: Work independently. Collaboration is explicitly forbidden. 1. Suppose it is feasible to take a random sample of upper division undergraduates at NC State and you are interested in 95% condence intervals of
School: N.C. State
Course: Introduction To Mathematical Statistics II
ST422 Spring, 2014 Quiz 1 Solutions 1. Suppose Y1 and Y2 are independent Poisson random variables with means 1 and 2 respectively. Let W = Y1 + Y2 . (a) Use moment-generating functions to prove that W has the Poisson distribution. ind MW (t) = E[et(Y1 +Y2
School: N.C. State
Course: Experimental Design
Test 2 St 711, 2011, Dickey I. (10 pts.) In the term Balanced Incomplete Block Design what does the word balanced mean, that is, what makes an incomplete block design balanced? II.(15 pts.) I randomly assign 3 feeds to sets of 30 cows (10 per feed). I mea
School: N.C. State
Course: Experimental Design
Test 2, St 711, Dickey 1. (4 pts.) I have seven evaluators, available for a 3 day period, and 7 employees to evaluate. Evaluation takes a day so each evaluator will evaluate 3 employees, rating each on a 1 to 100 scale. Name a design that would be good fo
School: N.C. State
Course: Experimental Design
Test 1, St 711, Dickey, F 2006 Q1: As in our homework, I create columns C1, C2, and C3 to do effects coding for a completely randomized design with 4 treatments and equal replication (so each C is -1 for treatment 4). I have the usual model Yij = + i + ei
School: N.C. State
Course: Experimental Design
Quiz 1 2011, St711, Dickey Here is a SAS printout for analyzing data from a completely randomized design with equal replication of 5 treatments. The model is the usual one Yij = + i + eij for replicate j of treatment i with the usual assumption that the e
School: N.C. State
Course: Experimental Design
Test 2, St 711, Dickey 1. (4 pts.) I have seven evaluators, available for a 3 day period, and 7 employees to evaluate. Evaluation takes a day so each evaluator will evaluate 3 employees, rating each on a 1 to 100 scale. Name a design that would be good fo
School: N.C. State
Course: Experimental Design
Test 3, St 711, Fall 2011, Dickey Throughout, factors are denoted A, B, C, etc. as usual. 1. (14 pts.) How many equal signs _ would appear in the defining contrast for an unreplicated 28-3 fractional factorial experiment (one eighth of a 28)? How many obs
School: N.C. State
Course: Experimental Design
Test 3 St 711 Fall 2010 Dickey 1. I had 6 wines (labeled A through F) to evaluate using tasters as my blocks. I gave each taster a sample of 3 of the 6 wines resulting in a balanced incomplete block design. Here is part of the analysis of variance I got u
School: N.C. State
Course: Experimental Design
Quiz3,St711,Fall2009,Dickey 1.(12pts.) IhaveacalculatedFstatistic2.91onmyoutput andapvalue,Pr>Fthatis0.0413.Thuswereject ournullhypothesisatthe5%level.Exactlywhat doesthat0.0413representonthegraphofF shownhere?UselabelsontheFgraphshown hereandafewwordstoe
School: N.C. State
Course: Introduction To Mathematical Statistics II
ST421 Quiz 1 - practice problems - solutions 1. (a) FU (u) = P (U u) = P (2Y 1 u) u+1 = P (Y ) 2 (u+1)/2 4y 3 dy = = 0 0 u+1 4 2 1 u < 1 (1 < u < 1) u>1 (b) FU (0) = (1/2)4 = 1/16 (c) f (u) = F (u) = 4/16(u + 1)3 (1 < u < 1) (d) E(U ) = E(2Y 1) = 2E(Y )
School: N.C. State
Course: Statistical Theory II
5T S:i2 1. ( o < )(ti> <'. y_ It'll < B) I) x, <. ')('"' <BJ .,:- ~ )jl L ( o < .:. ~ ~i r. ( 0 ' ')(lrJJ y l') '- Vu1i ~e ) ' r\ -= (T~Jl\I_(o(' ')(l,) 1-cfw_&7'i(tu) ~I ) .L l o < Yl' 1) 1. l $ 7 (b) T:. '( 1r11) '/.,<1 1 (:" dt) =- p I f :. p l . -t.)
School: N.C. State
Course: Statistical Theory II
ST522 Practice Midterm Exam Note: the actual length of midterm exam will be dierent from the practice exam. 1. (a) Suppose X is a Binomial(n, p) random variable, 0 < p < 1. Find the MLE for p(1 p). Show that the MLE is not unbiased for p(1 p). Construct a
School: N.C. State
Course: Introduction To Statistical Inference And Regression
ST 372 Midterm Exam 2 Nov 2, 1010 Instructor: Yichao Wu Your Name (Print): Your ID: Notes: 1. There are totally 5 problems plus one bonus question. 2. Be sure to show all your work; your partial credit might depend on it. 3. NO CREDIT will be given withou
School: N.C. State
Course: Introduction To Statistical Inference And Regression
School: N.C. State
Course: Applied Least Squares
ST 708 - Applied Least Squares Dr. Howard Bondell Fall 2009 Midterm Name _ Show ALL of your work to receive full credit. Your test should have total of 10 pages including this cover page and two blank pages at the end that you may remove and use for scrap
School: N.C. State
Course: Applied Least Squares
ST 708 - Applied Least Squares Dr. Howard Bondell Fall 2008 Final Exam Name _ Show ALL of your work to receive full credit. Your test should have 5 questions for a total of 14 pages including this cover page, two blank pages that you may use for scrap pap
School: N.C. State
Experimental Protocol ST 370 Section: _003_ Group Name _Rolling Thunder_ Group Members Derek Schreiner Haizhou Wu Kenneth Smith Lopez David Rogers Email addresses dsschrei@ncsu.edu hwu11@ncsu.edu ksmithl@ncsu.edu daroger2@ncsu.edu 1. Purpose: This experim
School: N.C. State
Second Exam - Fall 2002 - ST 370 Online - A Darken the circle on the answer sheet corresponding to your answer. Use a number 2 pencil. Stray marks on the form may cause errors. All questions are worth 5 points. 1. Let X be a random variable. Suppose that
School: N.C. State
Solutions to ST 370 Online Second Exam, Fall 2002, Version A. 1. continuous random variable 2. [1-(1-.8)^3][1-(1-.9)^2]=.982 Since the two parallel circuits are in series, one containing 1,2,3 and the other containing 4 and 5, hence both have to work prop
School: N.C. State
Exam 2 - Fall 2004 - ST 370 Online/Distance - A Darken the circle on the answer sheet corresponding to your answer. Use a number 2 pencil. Stray marks on the form may cause errors. All questions are worth 5 points. 1. What is the probability that a standa
School: N.C. State
Solutions to ST 370 Online Test 2, Fall 2004 1. .9530 = .9608 - .0078 2. .979 = [1-(1-.90)(1-.80)][1-(1-.95)(1-.98)]=(.98)(.999) 3. .594 = Pr(X=>2) = 1 - Pr(X=<1) = 1 - [Pr(X=0)+Pr(X=1)] =1-[(2)^0]*exp(-2)-[(2)^1]*exp(-2)=1-(1+2)*exp(-2) = 1 - 3exp(-2)
School: N.C. State
Exam 2 - Fall 2005 - ST 370 Online/Distance - A Circle the answer. All questions are worth 5 points. 1. NCSU has three games left on the football schedule, Boston College, Middle Tennessee State, and Maryland. Suppose that the probability of winning over
School: N.C. State
Second Exam - Spring 2002 - ST 370 Online - A Darken the circle on the answer sheet corresponding to your answer. Use a number 2 pencil. Stray marks on the form may cause errors. All questions are worth 5 points. 1. The sample space of a random experiment
School: N.C. State
Solutions to ST 370 Online Second Exam, Spring 2002. 1. A list of all possible outcomes. 2. 0.903 = [1-(1-.8)(1-.8)](.95)[1-(1-.9)(1-.9)] 3. 0.30 = Pr(H2|B)Pr(B) = (.75)(.40) 4. 40/170 5. 70/100 ("E. None of the above" is correct.) 6. 100/170 7. 0.97 = Pr
School: N.C. State
Final Exam - Fall 2002 - ST 370 Online - A Darken the circle on the answer sheet corresponding to your answer. Use a number 2 pencil. Stray marks on the form may cause errors. All questions are worth 3 points. (Since there are only 30 questions, Im giving
School: N.C. State
Solutions to ST 370 Final Exam, Fall 2002 1. sample standard deviation 2. 0.0004 3. 7/5 4. 5/24 5. 2 replications 6. experimental units 7. Temperature is more important than time. 8. 0.7487 9. -0.8653 10. Y = 3.165 - 0.012*temp 11. 0.8185 12. 10.495 13. C
School: N.C. State
Final Exam - Fall 2005 - ST 370 Online/Distance - A Circle the answer. All questions are worth 3 points. 1. If the same number is added to every member of a sample, then the sample standard deviation will not change. A. True B. False 2. A is a numerical v
School: N.C. State
Solutions to Final Exam, ST 370 Online/Distance, Fall 2005 1. True: sd(c+X1,c+X2, ., c+Xn) = sd(X1,X2, ., Xn) 2. statistic: by definition, a statistic is calculated from the sample 3. The middle 50% of the data is spread out over 45.7 grams 4. 0.931 = [1-
School: N.C. State
Final Exam - Spring 2002 - ST 370 Online - A Darken the circle on the answer sheet corresponding to your answer. Use a number 2 pencil. Stray marks on the form may cause errors. All questions are worth 3 points. 1. An important distinction in the second h
School: N.C. State
Solutions to ST 370 Final Exam, Spring 2002 1. binomial 2. the point such that 67 percent of the data lies below it 3. All of the above (standard deviation,sample range,interquartile range) 4. .9251 5. 1.358 6. (ii) a small population mean difference and
School: N.C. State
Final Exam - Spring 2004 - ST 370 Online/Distance Darken the circle on the answer sheet corresponding to your answer. Use a number 2 pencil. Stray marks on the form may cause errors. All questions are worth 4 points. 1. Which of the following are measures
School: N.C. State
Solutions to Final Exam, ST 370 Online/Distance, Spring 2004 1. sample median and sample mean 2. Weibull (Poisson and binomial are important discrete r.v.'s) 3. Statistically significant but not practically significant means the mean difference must be sm
School: N.C. State
WEB-DISTANCE ST 370 Quiz 1 Autumn 2007 ver. A NAME_ ID # _ I will neither give nor receive help from other students during this quiz Sign _ PROBLEM 1: If the number 3 is added to every member of a sample of observations (as might happen if an ohm meter wa
School: N.C. State
fall 2007 quiz A white one given in Mann Hall 1. the sample standard deviation of the sample does not change 2. 9.8 3. 8.1 4. r = (b*sx)/sy from lesson 12 and hw 12 5. 3.70 6. population 7. strong evidence against the null hypothesis 8. ordinal 9. interce
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WEB-DISTANCE ST 370 Quiz 1 FALL 2007 ver. B NAME_ ID # _ I will neither give nor receive help from other students during this quiz Sign _ PROBLEM 1: If the number 3 is added to every member of a sample of observations (as might happen if an ohm meter was
School: N.C. State
Course: St Pr Clin Tri Epi
ST520, Fall 2012 Homework 2, due: Wednesday, 9/12/2012 1. (5 pts) Show the calculation on slide 64 of the probability that the trial will stop at the 3rd dose level given the true toxicity probabilities and the results at the rst 2 dose levels. 2. (20 pts
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Course: Stat Quality Prod
WrittenAssignment7 1. 8.1 A process is in statistical control with x =20 and s =12. Specifications are at LSL =16 and USL =24. (a) Estimate the process capability with an appropriate process capability ratio. Cp = =.1111 Cpk = = min(.11111, .11111)=.11111
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ST 370 HW 4 11/27/12 9:30 PM Emily Gaye ST 370, section 001, Fall 2012 Instructor: Renee Moore WebAssign ST 370 HW 4 (Homework) Current Score : 10 / 10 Due : Thursday, September 13 2012 11:59 PM EDT The due date for this assignment is past. Your work can
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ST 370 HW 5 11/27/12 9:31 PM Emily Gaye ST 370, section 001, Fall 2012 Instructor: Renee Moore WebAssign ST 370 HW 5 (Homework) Current Score : 10 / 10 Due : Thursday, September 20 2012 11:59 PM EDT The due date for this assignment is past. Your work can
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ST 370 HW 2 11/27/12 9:28 PM Emily Gaye ST 370, section 001, Fall 2012 Instructor: Renee Moore WebAssign ST 370 HW 2 (Homework) Current Score : 10 / 10 Due : Thursday, August 30 2012 11:59 PM EDT The due date for this assignment is past. Your work can be
School: N.C. State
ST 370 HW 3 11/27/12 9:29 PM WebAssign ST 370 HW 3 (Homework) Current Score : 9 / 10 Emily Gaye ST 370, section 001, Fall 2012 Instructor: Renee Moore Due : Thursday, September 6 2012 11:59 PM EDT The due date for this assignment is past. Your work can be
School: N.C. State
9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-002 Smith Section SPRING 2004 HW # 3 Due Tues Feb 17, 2004 In Devore unless noted otherwise. 1. Riddle sheet #31 passed out in class 2. text, p. 90, # 74 3. tex
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9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-002 Smith Section SPRING 2004 WEEK 1 Readings: Cartoon Guide Chapters 1 and 2 Devore: Chapter 1 URL for football data HW # 1 Due Tuesday Jan 27, 2004 1. journal
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9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-002 Smith Section SPRING 2004 READINGS: Devore: 4.1, 4.2, 4.3, 4.4,4.5 cartoon guide: chapters 4 and 5 HW # 5 Due Thursday March 18, 2004 ST PATRICK's DAY In De
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9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-002 Smith Section Spring 2004 HW # 2 Due Tuesday Feb 10,12 2004 In Devore unless noted otherwise. problems 1 to 7 passed out in class they are also at D H Hill
School: N.C. State
9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-001 Smith Section FALL 2001 HW # 8 Due Tuesday NOV 27, 2001 In Devore unless noted otherwise. 1. text, p.286 , # 5 2. text, p.303, # 35 3. text, p.306, # 42 4.
School: N.C. State
9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-001 Smith Section FALL 2002 HW # 7 Due Thursday Nov 14, 2002 In Devore unless noted otherwise. 1. text, p. 232 , # 41 2. text, p. 243 , # 65 3. text, p. 247 , #
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9/18/2014 www4.stat.ncsu.edu/~bmasmith/371S04/371finA.txt 1 a . 2 c . 3 b . 4 b . 5 a . 6 c . 7 d . 8 e . 9 b . 1.badd 0 n 1.c 1 1.b 2 1.a 3 1.d 4 1.d 5 1.a 6 1.d 7 1.c 8 1.c 9 2.d 0 2.c 1 2.b 2 2.a 3 2.c 4 2.d 5 2.c 6 2.e 7 2.a 8 2.a 9 3.b 0 3.c 1 3.a 2
School: N.C. State
9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-002 Smith Section SPRING 2004 HW # 4 Due Thursday Feb 26, 2004 1. Assume that for single lanches of a space shuttle, that there is a constant probability = 0.10
School: N.C. State
9/18/2014 HOMEWORK ASSIGNMENTS Smith Section ST 371 HOMEWORK and READING ASSIGNMENTS for ST371-002 Smith Section SPRING 2004 HW # 6A Due Thursday April 1, 2004 In Devore unless noted otherwise. 1. text, p.188 , # 75 2. text, p.188 , # 78 3. text, p.216, #
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 1 EXTRA PROBLEM SOLUTIONS, FALL 2009 1. (a) The loglikelihood is n log L = cfw_Yj log f (xj , ) f (xj , ) log Yj !. j=1 Taking derivatives with respect to and setting equal to zero gives the estimating equation n / log L = cfw_Yj f (xj ,
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 1 EXTRA PROBLEMS, FALL 2009 These problems are from previous years and are for you to work on or not as you choose; they are not to be turned in. You should be familiar with the concepts covered by these problems for the midterm test. Sol
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 5, FALL 2009 These problems are to be turned in on the due date. 1. Does how one estimates really matter? Consider the usual mean-variance model var(Yj |xj ) = 2 g2 (, , xj ), E(Yj |xj ) = f (xj , ), (1) where the (Yj , xj ), j = 1, . . .
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 6, FALL 2009 These problems are to be turned in on the due date. 1. Recall the data in Homework 5, Problem 3, from a clinical trial studying the eectiveness of a treatment for patients with respiratory illness. See that problem for a desc
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 5 SOLUTIONS, FALL 2009 1. You should have written one program that computes all of the estimators. It is not necessarily appropriate to have a separate program for each estimator; in order that the comparison be sound, all estimators must
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 6 SOLUTIONS, FALL 2009 1. (a) Here, with the subject-specic model (2), rather than modeling the probabilities of having good (Y = 1) respiratory status in the population of subjects over time on the two treatments, the probabilities that
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 4, FALL 2009 1. Do the folklore properties hold in nite samples? Consider our usual mean-variance model var(Yj |xj ) = 2 g2 (, , xj ), E(Yj |xj ) = f (xj , ), (1) where the (Yj , xj ), j = 1, . . . , n are independent, and suppose that bo
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 4 SOLUTIONS, FALL 2009 1. Results of my simulation with S = 1000 are as follows. SIMULATION RESULTS FROM 1000 MONTE CARLO DATA SETS beta1 OLS Bias = -1e-04 Rel Bias = 0 Rel Bias SD = -0.0288 Mean beta1, SD beta1, Mean estimated SE beta1,
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 3 SOLUTIONS, FALL 2009 1. (a) Here is the plot weve also superimposed the GLS-PL t, as requested in part (g), where a dierent line type is used for each vibration condition. 1 1 1 60 1 1 50 0 0 0 0 0 0 1 0 30 dissolution 40 1 20 1 0 0 1 1
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 3, FALL 2009 These problems are to be turned in on the due date. 1. Using standard nonlinear regression software to implement GLS and normal theory ML with estimation of . For drugs intended to be administered orally, such as solid tablet
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 2, FALL 2009 These two problems are to be turned in on the due date. 1. Using standard nonlinear regression software to implement GLS. The data in the le trees.dat, available on the class web page, were collected by forest science researc
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 1 SOLUTIONS, FALL 2009 1. We demonstate two ways to solve the system of ODEs. (a) First, we use the standard method of Laplace transforms. It is often the case that nding the solution of a complicated system of dierential equations may be
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 1, FALL 2009 These problems are to be turned in on the due date. 1. Many nonlinear (in parameters) functions used to describe biological and physical phenomena arise as the solution to a system of ordinary dierential equations (ODEs). Thi
School: N.C. State
Course: Nonlinear Models For Univariate And Multivariate Response
ST 762, HOMEWORK 2 SOLUTIONS, FALL 2009 1. (a) Here are plots of the data by site preparation treatment (with the ts for part (c) superimposed, too). 20 1 1 0 1 1 1 0 0 15 0 16 18 0 1 0 1 1 0 0 1 1 10 dominant height (m) 0 0 0 0 1 1 5 0 0 1 0 0 1 1 4 6 8
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 5, SPRING 2007 1. Recall the lead exposure study from Homework 3, Problem 3. Consider model (1) in the statement of that problem, which is repeated here for convenience: Let Yij denote the jth lead level measurement on the ith child at
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 5, SOLUTIONS, SPRING 2007 1. We obtain Yij = (0 + 0a ai + 0g gi + 0ag ai gi ) + (k + ka ai + kg gi + kag ai gi )tij + (b0i + b1i tij + eij ). Because all of b0i , b1i and eij have mean zero, is it straightforward to see that E(Yij ) = (
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 6, SOLUTIONS, SPRING 2007 1. (a) (i) For such a subject, tj = 1. The expected tumor response E(Yj ) for such a subject (which is the same as the probability that the subject develops a new tumor under these conditions, P (Yj = 1), is e0
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 3, SPRING 2007 1. A study was conducted in which m = 40 devices were randomized to be operated under 4 dierent sets of conditions, 10 devices per set of conditions. A response reecting performance level of such devices was measured on e
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 6, SPRING 2007 1. A study was conducted to compare two treatments for patients with bladder cancer. Each of the n = 100 subjects recruited into the study had recently had surgery to remove the tumor; at baseline, each was then randomize
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 4, SOLUTIONS, SPRING 2007 1. (a) Here, the dimension of i is (2 1), so Z i this matrix becomes 1 1 Zi = 1 1 1 is (ni 2) matrix in general. When ni = 5, ti1 ti2 ti3 ti4 ti5 , so that sweeping the jth row of Z i down i yields the expres
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 3, SOLUTIONS, SPRING 2007 1. (a) The model that seems reasonable to capture the possibility of curvature of the mean proles as in criterion (ii) is one that allows the mean as a function of time in each group to be quadratic in time. Fu
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 4, SPRING 2007 1. Consider a straight line model for individual behavior as in Equation (9.1) of the notes, which for unit i is of the form Yij = 0i + 1i tij + eij , (1) where Yij is the random variable representing the observation that
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 1, SPRING 2007 The rst few exercises are meant to familiarize you with some operations that we will summarize using matrix notation throughout the course. Use of SAS to carry out the analyses we will discuss requires familiarity with th
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 2, SOLUTIONS, SPRING 2007 1. (a) We have M = 11 21 31 41 12 22 32 42 13 23 33 43 . (b) We have a = (1, 0, 0, 0), so that a M = (11 , 12 , 13 ). i i (c) We have = 1 2 3 4 1 2 3 ( )11 ( )12 ( )13 ( )21 ( )22 ( )23 ( )31 ( )32 ( )33 ( )41
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 1, SOLUTIONS, SPRING 2007 1. (a) First, it is clear that, using the result at the top of p. 35, E(c1 Y1 + c2 Y2 ) = c1 1 + c2 2 . Thus, using (3.2), var(c1 Y1 + c2 Y2 ) = Ecfw_(c1 Y1 + c2 Y2 c1 1 c2 2 )2 . This may be rewritten as, usin
School: N.C. State
Course: Applied Longitudinal Data Analysis
. ST 732, HOMEWORK 2, SPRING 2007 1. Suppose that we have a situation in which units have been randomized into q = 4 groups and each unit is observed at the same n = 3 times. As in the notation in the notes, let j be the mean for the th group at the jth t
School: N.C. State
Course: Statistic Theory I
ST521-002 Homework #9 Solution Department of Statistics Nov. 02, 2009 4.15 (i) P (X|X + Y = t) = P (X = x, Y = t x) P (X + Y = t) = e x e tx x! (tx)! e(+) (+)t t! x tx = t! x!(t x)! ( + )t = t Cx Thus, X|X + Y Binomial with parameters n = X + Y and p =
School: N.C. State
Course: Statistic Theory I
ST521-002 Homework #7 Solution Department of Statistics Oct. 19, 2009 3.28 (a) f (x) = 1 2 2 e (x)2 2 2 = 1 2 2 e x2 +2x2 2 2 (i) known 1 1 Let h(x) = 1, c( 2 ) = 22 , w( 2 ) = 22 and t(x) = (x )2 Thus, when is known, normal family is an exponential fami
School: N.C. State
Course: Statistic Theory I
ST521-002 Homework #5 Solution Department of Statistics Sep. 28, 2009 2.30 (a) c MX (t) = 0 1 tx 1 e etx dx = c ct c 0 = 1 ct e 1, t = 0 ct (b) c etx MX (t) = 0 2x 2 1 1 dx = 2 x etx c2 c t t c c etx dx 0 = 0 2 c2 c c ct 1 e 2 etx t t c ct ect 1 e , t=0
School: N.C. State
Course: Statistic Theory I
ST521-002 Homework #3 Solution Department of Statistics Sep. 16, 2009 1.55 The distribution function of V can be derived as 0 3 1 P (T < 3) = 0 1.5 et/1.5 dt = 1 e2 P (V v) = P (2T v) = v/2 1 et/1.5 dt = 1 ev/3 1.5 0 if v < 5 if 5 v < 6 if 6 v Therefore
School: N.C. State
Course: Statistic Theory I
ST521-002 Homework #1 Solution Department of Statistics Aug. 31, 2009 1.1 (a) S = cfw_T T T T, HT T T, T HT T, T T HT, T T T H, HHT T, HT HT, HT T H, T HHT, T HT H, T T HH, HHHT, HHT H, HT HH, T HHH, HHHH (b) S = cfw_0, 1, 2, . . . (c) S = [0, ) (d) S = (
School: N.C. State
Course: Intro To Statistic
4/28/2014 ST 311-601 Homework 6 Spring 2014 WebAssign ST 311-601 Homework 6 Spring 2014 (Homework) Current Score : 11 / 30 C orey Ames ST 311, section 601, Spring 2014 Instructor: Thomas Reiland Due : Monday, March 3 2014 11:59 PM EST The due date for thi
School: N.C. State
Course: Intro To Statistic
4/28/2014 ST 311-601 Homework 8 Spring 2014 WebAssign ST 311-601 Homework 8 Spring 2014 (Homework) Current Score : 30 / 30 C orey Ames ST 311, section 601, Spring 2014 Instructor: Thomas Reiland Due : Monday, March 24 2014 11:59 PM EDT The due date for th
School: N.C. State
Course: Intro To Statistic
4/28/2014 ST 311-601 Homework 11 Spring 2014 WebAssign ST 311-601 Homework 11 Spring 2014 (Homework) Current Score : 21.5 / 30 C orey Ames ST 311, section 601, Spring 2014 Instructor: Thomas Reiland Due : Monday, April 21 2014 11:59 PM EDT The due date fo
School: N.C. State
Course: Intro To Statistic
4/28/2014 ST 311-601 Homework 10 Spring 2014 C orey Ames ST 311, section 601, Spring 2014 Instructor: Thomas Reiland WebAssign ST 311-601 Homework 10 Spring 2014 (Homework) Current Score : 21 / 30 Due : Tuesday, April 15 2014 11:59 PM EDT The due date for
School: N.C. State
Course: Intro To Statistic
4/28/2014 ST 311-601 Homework 7 Spring 2014 C orey Ames ST 311, section 601, Spring 2014 Instructor: Thomas Reiland WebAssign ST 311-601 Homework 7 Spring 2014 (Homework) Current Score : 17.5 / 30 Due : Monday, March 17 2014 11:59 PM EDT Adjustment: +1 Th
School: N.C. State
Course: Intro To Statistic
4/28/2014 ST 311-601 Homework 9 Spring 2014 WebAssign ST 311-601 Homework 9 Spring 2014 (Homework) Current Score : 27.5 / 30 C orey Ames ST 311, section 601, Spring 2014 Instructor: Thomas Reiland Due : Monday, April 7 2014 11:59 PM EDT The due date for t
School: N.C. State
Course: Intro To Statistic
4/28/2014 ST 311-601 Homework 2 Spring 2014 WebAssign ST 311-601 Homework 2 Spring 2014 (Homework) Current Score : 22.25 / 30 Due : Monday, January 27 2014 11:59 PM EST C orey Ames ST 311, section 601, Spring 2014 Instructor: Thomas Reiland Adjustment: +1
School: N.C. State
Course: Intro To Statistic
4/28/2014 ST 311-601 Homework 3 Spring 2014 WebAssign ST 311-601 Homework 3 Spring 2014 (Homework) Current Score : 4.75 / 30 C orey Ames ST 311, section 601, Spring 2014 Instructor: Thomas Reiland Due : Monday, February 3 2014 11:59 PM EST The due date fo
School: N.C. State
Course: Statistic Theory I
ST 521 LAB Solution #10 Prepared by Dong wang and Chen-Yen Lin FALL 2011 5.3 Since Y i =1 or 0 and they are independent and with the same probability p( y i 1 )= 1 F ( ) . Thus n y i is binomial distribution ( n, p 1 F ( ) ) i 1 5.6 a, let Z=X-Y, W=X, the
School: N.C. State
Course: Statistic Theory I
ST 521: Statistical Theory I Solution to Lab Exercise - 9 Prepared by Chen-Yen Lin and Dong Wang Fall, 2011 4.51 (a) P (X/Y t) = P (X tY ) = P (XY t) t 2 if t < 1 if t 1 = EI (XY t) 1 1 2t = EX EY |X I (Y < t/x)|X = EX P (Y t/x) t = EX I (0 < t/x < 1) + 1
School: N.C. State
Course: Statistic Theory I
ST 521 LAB Solution #8 Prepared by Dong wang and Chen-Yen Lin FALL 2011 4.27 Since X and Y are independent normal distribution, the linear combination of them is also normally distributed. By Theorem 4.2.14, U N ( ,2 2 ) , V N ( ,2 2 ) f X ,Y ( x, y ) f X
School: N.C. State
Course: Statistic Theory I
ST 521: Statistical Theory I Solution to Lab Exercise - 7 Prepared by Chen-Yen Lin and Dong Wang Fall, 2011 4.9 P (a X b, c Y d) = P (X b, Y d) P (X b, Y c) P (X a, Y d) + P (X a, Y c) = FX (b)FY (d) FX (b)FY (c) FX (a)FY (d) + FX (a)FY (c) = FX (b)[FY (d
School: N.C. State
Course: Statistic Theory I
ST 521 LAB Solution #6 Prepared by Dong wang and Chen-Yen Lin FALL 2011 3.21 f ( x) 1 1 M X (t ) 2 (1 x ) e tx 1 x 2 dx e tx x 1 x 2 dx 1 x 2 dx 0 0 Thus the moment generating function does not exist. If x is positive, we have e tx x 3.22 (a) E (
School: N.C. State
Course: Statistic Theory I
ST 521: Statistical Theory I Solution to Lab Exercise - 5 Prepared by Chen-Yen Lin and Dong Wang Fall, 2011 1 Let X Hyp(N, M, K ), P (X = x) = K EX = x x=0 K EX (X 1) = x(x x=0 M N M Cx CK x N CK K M N M Cx CK x = N CK x=1 M N M Cx CK x 1) N CK , M (M 1)
School: N.C. State
Course: Statistic Theory I
ST 521 LAB Solution #4 Prepared by Dong wang and Chen-Yen Lin FALL 2011 = l 1 k 0 l 0 0 yf X ( y )dy EX (b) k 0 k 0 (1 FX (k ) P( X k ) k 0 l k 1 P( X l ) l 0 P( X l ) lP( X l ) EX c. 1 ( x ) / tx 1 E (e ) e e dx e ( x ) / e tx dx 2 2 tx e / = 2 t =
School: N.C. State
Course: Statistic Theory I
ST 521: Statistical Theory I Solution to Lab Exercise - 3 Prepared by Chen-Yen Lin and Dong Wang Fall, 2011 2.8 (a) (i) FX (x) is continuous function, it must be right-continuous. (ii) x FX (x) = ex > 0, x (0, ) (iii) F () = 1 e = 1 and F () = 0 The inver
School: N.C. State
Course: Statistic Theory I
ST 521 LAB Solution #2 Prepared by Dong wang and Chen-Yen Lin FALL 2011 Note that the summation can be simplified as (1/4)^n * choose(2n, n) as choose(n, x)=choose(n, n-x). 1.26 P(more than five times to get the first 6) = 1 (1 / 6) (5 / 6)(1 / 6) . (5 /
School: N.C. State
Course: Statistic Theory I
ST 521: Statistical Theory I Solution to Lab Exercise - 1 Prepared by Chen-Yen Lin and Dong Wang Fall, 2011 1.2 (a) If A\B A and B . A and A B . A\(A B ). Similarly, if / / A\B A and B . A and B c . A B c / (b) (B A) (B Ac ) = B (A Ac ) = B S = B . (c) I
School: N.C. State
Course: Exp Stat Bio Sc II
ST512 Assignment 1 Due Date: Wednesday, September 9. Note the unusual due date. Labs will typically be due on Mondays. Assignment Goals: 1. Introduce R. 2. Fit a linear regression model in R. 3. Draw statistical inferences about regression parameters. 4.
School: N.C. State
Course: Exp Stat Bio Sc II
ST512 Assignment 1. KEY 1. What is the age of the youngest person in this data set? What is the age of the oldest person? Youngest: 18 years Oldest: 67 years 2. Use the simple linear regressions of weight vs. age and weight vs. height to complete the tabl
School: N.C. State
Course: Exp Stat Bio Sc II
ST512 Assignment 2 Due Date: September 14. Assignment Goal: Use R to conduct a computer experiment that investigates the consequences of violating an SLR assumption. We spend lots of time in statistics class emphasizing the assumptions on which different
School: N.C. State
Course: Exp Stat Bio Sc II
ST512. Lab 2 KEY. 1. Answers will vary. 2. 1.734 3. Answers will vary. Observed type I error rate when variances are equal should be approximately 5%. Observed type I error rate when variances are not equal should be around 710%. 4. In this case, unequal
School: N.C. State
Course: Exp Stat Bio Sc II
ST512 Assignment 3 Due Date: September 21. Assignment Goals: 1. Fit a multiple regression model in R. 2. Gain practice with indicator variables. This lab is based on Ch. 10 of the book StatLabs: Mathematical Statistics Through Applications, by D. Nolan an
School: N.C. State
Course: Exp Stat Bio Sc II
ST512 Assignment 3. KEY 1. Summarize the simple linear regression models of birth weight vs. mother's age, weight, height, and gestation period in the table below. Predictor Age Height Weight Gestation period Estimated slope 0.085 1.48 0.138 0.467 p-value
School: N.C. State
Course: Exp Stat Bio Sc II
ST512 Assignment 4 Due Date: September 28 Assignment Goals: 1. Calculate leverages in R. 2. Explore the effects of collinearity. 3. Practice using indicator variables. This lab is based on the article, "Using cigarette data for an introduction to multiple
School: N.C. State
Course: Exp Stat Bio Sc II
ST512 Assignment 4. Questions KEY 1. a. One data point has cosniderably higher tar, nicotine, weight, and CO than the others. (There is also a data point that has considerably lower tar, nicotine, weight, and CO. although this data point is not as aberran
School: N.C. State
Course: Exp Stat Bio Sc II
ST512 Laboratory Assignment 5 Due Date: October 19. Assignment Goals: 1. Introduce SAS 2. Introduce ANOVA techniques for comparing samples from more than two populations. The SAS system: SAS is state-of-the-art data analysis software. SAS was originally f
School: N.C. State
Course: Exp Stat Bio Sc II
ST512 Laboratory Assignment 5 KEY 1. In the table below, fill in the average heights, the standard error of the average heights, and the sample sizes for each voice. Voice Alto Bass Tenor Soprano Average height 64.9 in 70.7 69.2 64.3 Std. Error 0.5 0.4 0.
School: N.C. State
Course: Exp Stat Bio Sc II
ST512 Assignment 6 Due Date: October 26. Assignment Goal: Use PROC GLM to analyze data from a two-way factorial design. The data used in this lab are from a study reported by Pappas and Mitchell (Plant, Cell and Environment 1985) that examined two factors
School: N.C. State
Course: SPATIAL STATISTICS
DATASETS NEEDED: coalash.dt (Coal Ash dataset) www4.stat.ncsu.edu/~fuentes/coalash.dat davis.dat (topographic heights) www4.stat.ncsu.edu/~fuentes/davis.dat ozone.dat (ozone values) www4.stat.ncsu.edu/~fuentes/ozone.dat software R with library geoR,
School: N.C. State
Course: SPATIAL STATISTICS
Areal analysis in R Load Splancs Package Reading the rushes.dat dataset rushes<-read.table("rushes.txt") rushes$x<-rushes[,1] rushes$y<-rushes[,2] plot(as.points(rushes) poly1<- matrix(c(0,0, 0,1 ,1,1, 1,0),byrow=T, ncol=2) #defines domain EXERCISE
School: N.C. State
Course: STAT
ST 311 Su '09 PRACTICE PROBLEMS FOR FINAL EXAM Reiland Exam dates: Wednesday, August 5 - Friday, August 7 Material covered on exam: chapters 18 - 27 the topics in chapters 18 -27 are covered in webassign homework #5 through #8 Needed for exam: 8 " " 11" h
School: N.C. State
ST 370-003 Probability and Statistics for Engineers Fall 2013 Professor: Dr. Justin Post - jbpost2@ncsu.edu - (919) 515-0637 Meeting Place/Time: 124 Dabney T/Th 11:45 - 1:00 Course Goals: Office/Hours: Construct basic numeric and graphical summaries of da
School: N.C. State
Course: Intro To Statistic
Dhruv Sharma ST311 Sum-II ST311 Introduction to Statistics Section 001 Summer II 2007, NC State University Instructor: Dhruv Sharma Email: dbsharma@ncsu.edu Website: www4.ncsu.edu/~dbsharma/st311 Course Webpage: http:/courses.ncsu.edu/st311/lec/001
School: N.C. State
Course: Analy Surviv Data
ST 745001: Analysis of Survival Data Spring, 2005 Textbook Lecture notes 1. Survival Analysis: Techniques for Censored and Truncated Data (2nd edition) by John P. Klein and Melvin L. Moeschberger (the website http:/www.biostat.mcw.edu/homepgs/klei
School: N.C. State
ST 372 Spring 2007 Introduction to Statistical Inference and Regression Instructor: Email: Office: Phone: Office hour: Dr. Judy Huixia Wang wang@stat.ncsu.edu Patterson Hall Rm 209 F (919) 513-1661 Wednesdays, 3pm-5pm (or by appointment) Lectur