Statistics 342 - Spring 2008

Course Syllabus

Homework Assignments

• Homework Assignment #1 - Answers
• Homework Assignment #2 - Answers
• Homework Assignment #3 - Answers
• Homework Assignment #4 - Answers
• Homework Assignment #5 - Answers
• Homework Assignment #6 - Answers
• Homework Assignment #7 - Answers
• Homework Assignment #8 - Answers
• Homework Assignment #9 - Answers

Data Sets:

• M&M Weights 1
• M&M Weights 2
• Net Weight and Number
• Baby and Mother Weights
• Large Study Data
• SATS Pre-Value Data
• Butter Clams Data

Course Project

• Project Guidelines

Exams

• Exam #1
  • Take Home Exam #1 - Due Friday, February 29th
  • In-Class Exam #1 - Answers
  • Take Home Exam #1 - Explanation of Grading
  • Class Results - Exam #1 - Spring 2008
• Exam #2
  • Take Home Exam #2 - Due Friday, April 11th
  • In-Class Exam #2 - Answers
• Final Exam - Thursday May 8 9:45 a.m. - 11:45 a.m.
  • Take Home Final Exam
  
  

  Lecture Notes

  
  Unit #1 - Transformations and Sampling Distributions.
  • Introduction
  • Transformations of One Random Variable
  • Generating Data from a Distribution
  • Sums of Random Variables
  • Ordered Statistics
  • Means from Normal Distributions (Normal Model Theory)
  • Central Limit Theorem
  Unit #2 - Inference
  • Introduction to Inference (Confidence Interval and Hypothesis Test for Normal Models)
  • Method of Moments Estimators
  • Maximum Likelihood Estimators
  • Properties of Estimators
  • Properties of MOM and MLE for Gamma Distribution Parameters
  • Properties of MOM and MLE for Binomial Distribution Parameter
  • Properties of MOM and MLE for Exponential Parameter
  • Hypothesis Tests and Confidence Intervals for Exponential Parameter
  • Hypothesis Tests and Confidence Intervals for Binomial Parameter
  • General Properties of Estimators
  • The Neyman-Pearson Lemma and Most Powerful Tests
  Unit #3 - From Statistical Theory to Practice and Back
  • Inference for One-Sample Means
  • Inference for Two Independent Sample Means
  • Inference for Contingency Tables
  • Linear Regression
  • Multiple Regression
  
  

  Distribution Handouts from Stat 341:

  • Summary Sheets:
    • Discrete Distributions
    • Continuous Distributions
  • Individual Distributions:
    • Binomial Distributions
    • Geometric Distributions
    • Negative Binomial Distributions
    • Hypergeometric Distributions
    • Poisson Distributions
    • Uniform Distributions
    • Normal Distributions
    • Gamma Distributions
    • Special Gamma Distributions
    • Beta Distributions
  
  

  Handouts from Stat 342:

  • t Distribution
  • Hypothesis Test for mu from Normal Distribution
  
  

  Information about R:

  We will be using R pretty extensively in this course. If you have a personal computer, you can download R free of charge. For instructions on getting R onto your own computer, click on the link below. The Department of Statistics also maintains two on-campus computer labs where R is installed: Pearson 0113 and Carver 205. Hours for Pearson 0113 are from 8am - 5pm M-F and the hours for Carver 205 are from 7am - 8pm M-F.
  • Getting Started with R in Stat 342 - Includes information about how to download the program and basic saving and printing information.
  • Introduction to Transformations of One Random Variable - R code is colored red.
  • Example of Transformations of One Random Variable - From Lecture on 1/18/08. - R code is colored red.
  • Examples of Generating Data Using R - From Lecture on 1/23/08.
  • Introduction to Generating Sums of Independent Random Variables in R - From Lecture on 1/28/08.
  • Introduction to Ordered Statistics Using R - From Lecture on 2/1/08.
  • Introduction to Normal Model Theory Using R - From Lecture on 2/6/08 to 2/11/08.
  • An Example of the Central Limit Theorem Using R - From Lecture on 2/15/08.
  • The Power of Hypothesis Tests
  • The Coverage Rate for Confidence Intervals
  • Functions in R for Confidence Intervals and Hypothesis Tests for Mean Value from Normal Distribution
  • The Method of Moments Estimators for the Gamma Distribution Parameters
  • The Maximum Likelihood Estimators for the Gamma Distribution Parameters
  • Studying the MOM and MLE estimators for Uniform Distribution
  • Studying the MOM and MLE estimators for Gamma Distribution
  • Functions in R for Hypothesis Tests and Confidence Intervals for Exponential Distribution Parameter
  • Functions in R for Hypothesis Tests and Confidence Intervals for Binomial Distribution Parameter
  • Functions in R to Obtain the Estimated Coverage Rate of the Traditional and Plus-4 Method Confidence Intervals for Binomial Distribution Parameter
  • Functions in R for Inference for the One-Sample Mean Parameter
  • Functions in R for Inference for Two-Sample Means
  • Functions in R for Inference for Contingency Tables
  • Functions in R for Linear Regression