Syllabus

Instructor

Spring 2009 Office Hours:

Class Meeting Time

Textbook:
Required Textbook: "Applied Linear Statistical Models" (5th edition) by Kutner, Nachtsheim, Neter, and Li.
(This is the same book used for STAT 704 in the fall.)

Supplementary Books (NOT required):
"Linear Models with R" by Faraway, J.J.
"Extending the Linear Model with R" by Faraway, J.J.
"An R and S-plus Companion to Applied Regression" by Fox, J.
"Statistical Analysis and Data Display" by Heiberger and Holland.
"Statistical Research Methods in the Life Sciences" by Rao, P.V.

Computer Code for Class Examples

Statistical Topic	Example in SAS Code	Example in R Code
Regression with Qualitative Predictors	SAS example: (Insurance Innovation data & Shirt data)	R example: (Insurance Innovation data & Shirt data)
Single-Factor ANOVA	SAS example: (Kenton Foods data)	R example: (Kenton Foods data)
Regression Approach to ANOVA model	SAS example: (Kenton Foods data)	R example: (Kenton Foods data)
Investigation of Treatment Means	SAS example: (Kenton Foods data)	R example: (Kenton Foods data)
Checking ANOVA Model Assumptions	SAS example: (Kenton Foods data)	R example: (Kenton Foods data)
Two-Factor ANOVA	SAS example: (Castle Bakery data)	R example: (Castle Bakery data)
Investigation of Factor Effects (No Interaction)	SAS example: (Castle Bakery data)	R example: (Castle Bakery data)
Investigation of Factor Effects (With Interaction)	SAS example: (Melon data)	R example: (Melon data)
Two-Factor ANOVA With One Observation per Cell	SAS example: (Insurance data)	R example: (Insurance data)
Two-Factor ANOVA With Unequal Cell Sample Sizes	SAS example: (Growth data) (SAS example includes empty-cell analysis)	R example: (Growth data)
Three-Factor ANOVA	SAS example: (stress data)	R example: (stress data)
Random-Effects Model	SAS example: (Apex Enterprises data)	R example: (Apex Enterprises data)
Mixed-Effects Model	SAS example: (training data)	R example: (training data)
Randomized Complete Block Design	SAS example: (executive data)	R example: (executive data)
Generalized Random Block Design	SAS example: (task completion data)	R example: (task completion data)
Balanced Incomplete Block Design	SAS example: (taste test data)	R example: (taste test data)
Latin Square Design	SAS example: (bank teller data)	R example: (bank teller data)
Single-Factor Repeated Measures	SAS example: (wine judging data)	R example: (wine judging data)
Distribution-free Tests in ANOVA (Kruskal-Wallis Test & multiple comparisons)	SAS example: (soil data)	R example: (soil data)
Distribution-free Tests for RCBD (Friedman Test)	SAS example: (wind speed data)	R example: (wind speed data)
Analysis of Covariance	SAS example: (cracker data)	R example: (cracker data)
Piecewise Regression	SAS example: (raw materials data)	R example: (raw materials data)
Nested Design	SAS example: (training school data)	R example: (training school data)
1 × 2 Contingency Tables (Inference about binomial probability) (z-test/CI and Binomial test)	SAS example: (driver's test data)	R example: (driver's test data)
1 × c Contingency Tables (Inference about multinomial probabilities) (Chi-square goodness-of-fit test)	SAS example: (blood types data)	R example: (blood types data)
2 × 2 Contingency Tables (Comparing 2 proportions, independent samples) (z-test/CI and Fisher's Exact test)	SAS example: (trees data)	R example: (trees data)
2 × 2 Contingency Tables (Comparing 2 proportions, paired samples) (McNemar's test)	SAS example: (ice cream data)	R example: (ice cream data)
r × c Contingency Tables (Chi-square test for independence & Chi-square test for homogeneity)	SAS example: (snoring data & voter data)	R example: (snoring data & voter data)

Available Computing Resources: R is available as a free download from the CRAN home page) and students who want SAS can buy a copy from USC Computer Services.
These packages are also available on the computers in the labs in LeConte College (and a few other buildings). Help in using R can be found on the CRAN home page.

Downloading Instructions for R

• Instructions for Downloading R

Computing Tips: Some Review

• Introduction to SAS which should be a good instruction for the software

Homework

• Homework 1 (due Tuesday, January 20)

• Homework 2 (due Thursday, January 29)

• Homework 3 (due Tuesday, February 10)

• Homework 4 (due Friday, February 27, by 3 p.m.)

• Homework 5 (due Thursday, March 19)

• Homework 6 (due Tuesday, April 14)

• Homework 7 (due Friday, April 24, by 4 p.m.)

Data Sets

• Insurance Innovation Data Set (from Chapter 8)

• Shirt Data Set

• Kenton Foods Data Set (from Chapter 16)

• Copier Maintenance Data Set

• Questionnaire Color Data Set

• Rehabilitation Therapy Data Set

• Castle Bakery Data Set (from Chapter 19)

• Melon Data Set

• Insurance Data Set (from Chapter 20)

• Growth Data Set (from Chapter 23)

• Stress Data Set (from Chapter 24)

• Apex Enterprises Data Set (from Chapter 25)

• Training Data Set (not the same data as in the book's example!)

• Eye Contact Data Set

• Programmer Requirements Data Set

• Coin-operated Terminals Data Set

• Adjunct Professors Data Set

• Case Hardening Data Set

• Sodium Content Data Set

• Imitation Pearls Data Set

• Executive Data Set (from Chapter 21)

• Task Completion Data Set (from Chapter 21)

• Auditor Training Data Set

• Auditor Training (Problem #21.13) Data Set

• Automobile Tire Wear Data Set

• Hardware Sales Data Set

• Taste Test Data Set (from Chapter 28)

• Bank Teller Data Set (from Chapter 28)

• Wine Judging Data Set (from Chapter 27)

• Cracker Data Set (from Chapter 22)

• Training School Data Set (from Chapter 26)

• Questionnaire Color #22.9 Data Set

• Telephone Communications Data Set

• Bottling Plant Production Data Set

• Muscle Mass Data Set

Review Sheets

• Review Sheet for Test 1

• Review Sheet for Test 2

• Review Sheet for Post-Test 2 material

Formula Sheets

• Formula Sheet for Test 1 (this will be provided with the exam)

• Formula Sheet for Test 2 (this will be provided with the exam)

Example Solutions

• Selected Example Solutions, Test 1

• Example Solutions, Final Exam

Information about the Project

• Information about the Project, Spring 2009

• Project Grading Guidelines

Exams

• Exam 1: Tuesday, Feb. 17
• Exam 2: Thursday, March 26
• Final Exam: Thursday, April 30, 2:00 p.m.