Instructor

Syllabus

Office Hours -- Spring 2007

Class Meeting Time

Current Textbook: Applied Linear Statistical Models, 5th edition, by Kutner, Nachtsheim, Neter and Li (4e: J. Neter, M.H. Kutner, C. Nachstheim, and W. Wasserman).

Course Description: 701—Applied Statistics II. (3) (Prereq: STAT 700 or consent of department) Continuation of STAT 700. Simple linear regression, correlation, multiple regression, fixed and random effects analysis of variance, analysis of covariance, experimental design, some multivariate methods, various statistical packages. Not to be used for M.S. or Ph.D. credit in statistics or mathematics.

Purpose: To expand the methodological abilities of future scientists in the experimental, social, and professional sciences beyond what is usually learned in a basic course.

 

Topics Covered	Chapters	Time
Review of simple linear regression and correlation	1.6-1.8, 2.1, 2.4, 2.6-2.10	1 week
Multiple linear and polynomial regression: model fitting, formal inferences	6.1-6.7, 6.9, 7.1-7.4, 7.6, 8.1-8.2 (4e:7.7-7.8)	3 weeks
Model building; Collinearity and influence; regression diagnostics	9.1, 9.4 (4e: 8.1, 8.4), 11.1 (4e: 10.1), 10.1-10.5 (4e: 9.1-9.5)	1 week
One-factor ANOVA, multiple comparisons, random effects models	16.1-16.8 (4e: 16.1-16.11), 25.1 (4e: 24.1), 17.1-17.7	1.5 weeks
Two-factor crossed ANOVA; fixed, random, and mixed models	19.1-19.7, 19.8-19.9 (4e: 20.1-20.3), 20.1-20.2 (4e: 21.1-21.2), 23.1-23.4 (4e: 22.1-22.4), 25.1-25.4 (4e: 24.1-24.4)	2.5 weeks
Randomized complete block designs, balanced incomplete block designs, Latin square	21.1-21.5 (4e: 27.1-27.5), 28.1-28.2 (4e: 27.8, 27.11, 27.13), 28.3-28.6 (4e: 30.1-30.5)	1.5 weeks
Analysis of Covariance	8.5 (4e: 11.2), 22.1-22.3 (4e: 25.1-25.3)	1 week
Nested Analysis of Variance	26.1-26.7 (4e: 28.1-28.7)	1.5 weeks

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

Computer Code for Class Examples

• SAS example: Simple Linear Regression (Toluca data)

• R example: Simple Linear Regression (Toluca data)

• SAS example: Multiple Linear Regression (studio data)

• R example: Multiple Linear Regression (studio data)

• Macro for Scatterplot Matrix in SAS

• SAS example: Extra SS Tests, Multicollinearity & Interaction Models (body fat data)

• R example: Extra SS Tests, Multicollinearity & Interaction Models (body fat data)

• SAS example: Polynomial Regression (cornmeal data)

• R example: Polynomial Regression (cornmeal data)

• SAS example: Model Selection (surgical unit data)

• R example: Model Selection (surgical unit data)

• SAS example: Diagnostics for Model Specification, Outliers and Influential Observations (life insurance data, body fat data, surgical unit data)

• R example: Diagnostics for Model Specification, Outliers and Influential Observations (life insurance data, body fat data, surgical unit data)

• SAS example: Single-Factor ANOVA (Kenton Foods data)

• R example: Single-Factor ANOVA (Kenton Foods data)

• SAS example: Two-Factor ANOVA (Castle Bakery data)

• R example: Two-Factor ANOVA (Castle Bakery data)

• SAS example: Two-Factor ANOVA (Interaction Present) (Melon data)

• SAS example: Two-Factor ANOVA (One Observation Per Treatment) (Insurance data)

• SAS example: Two-Factor ANOVA (Unequal Sample Sizes, i.e., Unbalanced Data) (Growth data)

• SAS example: Random Effects Model (Apex Enterprises data) and Mixed Effects Model (Training data)

• SAS example: Randomized Complete Block Design (Executive data)

• SAS example: Balanced Incomplete Block Design (Taste Test data)

• SAS example: Latin Square Design (Bank Teller data)

• SAS example: Analysis of Covariance (Cracker data)

• SAS example: Nested Designs (Training School data)

Data Sets

• Toluca Company Data Set (from Chapter 1)

• Portrait Studio Data Set (from Chapter 6)

• Grade Point Average Data Set

• Plastic Hardness Data Set

• Body Fat Data Set (from Chapter 7)

• Cornmeal Data Set

• Brand Preference Data Set

• Commercial Properties Data Set

• Surgical Unit Data Set (from Chapter 9)

• Job Proficiency Data Set

• Kenton Foods Data Set (from Chapter 16)

• 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)

• 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

• Sodium Content Data Set

• Imitation Pearls Data Set

• Executive Data Set (from Chapter 21)

• Taste Test Data Set (from Chapter 28)

• Bank Teller Data Set (from Chapter 28)

• Auditor Training Data Set

• Automobile Tire Wear Data Set

• Hardware Sales Data Set

• Cracker Data Set (from Chapter 22)

• Training School Data Set (from Chapter 26)

Homework

• Homework 1 (due Friday, Feb. 2)

• Homework 2 (due Friday, Feb. 16)

• Homework 3 (due Friday, March 2)

• Homework 4 (due Friday, March 23)

• Homework 5 (due Friday, April 13)

• Homework 6 (due Thursday, April 26 by 3 p.m.)

SAS Code for Homework

(Past students can email me for these files.)

• SAS Code for Homework 1

• SAS Code for Homework 2

• SAS Code for Homework 3

• SAS Code for Homework 4

• SAS Code for Homework 5

• SAS Code for Homework 6

Information about the Project

• STAT 701 Project Assignment, Spring 2007

Review Sheets

• Exam 1 Review Sheet

• Exam 2 Review Sheet

• Final Exam Review Sheet (use this plus first two review sheets)

Exam Solutions

• Exam 1 Example Solutions

Exams

• Midterm 1 (Wednesday, Feb. 21) (Note this change.)
• Midterm 2 (Wednesday, March 28)
• Final Exam (Friday, May 4, 2:00 p.m.)