Schedule
 Schedule of Classes
 Revised 7/7/07
 
 
Class #1   August 31
    Topic(s)   Introduction to the course
Review of basic statistics
Introduction to regression
             
     

Reading(s)/
Assignment(s)

 •   READ: Chapter 1 in Applied Linear Regression Models "Linear Regression with One Predictor Variable"
      •   REVIEW: Appendix A: in Applied Linear Regression Models
        •   READ: When Others Retire Early: What About Me?
Mollica, Kelly A.; DeWitt, Rocki-Lee Academy of Management Journal, Dec2000, Vol. 43 Issue 6, p1068-1075 (available through Oncourse; see "Resources")
             
Class #2   September 7
    Topic(s)   Overview of the SAS system
Inference in Regression
             
   

Reading(s)/
Assignment(s)

 •   Readings in The Little SAS Book
· Chapter 1 Getting Started Using SAS Software
· Chapter 2 Getting Your Data into SAS
· Chapter 3 Working with Your Data
· Chapter 4 Sorting, Printing, and Summarizing Your Data
        •   READ: Chapter 2 in Applied Linear Regression Models: "Inferences in Regression and Correlation Analysis" pages 40 - 62
        •   "What makes a perfect parent?"Chapter 5 of Freakonomics: A Rogue Economist Explores the Hidden Side of Everything (handout; see discussion questions in Resources)
        •  

Homework #1 due
Problems are in Kutner, Nachtsheim, and Neter:
For discussion 
  p. 33 1.8
  p. 34 1.10; 1.12;1.18
  p. 37 1.29; 1.30
To hand in:
  p. 35 1.19; 1.21
  p. 36 1.24; 1.27;
  p. 37 1.39

             
Class #3   September 14
    Topic(s)   Further topics related to SAS
Diagnostics and Remedial Measures
             
    Reading(s)/
Assignment(s)		 •   Readings in The Little SAS Book
· Chapter 5 Enhancing Your Output with ODS
· Chapter 6 Modifying and Combining SAS Data Sets
· Chapter 7 Writing Flexible Code with the SAS Macro Facility
· Chapter 9 Exporting Your Data
        •   READ: Chapter 3 in Applied Linear Regression Models: "Diagnostics and Remedial Measures"
             
Class #4   September 21
    Topic(s)   Simultaneous Inference in Regression
Review of matrix algebra
Matrix Approach to Regression
             
   

Reading(s)/
Assignment(s)

 •  

Homework #2 due
p. 91 Problem 2.13 (a, b, and c; use SAS for the regression)
p. 93 Problem 2.26 (a, b, c, d; use SAS for the regression)
p. 146 Problem 3.3 (all parts; use SAS for the regressions; part e must be done by hand or using Excel)
P. 150 Problem 3.17 (all parts; use Excel do perform the transformations and the regressions)

PLUS: US News college data analysis

 
    •   Project assignment 1
      •   READ: Chapter 4 in Applied Linear Regression Models: "Simultaneous Inferences and Other Topics in Regression Analysis"
    •   READ: Chapter 5 in Applied Linear Regression Models: "Matrix Approach to Simple Linear Regression Analysis"
             
Class #5   September 28
    Topic(s)   Multiple regression I
             
   

Reading(s)/
Assignment(s)

 •   READ: Chapter 6 in Applied Linear Regression Models: "Multiple Regression I"
      •   READ: Chapter 7 in Applied Linear Regression Models: "Multiple Regression II"
    •  

Homework #3 for next week
You may use Excel or SAS for the following or do the work by hand:
1. Problem 5.1 on page 209
2. Problem 5.2 on page 209
3. Problem 5.4 on page 210
4. Problem 5.11 on page 211. Check the inverse for accuracy.

Use SAS for the regression work and Excel for graphs in the following:
5. Problem 6.9 on page 249. In part a, develop box plots rather than stem-and-leaf plots.
6. Problem 6.10 on page 249.
7. Problem 6.11 on page 250.
8. Problem 6.14 on page 250.  
             
Class #6   October 5
    Topic(s)   Multiple regression II
             
   

Reading(s)/
Assignment(s)

 •   READ: Chapter 7 in Applied Linear Regression Models: "Multiple Regression II"
      •   READ: Chapter 8 in Applied Linear Regression Models: "Regression Models for Quantitative and Qualitative Predictors"
        •   READ: Chapter 9 in Applied Linear Regression Models: "Building the Regression Model I: Model Selection and Validation"
           
             
Class #7   October 12
            Mid-term Exam (in-class)
             
Class #8   October 19
           
            Project assignment 2
    Topic(s)   Dealing with assumptions
            Autocorrelation
   

Reading(s)/
Assignment(s)

 •   READ: Chapter 10 in Applied Linear Regression Models: "Building the Regression Model II: Diagnostics"
    •   READ: Chapter 11 in Applied Linear Regression Models: "Building the Regression Model III: Remedial Measures"
    •   READ: Chapter 12 in Applied Linear Regression Models: "Autocorrelation in Time Series Data"
        Homework #4 due
Class #9   October 26
    Topic(s)   Introduction to experimentation and ANOVA
             
   

Reading(s)/
Assignment(s)

 •   Readings in Keppel and Wickens:
· Chapter 1 Experimental Design
· Chapter 2 Sources of Variability and Sums of Squares
· Chapter 3 Variance Estimates and F Ratio
      •   Homework #6 due
             
Class #10   November 2
    Topic(s)   Effect size and power issues
             
   

Reading(s)/
Assignment(s)

 •   Readings in Keppel and Wickens:
· Chapter 7 The Linear Model and Its Assumptions
· Chapter 8 Effect Size and Power
· Chapter 9 Using Statistical Software
      •   Homework #7 due
             
Class #11   November 9
    Topic(s)   Factorial Experiments
             
   

Reading(s)/
Assignment(s)

 •   Readings in Keppel and Wickens:
· Chapter 10 Introduction to the Factorial Design
· Chapter 11 The Principal Two-Factor Effects
· Chapter 12 Main Effects and Simple Effects
      •   Homework #8 due
             
Class #12   November 16
    Topic(s)   The general linear model
             
   

Reading(s)/
Assignment(s)

 •   Readings in Keppel and Wickens:
· Chapter 13 The Analysis of Interaction Components
· Chapter 14 General Linear Model
· Chapter 15 The Analysis of Covariance
        •   Homework #9 due
             
Class #13   November 30
        Project presentations - I
             
Class #14   December 7
        Project presentations - II
             
Class #15   December 14
        Final exam: 8:00 - 10:00 am