Course Prerequisites: EXST 2201 or equivalent.

Course Objectives:
General objectives include the following:

• Provide the basic tools and building blocks for performing data analysis using analysis of variance and multiple regression methods
• Provide a foundation for further study in statistics including both theory and methods
• Stimulate a general interest in and an appreciation for statistics and its application

• Perform simple and multiple regression analysis, interpret basic and standard regression output, and perform basic model diagnostics
• Conduct basic analyses and diagnostics of and interpret the results from analyses of completely randomized designs, randomized complete block designs, split-plot designs, and basic repeated measures designs
• Perform and interpret the results of a basic analysis of covariance and multi-source regression
• Perform basic ANOVA-like and regression analysis of discrete data
• Understand descriptions of statistical analyses in subject-area journals when ANOVA, multiple regression, analysis of covariance, or simple discrete data models are applied
• Consult with a statistician on designing experiments and analyzing data

Instructor Office Hours: By appointment (schedule with e-mail when possible).

Required Text: Ramsey, F.L., and D.W. Schafer. 2002. The Statistical Sleuth, A Course in Methods of Data Analysis. Duxbury Press, Belmont, CA.

Assignments and Grading: Grades will be based upon 3 regular exams, 2 laboratory practica (midterm and final), a lab grade based upon periodic homeworks, a data analysis project, and a final exam. You must take the exams and practica and turn in homework when scheduled unless you have received prior approval for alternative arrangements from the instructor, as appropriate. A grade of "incomplete" can not be given for unsatisfactory work or for failure to take exams or turn in homework when scheduled. Each exam, practicum, project, and the overall homework lab grade will be reported on a 100% basis. The Final Course Grade will then be computed by averaging the scores over each of the items. Final letter grades will be assigned using the scale below. The instructor reserves the right to modify this scale by extending the lower limits on the minimum score required for a grade.

Grade Total Score
----- -----------
  A      >=90 
  B     80-89 
  C     70-79 
  D     60-69
  F       <60

This Section Is Still Incomplete and Under Development

I. Review and Preparation

1. Introduction to the SAS System. Basic t tests and analysis of variance. [1-3]
2. Linear combinations of random variables, expected values, variance, covariance, and correlation

II.

III. Regression

1. Simple linear regression and correlation (review) []
2. Curvilinear and polynomial regression, multiple regression analysis []
3. Multisource regression and dummy variables, general linear hypothesis test []
4. Variable selection, regression diagnostics []
5. Logistic regression on binary responses

IV. Experimental Design

1. Completely randomized design, factorial treatment arrangements []
2. Contrasts and multiple comparisons procedures []
3. Randomized complete block design, blocks as random effects, other blocked designs: latin square design and extensions []

V. Analysis of Categorical (Count) Data

1. Logistic analysis with categorical predictors []
2. Conditional independence and three-way contingency tables with the log-linear model