# Math 468 Syllabus

Class Times, Days, and Place: 9:05 am MWF SE 010
Text: The Statistical Sleuth by Ramsey and Schafer, 2nd edition
Professor: Elizabeth Housworth
Office: 371 Rawles Hall
Office Hours: Fridays, 10:10-11 am and by appointment
Office Phone: 855-1960
e-mail ehouswor@indiana.edu
Important Dates Last Day for Automatic Withdrawals: Wednesday, March 9, 2005

Course Description: This course is the second term of the applied statistics sequence. Statistics involves numbers in context. The numbers you analyze are numbers of something and the results should be described in terms of that thing. Thus, statistics is its own peculiar mixture of common sense, mathematics, and communication skills. This course emphasizes all of these aspects. The topics covered include time series, the analysis of multivariate responses including Hotelling's T-squared and MANOVA, principal components, factor analysis, clustering algorithms, discriminant analysis, and classification schemes.

Assignments: The sole basis for your grade in this course is your performance on the homework assignments. Most of the homework problems involve analyzing real datasets and writing brief reports summarizing your results. These analyses and reports should be taken seriously, done properly, and written well. When asked to summarize your findings on a homework problem, you should write a consultant's report that any lay person could understand, keeping statistical jargon to a minimum. Assignments should be typed into a word processor with graphics added as appropriate.

Software: We will use Minitab in class. You may use other software, but my demonstrations and instructions will be for Minitab. Minitab is simple, menu driven but with easy syntax for writing code when necessary, and cheap. You may purchase a year's license through the IU's Math Stat center for \$20 or a 5 month liscence directly through the Minitab corporation for \$25.99. It is a wonderful program and you might consider buying a permanent copy for only \$100.

### Tentative Syllabus

Date Topics Covered Homework Assignments
Monday, January 10 Time Series: theory and example Ch. 15: 12, 13, 15
due Monday, February 7
Wednesday, January 12 Time Series: the AR(1) model
Friday, January 14 Time Series: tests for serial correlation
Monday, January 17 Martin Luther King Holiday
Wednesday, January 19 Time Series: Other models
Friday, January 21 Time Series: model determination
Monday, January 24 Time Series: Comparing Models using BIC
Wednesday, January 26 Time Series: Discrete analog
Friday, January 28 Time Series: Higher order markov chains vs. lags
Monday, January 31 Time Series: summary Ch. 16: 13
due Monday, February 21
Wednesday, February 2 Multivariate Responses: theory and examples
Friday, February 4 Multivariate Responses: Bivariate Normal Distribution, eigenvalues, eigenvectors
Monday, February 7 Multivariate Responses: Hotelling's T2
Wednesday, February 9 Multivariate Responses: Hotelling's T2
Friday, February 11 Multivariate Responses: multipliers and confidence intervals
Monday, February 14 Multivariate Responses: Hotelling's T2 and sensitivity to assumptions
Wednesday, February 16 Multivariate Responses: Hotelling's T2 and checking model assumptions
Friday, February 18 Multivariate Responses: Hotelling's T2 and large sample sizes
Monday, February 21 Multivariate Responses: Summary, review, and One-way MANOVA theory Ch 16: 14, 15
due Monday, March 7
Wednesday, February 23 Multivariate Responses: One-way MANOVA theory and example
Friday, February 25 Multivariate Responses: One-way MANOVA, model checking, and pairwise comparisons
Monday, February 28 Multivariate Responses: Two-way and higher MANOVA
Wednesday, March 2 Multivariate Responses: Profile Analysis
Friday, March 4 Multivariate Multiple Regression - Including Covariates in MANOVA
Monday, March 7 Principal Component Analysis - theory Ch. 17: 13
due Monday, March 28
Wednesday, March 9 Principal Component Analysis - example
Friday, March 11 Principal Component Analysis - comparing variance-covariance structures
Monday, March 14 Spring Break
Wednesday, March 16 Spring Break
Friday, March 18 Spring Break
Monday, March 21 Factor Analysis -theory
Wednesday, March 23 Factor Analysis - example
Friday, March 25 Factor Analysis- rotations
Monday, March 28 Factor Analysis- PCA vs. MLE estimation Homework 5 problem
due Monday, April 11
Wednesday, March 30 Path Analysis and Structural Equation Modeling
Friday, April 1 Path Analysis and Structural Equation Modeling
Monday, April 4 Discrimination
Wednesday, April 6 Discrimination
Friday, April 8 Discrimination
Monday, April 11 Similarity Measures Homework 6 problems
due Monday, April 25
Friday, April 15 Hierarchical and K-means Clustering
Monday, April 18 Special Topics
Wednesday, April 20 Special Topics
Friday, April 22 Special Topics
Monday, April 25 Special Topics
Wednesday, April 27 Special Topics
Friday, April 29 Special Topics, Review and Evaluations