Math 467 Syllabus

Class Times, Days, and Place: 9:05 am MWF PY 111
Text: The Statistical Sleuth by Ramsey and Schafer, 2nd edition
Professor: Elizabeth Housworth
Office: 371 Rawles Hall
Office Hours: Tuesdays 9-11 am and by appointment
Office Phone: 855-1960
Important Dates Last Day for Automatic Withdrawals: Wednesday, October 27, 2004

Course Description: This course is a second course in applied statistics. 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 simple and multiple regression, analysis of variance with fixed and random effects, multiple comparison procedures, model selection and verification procedures, categorical data analysis, odds ratios, and prediction of binary responses.

Assignments: The sole basis for your grade in this course is your performance on the homework assignments. Each student's lowest assigment grade will be dropped from the final average. 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. Example acceptable write-up of an exercise. Assignments should be typed into a word processor with graphics added as appropriate.

Students in the past have commented that they do not understand what I expect from these assignments and how I assign grades to their answers. These comments reflect the difficulty I have in assigning grades. Your write-ups need to be clear, grammatical, concise, and understandable (in English not in statistical jargon). Your statistics need to be appropriate to the problem. Some techniques may be better than others, some techniques may not be applicable, but, often, there is more than one good technique to address any given problem. Example: If your dataset consists of all 0's, 1's, and 2's, techniques that rely on rankings are unlikely to be applicable and you should be suspicious of using continuous-value based normal theory statistics such as t-tests but categorical and bootstrapping techniques may work well. Finally, you need to use common sense. Example: If the dataset contains a 100 lb cicada, you should figure that out, report it as a data entry error, and remove it from the data set (or correct it) before conducting your analysis. ALL of these aspects are critical to good applied statistics and should be taken into account when assigning grades. I am going to lump common sense in with the statistical analysis and assign separate "writing" and "mathematics" grades to your assignments. The "writing" part will count 40% and the "mathematics" part will count 60% towards the total assignment grade.

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, August 30 Ch. 1: Inferences and p-values Ch. 1: 16, 17; Ch. 2: 22, 23
due September 8
Wednesday, September 1 Ch. 1: Code for bootstrapping a p-value
Friday, September 3 Ch. 2: Review of t-tests
Monday, September 6 Ch. 3: Review and assumptions of t-tests Ch. 3: 33, form a display like 3.5 in your text
using the t-test without pooling variances;
Ch. 4: 31
due September 15
Wednesday, September 8 Ch. 4: Log Transformations
Friday, September 10 Ch. 4: Alternatives to t-tests - nonparametric approach
Monday, September 13 Ch. 4: Alternatives to t-tests - bootstrapping Ch. 4: 32; Ch. 5: 17, 21, 23
due September 29
Wednesday, September 15 Ch. 4: Paired tests
Friday, September 17 Ch. 4: Guide to t-tests
Monday, September 20 Ch. 5: ANOVA - The Theory Ch. 6: 12, 21, 22
due October 6
Wednesday, September 22 Ch. 5: ANOVA -In Practice
Friday, September 24 Ch. 5: NonParametric ANOVA
Monday, September 27 Ch. 5-6: Linear Contrasts and Random Effects Ch. 7: 23, 26, 29
due October 13
Wednesday, September 29 Ch. 6: Multiple Comparisons
Friday, October 1 Ch. 7: Simple Linear Regression
Monday, October 4 Ch. 7: Simple Linear Regression Ch. 8: 20 Corrected Data Set , 22, 24
due October 20
Wednesday, October 6 Ch. 7/8: Regression Diagnostics
Friday, October 8 Ch. 8: Regression Diagnostics
Monday, October 11 Ch. 8: Regression Diagnostics
Wednesday, October 13 Ch. 9: Multiple Regression
Friday, October 15 Ch. 9: Multiple Regression
Monday, October 18 Ch. 10: Multiple Regression Ch. 9: 17, 20; Ch 10: 27
due November 3
Wednesday, October 20 Ch. 10: Multiple Regression
Friday, October 22 Ch. 10: Multiple Regression
Monday, October 25 Ch. 11: Model checking Ch. 10: 29; Ch 11: 22, 24; Ch 12: 20
due November 10
Wednesday, October 27 Ch. 12: Variable Selection
Friday, October 29 Ch. 12: Variable Selection
Monday, November 1 Ch. 13: Two-Way ANOVA
Wednesday, November 3 Ch. 13: Two-Way ANOVA
Friday, November 5 Ch. 13: Two-Way ANOVA
Monday, November 8 Ch. 13: Two-Way ANOVA Ch. 13: 19
Ch. 14: 14, 15, 16
due November 17
Wednesday, November 10 Ch. 14: ANOVA without replication
Friday, November 12 Extra: Random Effects and Experimental Design
Monday, November 15 Ch. 18: Proportions and Odds Ch. 18: 16, 18; Ch. 19: 6, 17, 19
due November 29
Wednesday, November 17 Ch. 18: Proportions and Odds
Friday, November 19 Ch. 19: Chi-Square, Fisher's Exact Test, Mantel-Haenszel
Monday, November 22 Ch. 19: Chi-Square, Fisher's Exact Test, Mantel-Haenszel See above homework
Wednesday, November 24 Thanksgiving Recess
Friday, November 26 Thanksgiving Recess
Monday, November 29 Ch. 20: Logistic Regression Ch. 20: 15, 16, 18
due December 8
Wednesday, December 1 Ch. 20: Logistic Regression
Friday, December 3 Ch. 21: Logistic Regression
Monday, December 6 Ch. 21: Logistic Regression Ch. 21: 15, 19
due December 13
Wednesday, December 8 Ch. 21: Logistic Regression
Friday, December 10 Review

This page updated on August 8, 2004 by Elizabeth Housworth
Department of Mathematics
Department of Biology
Indiana University
Bloomington, IN 47405
Phone: 812-855-1960
Email: < >