Math 467 Syllabus

Class Times, Days, and Place: 9:05 am MWF SW 220
Text: The Statistical Sleuth by Ramsey and Schafer, 2nd edition
Text website with datasets:
Course website:
Professor: Elizabeth Housworth
Office: 371 Rawles Hall
Office Hours: Tuesdays, Wednesdays, and Sundays 12-1
Office Phone: 855-1960
Important Dates Last Day for Automatic Withdrawals: Wednesday, October 25, 2006

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 2 assignment grades will be dropped from the final average - this should cover reasonable illnesses and excused absences. 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.

Another comment that students have made is that the text is not an appropriate reference text for their research. This complaint is valid. The text and the software I choose to use are chosen for teaching purposes. The statistics text you use as a reference for your research and the software you need to use for your research may well vary from that used in class.

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 $29.99. 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 28 Ch. 1: Inferences and p-values Ch. 1: 17 (no essay required); Ch. 2: 22, 23 Corrected Data Set
due September 6
Wednesday, August 30 Ch. 1: Code for bootstrapping a p-value
Friday, September 1 Ch. 2: Review of t-tests
Monday, September 4 Ch. 3: Review and assumptions of t-tests Ch. 3: 33
Additional (non-essay) problem: form a display like 3.5 in your text using Welch's t-test (without pooling variances)
due September 13
Wednesday, September 6 Ch. 4: Log Transformations
Friday, September 8 Ch. 4: Alternatives to t-tests - nonparametric approach
Monday, September 11 Ch. 4: Alternatives to t-tests - bootstrapping Ch. 4: 31, 32
due September 20
Wednesday, September 13 Ch. 4: Paired tests
Friday, September 15 Ch. 4: Guide to t-tests
Monday, September 18 Ch. 5: ANOVA - The Theory Ch. 5: 21 (no essay required), 23
due September 27
Wednesday, September 20 Ch. 5: ANOVA -In Practice
Friday, September 22 Ch. 5: NonParametric ANOVA
Monday, September 25 Ch. 5-6: Linear Contrasts and Random Effects Ch. 6: 22
due October 4
Wednesday, September 27 Ch. 6: Multiple Comparisons
Friday, September 29 Ch. 7: Simple Linear Regression
Monday, October 2 Ch. 7: Simple Linear Regression Ch. 7: 26 (no essay required), 29
due October 11
Wednesday, October 4 Ch. 7/8: Regression Diagnostics
Friday, October 6 Ch. 8: Regression Diagnostics
Monday, October 9 Ch. 8: Regression Diagnostics Ch. 8: 20 Corrected Data Set , 24 (provide a truly useful plot or chart - minimize the effort required on he physician's part)
due October 18
Wednesday, October 11 Ch. 9: Multiple Regression
Friday, October 13 Ch. 9: Multiple Regression
Monday, October 16 Ch. 10: Multiple Regression Ch. 9: 20; Ch 10: 29
due October 25
Wednesday, October 18 Ch. 10: Multiple Regression
Friday, October 20 Ch. 10: Multiple Regression
Monday, October 23 Ch. 11: Model checking Ch 11: 24 (find the common names of the species and identify obvious data-entry errors); Ch 12: 21
due November 1
Wednesday, October 25 Ch. 12: Variable Selection
Friday, October 27 Ch. 12: Variable Selection
Monday, October 30 Ch. 13: Two-Way ANOVA Ch. 13: 19
due November 8
Wednesday, November 1 Ch. 13: Two-Way ANOVA
Friday, November 3 Ch. 13: Two-Way ANOVA
Monday, November 6 Ch. 13: Two-Way ANOVA Ch. 14: 14, 15, 16 (these are related projects and can be written up into one extended essay)
due November 15
Wednesday, November 8 Ch. 14: ANOVA without replication
Friday, November 10 Extra: Random Effects and Experimental Design
Monday, November 13 Ch. 18: Proportions and Odds Ch. 18: 18; Ch. 19: 17
due December 1
Wednesday, November 15 Ch. 18: Proportions and Odds
Friday, November 17 Ch. 19: Chi-Square, Fisher's Exact Test, Mantel-Haenszel
Monday, November 20 Ch. 19: Chi-Square, Fisher's Exact Test, Mantel-Haenszel See above homework
Wednesday, November 22 Thanksgiving Recess
Friday, November 24 Thanksgiving Recess
Monday, November 27 Ch. 20: Logistic Regression Ch. 20: 15, 18
due December 8
Wednesday, November 29 Ch. 20: Logistic Regression
Friday, December 1 Ch. 21: Logistic Regression
Monday, December 4 Ch. 21: Logistic Regression Ch. 21: 19
due December 13
Wednesday, December 6 Ch. 21: Logistic Regression
Friday, December 8 Review

This page updated on August 25, 2006 by Elizabeth Housworth
Department of Mathematics
Department of Biology
Indiana University
Bloomington, IN 47405
Phone: 812-855-1960
Email: < >