**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.

v

## 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: < ehouswor@indiana.edu >