## Project Information

Annotated Bibliography due November 1, 2017
Description of six (6) sources

R Tutorial: Tips for working with IPUMS data in R

## Exams

Take Home Exam 2
Due on Friday, December 15, at 9:00 PM to the D2L Dropbox folder

Take Home Exam 1
Due on Wednesday, October 18, at 5:30 PM to the D2L Dropbox folder

## Module 10: Decision Analysis

### Class Notes

Slides:

Taylor, Chapter 12, pp. 547-559; 575-577

### Exercises

In-class Exercise

## Module 9: Transportation & Assignment Models

### Class Notes

Slides:

• Transportation models: Taylor Ch 6, pp. 237-244
• Assignment models: Taylor Ch 6, pp. 251-253

### Exercises

Homework Taylor End of Chapter 6 Problems 26, 32, 37

## Module 8: Linear Programming

### Class Notes

Slides:

• Graphical Solution: Taylor, Ch 2, pp. 31-45, 48-53
• Computer Solution: Taylor, Ch 3, pp. 74-77
• Sensitivity Analysis: Taylor, Ch 3, pp. 82-93
• Example: Taylor, Ch 3, pp. 93-95
• More Examples: Taylor, Ch 4, pp. 114-127

### Exercises

Homework Taylor End of Chapter 3 Problems 13, 22, 23, 24a,b.

## Module 7: Analysis of Variance

### Class Notes

Slides: R tutorials:

• One-way ANOVA: FMF, Ch 10, pp 399-414, 447-449
• Multiway ANOVA: FMF, Ch 12, pp 501-511, 520-530
• Kruskal Wallis test: FMF, Ch 15, pp 674-686

### Exercises

Exercise: ANOVA
Data set: jobsat.RData

## Module 6: Binary Variables in Regression

### Class Notes

Slides: R tutorials:

• Logistic regression: FMF, Ch. 8

### Exercises

Exercise: Logit
Data set: jobsat.RData

## Module 5: Multiple Regression

### Class Notes

Slides: R tutorials:

• Multiple regression: FMF, Ch. 7, pp. 261-263(top), 276-284
• Assumptions: FMF, Ch. 7, pp. 271-273(top)

### Exercises

In-class Exercise: Regression
Wed Oct 18

Homework: Regression
Due on Wed Oct 25

## Module 4: Bivariate Relationships

### Class Notes

Slides: R tutorials:

• Correlation: FMF, Ch. 6, pp. 205-212 (skim math); 219-225
• Chi-square test of independence, FMF Ch. 18, pp. 815-816(top)
• Bivariate regression: FMF, Ch. 7, pp. 245-253, 256-260

### Exercises

No Homework Due for Oct 11

Worksheet: Correlation and Regression
For exam practice, be able to do problems 1, 2, 3, and 4(a)

For regression exam practice: Know how to interpret the meaning of coefficients in regression; conduct hypothesis tests on the regression coefficients.

## Module 3: Visualizing Means

### Class Notes

R tutorials:

R for Data Science
Garrett Grolemund
Chapter 3

## Module 2: Univariate and Bivariate Statistics

### Class Notes

Slides, Printer Friendly

R tutorials:

• Comparing Means:
FMF, Ch 9, pp. 368-397
• ### Exercises

Homework: Bivariate Stats
Due Wed, Sept 20, to D2L Dropbox

## Module 1: Introduction to Statistics

### Class Notes

• Intro to statistics:
FMF Ch 2 - All of it, but skim the math
• R environment: FMF Ch 3, pp. 62-83

## R Resources

### Installing R

• Rstudio is a great Integrated Development Environment (IDE). This is the graphical user interface that you use to interact with R, which is actually a separate software package than the R computing package.

## Instructor

### James Murray, Ph.D.

403T Wimberly Hall
608-785-5140
608-406-4068
jmurray@uwlax.edu

### Office Hours Appointments

Office hours appointments are available with only a one-hour notice, and are generally available at the following times:

• 8:30 AM - 11:00 AM Monday through Thursday
• 1:00 PM - 2:30 PM Monday and Wednesday
• 10:00 AM - 11:30 AM Friday
Make office hours appointment