## 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

## Module 9: Transportation & Assignment Models

### Class Notes

**Slides:**

### Reading

- 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:**

### Reading

- 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:**

### Reading

- 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

Data set: jobsat.RData

## Module 6: Binary Variables in Regression

### Class Notes

**Slides:**

**R tutorials:**

### Reading

- Logistic regression: FMF, Ch. 8

### Exercises

Data set: jobsat.RData

## Module 5: Multiple Regression

### Class Notes

**Slides:**

**R tutorials:**

### Reading

- 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:**

### Reading

- 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:**

### Reading

by Hadley Wickham and

Garrett Grolemund

Chapter 3

### Exercises

## Module 2: Univariate and Bivariate Statistics

### Class Notes

**R tutorials:**

### Reading

FMF, Ch 9, pp. 368-397

### Exercises

In-class Exercise: Bivariate Stats

Data set: electricity.RData

Homework: Bivariate Stats

Data set: facebook.RData

Due Wed, Sept 20, to D2L Dropbox

## Module 1: Introduction to Statistics

### Class Notes

### Reading

- Intro to statistics:

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

### Exercises

- Online tutorial for first-time R user: https://tryr.codeschool.com/

## 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.
- R is a language and environment for statistical computing and graphics. You can learn more about it and when you are ready, download R to install to your Windows, Linux, or Mac computer.

### Resources

### Online Tutorial

- Online tutorial for first-time R user: https://tryr.codeschool.com/

## Instructor

### James Murray, Ph.D.

403T Wimberly Hall608-785-5140

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