## Practice Exams

**Exam 1 - Monday, March 11**

Practice Exam

R Code and Output

**Exam 2 - Monday, May 13, 7:00 PM - 9:00 PM**

Practice Exam

**Homework Answer Keys**

Homework 1 Answers

Homework 2 Answers

Homework 3 Answers

Homework 4 Answers

## Project

**Final Project Guidelines and Deadlines**

**Final Presentations**

Presentations are Monday, May 5

Group 1: 5:30 - 7:20 PM

Group 2: 7:30 - 9:00 PM

See Canvas for the schedule

Presentation Instructions

See **Oral Presentation** and **Data Analysis** pages of the Final Project Rubrics

**Final Paper**

**Due Friday, 17, 5:00 PM**

Presentation Instructions

See **Written Communication** and **Data Analysis** pages of the Final Project Rubrics

Annotated Bibliography due **Monday, April 29, 5:30 PM**

**Rstudio assignment: Preparing your data**

Put your data in Rstudio.cloud and prepare it for analysis.

See the `preparedata.R` file in the `Final Project` Rstudio project.

Upload the .R script to Canvas

**Due April 24, 5:30 PM**

**Rstudio writing assignment: Data description**

Load the prepared data into an RMarkdown file and describe and provide summary statistics for each variable.

See the `datasummary.Rmd` file in the `Final Project` Rstudio project.

Upload the RMarkdown file and PDF file to Canvas

**Due April 24, 5:30 PM**

**R Tutorials:**

Using IPUMS Data in R

Constructing Your Model

Upload to Canvas

Upload to Shared Google Drive to share with the class

**Due Mon April 8, 5:30 PM**

Refining your Idea Writing

Upload to Canvas

Upload to Shared Google Drive to share with the class

**Due Mon March 25, 5:30 PM**

Brainstorming Writing

**Due Mon March 4, 5:30 PM**

## Module 8: Forecasting

### Class Lessons

### Exercises

**Datacamp due Mon Apr 22**

Forecasting in R

Do your best and take pride in your work for full credit

## Module 7: Panel Regression

### Reading

### Exercises

## Module 6: Binary Dependent Variables

### Reading

Stock and Watson, Ch 11, pp. 385-398.

### Class Lessons

Linear Probability Model

Logistic Regression

Estimating Probabilities with Binary Variables (on your own)

### Exercises

**Assignment**
Homework 4: Binary Dependent Variable

Use resources in rstudio.cloud to complete this assignment.

**Due Mon Apr 8 5:30 PM**

**Datacamp**

Multiple and Logistic Regression

Graded based on effort. You may follow

**Due Monday, April 1, 5:30 PM**

## Module 5: Data Visualization

### Reading

### Class Lessons

**Tutorials:**

Grammar of Graphics

Scatter Plots and Anscombe's Quartet

Bar Plots to Illustrate Means

### Exercises

**Datacamp**

Data visualization with ggplot

Graded based on effort. You may follow

**Due Monday, April 1, 5:30 PM**

## Module 4: Heteroskedasticity

### Reading

### Class Lessons

**Tutorials:**

Introduction to Heterskedasticity

Inference with Heteroskedasticity

### Exercises

**Assignment**
Homework 3: Heteroskedasticity

Use resources in rstudio.cloud to complete this assignment.

**Due Mon Apr 1 5:30 PM**

## Module 3: Multiple Regression

### Reading

Heiss, Chapter 3, pages 91-95, 97-99

Heiss, Chapter 4, pages 103-109

Heiss, Chapter 6, pages 125-129

Heiss, Chapter 7, pages 135-137

### Class Lessons

**Tutorials:**

Introduction to Multiple Regression

Variance Decomposition

Standardized Regression

General Linear Restriction

Multicolinearity

Interaction Effects

Linear Combinations

Dummy Variables

### Exercises

**Datacamp courses:**

Modeling Data Tidyverse

**Due Mon Feb 25 5:30 PM**

**Datacamp**

Data visualization with ggplot

Graded based on effort. You may follow

**Due Monday, April 1, 5:30 PM**

**Assignment**
Homework 2: Introduction to Regression

Use resources in rstudio.cloud to complete this assignment.

**Due Mon Feb 25 5:30 PM**

## Module 2: Introduction to Regression

### Reading

Stock and Watson, Chapter 4

### Class Lessons

**Tutorials:**

Introduction to Bivariate Regression

Estimating and Interpretting Coefficients

Nonlinearities in Regression

### Exercises

Datacamp: Correlation and Regression

**Due 5:30 PM February 18, 2019**

## Module 1: Introduction and Review

### Reading

**Hypothesis testing and confidence intervals:**

Stock and Watson, Chapter 3

(Focus on concepts and intuition, not the mathematical details)

**Getting started in R:**

Wickham and Grolemund

Chapter 1,
Chapter 4, and
Chapter 5

### Class Lessons

**Tutorials:**

Introduction to Data

Estimating the Mean

Differences in Means (Indep)

Differences in Means (Paired)

Correlation

**Slides:**

Slides

Printer Friendly

### Exercises

**Datacamp courses:**

Intro to Tidyverse

**Due Mon Feb 4 5:30 PM**

Intro to T-tests

**Due Mon Feb 11 5:30 PM**

**Assignment**
Homework 1: T-Tests

Use resources in rstudio.cloud to complete this assignment.

**Due Mon Feb 18 5:30 PM**

## Resources

## Tutor

Haley Maus is available for tutoring!

Hours:

- Monday's 1:00-4:00 pm
- Tuesday's 7:00-9:00 pm
- Thursday's 1:00- 3:00 pm

**Room 327 Wimberly**

Make an appointment at

https://uwl-eco-lab.youcanbook.me/

**Please only make appointments during above available times. Other times are designated for other classes.**

## Textbook

Stock, James H. and Watson, Mark W., (2015),

Available in UWL Textbook Rental

## R Resources

### Datacamp

Datacamp is a commercial service that provides automated interactive online "courses" in data science and coding. You will be assigned several of these courses that cover introductory statistical programming using the R programming language. The service is provided for free to students in higher education.

Please join the Datacamp class site specific to this offering of ECO 307. Once logged in, you will see several courses assigned with due dates. Courses take approximately 4 hours to complete and you will be given one week to complete the courses when assigned. You do not need to complete the course all at once. You may log in and out and complete small amounts throughout the week. Your work is saved automatically.

Please follow this link to join the ECO 307 Datacamp course: https://tinyurl.com/ECO307DataCamp

### RStudio Cloud

Rstudio.cloud is a free online platform for using R that does not require installing any software and makes collaborating with other users easy.

Please join the ECO 307 instructor RStudio workspace using the link below. With this workspace, you can see what we do in class and copy files relevant for your project.

### Installing Software

**R**

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.

**Rstudio**

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.

**MiKTeX** (Windows)

MiKTeX is software for compiling LaTeX and Markdown documents. LaTeX and Markdown are markup languages, which are alternatives to using word processing software. With markup languages, you type code to dictate what a document should look like, then compile this code to create a pretty document. The software is free and open source. You can download here. When you do so, download and install the "Net Installer" 64 bit, and select the "Complete" installation.

**MacTex** (Mac)

MacTeX is software for compiling LaTeX and Markdown documents. LaTeX and Markdown are markup languages, which are alternatives to using word processing software. With markup languages, you type code to dictate what a document should look like, then compile this code to create a pretty document. The software is free and open source. You can download here.

## Instructor

### James Murray, Ph.D.

Interim Associate Dean | College of Business AdministrationAssociate Professor of Economics

138 Wimberly Hall

608-785-8095

608-406-4068

jmurray@uwlax.edu

### Office Hours Appointments

Office hours appointments are available with only a one-hour notice:

Make office hours appointment