Jump to:

• Basic course information
  • Instructor
  • Teaching Assistant
• Lecture notes and related materials
• Homework assignments (solutions available via Canvas)
• R/RStudio resources

Basic course information

Instructor

Jared S. Murray, Ph.D., Assistant Professor of Statistics

• Email: jared.murray@mccombs.utexas.edu
• Website: http://jaredsmurray.github.io/
• Office: CBA 6.482 (Sixth floor, on the east side of the building that faces the entrance of Gregory Gym)
• Office Hours: Wednesday 5 - 6 PM and Tuesday 9 - 10 AM, or by appointment. Please email me to set appointments; this ensures that I’m prepared for your visit, and that you don’t waste time trying to drop in when I’m not available.

Teaching Assistant

Yilin He, IROM PhD Student

• Email: yilin.he@mccombs.utexas.edu
• Office Hours: Tuesday 4 - 5 PM in CBA 5.324C

Please see the syllabus for more information.

Lecture notes and related materials

• Section 1.1: Introduction and Probability concepts (R code)
• Section 1.2: Probability and Decisions (Sharpe ratio R code)
• Section 1.3: The Normal and Other Continuous Distributions (R code)
• Section 1.4: Learning from Data (R code)
• Section 2.1: Simple Linear Regression: Least Squares (R code)
• Section 2.2: Simple Linear Regression: Predictions and Inference (R code)
• Section 3.1: Multiple Linear Regression (R code)
• Section 3.2: Multiple Linear Regression II (R code)
• Section 3.3: Dummies and Interactions (R code)
• Example: Beauty and course evaluations (R code to load data)
• Section 3.4: Diagnostics and Transformations (R code)
• Section 4.1: Time Series I (R code)
• Section 4.2: Time Series II (R code)
• Section 5.1: Model Selection (R code)

Homework assignments (solutions available via Canvas)

1. Homework 0 (nothing to turn in!): Complete the first three tutorials listed below under “R/RStudio resources”. By “complete” I mean follow along with the reading, and make sure you can run all the R commands from within RStudio. It is most helpful if you actually type the commands in yourself, rather than copy and pasting the code. It’s tedious at first but helpful for learning. Once you’ve worked through the tutorials, run the R code accompanying the Section 1.1 lecture notes. Try changing things and see what happens!
2. Homework 1
3. Homework 2
4. Homework 3
5. Homework 4
6. Homework 5 R code to read in data is here: hw5.R
7. Homework 6 R code to read in data is here: hw6.R
8. Homework 7 R code to read in data is here: hw7.R
9. Homework 8 R code to read in data is here: hw8.R
10. Homework 9 (Optional – Extra credit) R code to read in data is here: hw9.R

R/RStudio resources

There are many, many resources online for working in R. I have collected a few for you here, and I will continue to update this section throughout the semester:

• Installing R and RStudio: This should get you up and running with R and RStudio.
• Installing packages in RStudio: R is more than a single software program; it has the ability to access a central repository of “packages” that add new functionality to R. As of this writing there are over 10,000 R packages in this repository! We will use the mosaic package throughout the semester, so go ahead and install that package following this tutorial.
• Getting familiar with R: A quick demo to help get you started in R.
• Tips on writing code in RStudio’s Script pane: Tips for creating, loading, editing, and saving R scripts.

Other general resources:

• R/Rstudio video walkthroughs The Statistics department at UT Austin has produced a number of video walkthroughs for common tasks in R.

Specific guidance/extra information:

• Working with categorical data in R: Understanding factors