Tentative STT 3850 Course Schedule - Fall 2026

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General Notes:

1. Please bring a notebook and pencil to every class.
2. The principal documents for this course are ModernDive: An Introduction to Statistical and Data Sciences via R (MD), Data Science with R (DSWR), and Mathematical Statistics with Resampling and R (MSWR) -available inside the ASULEARN course page.
3. Problem Set (PS) assignments are generally due on Thursdays by 5:00 pm
4. The links for the problem sets and the sampling distribution assignment in this document may only work for the instructor! To accept each of these assignments, go to ASULEARN and click on the appropriate link in the GitHub Classroom Invitation Links for Assignments block.
5. QUARTO cheat sheet

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Week 1: (Aug 17—21)

• Before the first class meeting, read Chapter 1 (Getting Started with Data in R) of MD—pgs 1-20

• Before the first class meeting, read Chapter 1 Why Git? Why GitHub? of Happy Git With R.

• Become familiar with the Appstate RStudio/POSIT workbench server. You will use your Appstate user name and password to log in to the server. You must be registered in the class to access the server.

We will walk through everything outlined below in class. If you want to complete the setup before class that is fine.

• Sign-up for a free account on GitHub. When you register for a free individual GitHub account, request a student discount to obtain a few private repositories as well as unlimited public repositories. Please use something similar to FirstNameLastName as your username when you register with GitHub. For example, my username on GitHub is alanarnholt. If you have a popular name such as John Smith, you may need to provide some other distinguishing characteristic in your username.

• Introduce yourself to Git by following the directions in HappyGitWithR

• Cache your credentials and set up a personal access token (PAT) by following the directions in HappyGitWithR.

• TL;DR the chapters in Happy Git With R — follow this document to Set up Git and GitHub

• VIDEO of the setup process

• Complete PS-01 due by 5:00 pm Aug 21

• VIDEO of how to accept and clone the assignment

Optional

• For additional ideas with Quarto documents watch Hello, Quarto: A World of Possibilities (for Reproducible Publishing)

• Introduction to R slides

• Watch Paul the Octopus clip (61 seconds).

• You may want to install Git, R, RStudio, zotero, and optionally \(LaTeX\) on your personal computer. If you do, you will want to follow Jenny Bryan’s excellent advice for installing R and RStudio and installing Git. Jenny’s advice is also in chapters 6 and 7 of Happy Git and GitHub for the useR. Note: Git, R, RStudio, and \(LaTeX\) are installed on the Appstate RStudio server.

• Watch the following videos as appropriate:

• Install R on Mac (2 min)

• Install R for Windows (3 min)

• Install R and RStudio on Windows (5 min)

• Work through chapter 1 (Git and GitHub) of DSWR. Make sure RStudio is set up to communicate with Git by following the directions in HappyGitWithR for introducing yourself to Git.

• Work through chapter 2 (Introduction to R) of DSWR

• Reading in Data

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Week 2: (Aug 24—28)

• Lecture Slides

• Complete the Data Visualization chapter of Introduction to the Tidyverse — DataCamp — Due NLT 5:00 pm Aug 23

• Before class read chapter 2 (Data Visualization) of MD — pgs 21-62

• Complete the Types of Visualizations chapter of Introduction to the Tidyverse — DataCamp — Due NLT 5:00 pm Aug 24

• Complete PS-02 due by 5:00 pm Aug 27

Optional

• Read Getting used to R, RStudio, and R Markdown

• Work through chapter 5 (Using ggplot2) of DSWR

• Complete Data Visualization with ggplot2 (Part 1) (DataCamp)

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Week 3: (Aug 31—Sep 4)

• Lecture Slides

• Before class read chapter 3 (Data Wrangling) of MD — pgs 65-96

• Complete the Data Wrangling chapter of Introduction to the Tidyverse — DataCamp — Due NLT 5:00 pm Aug 30

• Complete the Grouping and Summarizing chapter of Introduction to the Tidyverse — DataCamp — Due NLT 5:00 pm Aug 31

• Scales

• Complete PS-03 by 5:00 pm Sep 3

• In-class work on dplyr-CH1-handout

• Quiz #1 - Sep 4

Test yourself:

1. R Markdown Practice
2. R Markdown Extra Credit
3. LaTeX/knitr Extra Credit

Optional

• Watch Selecting Variables Using dplyr in R

• Watch Filtering Observations Using dplyr in R

• Watch Arranging Your Data Using dplyr in R

• Watch Adding New Columns Using dplyr in R

• Watch Creating Summary Statistics Using dplyr in R

• Watch Practicing your Tidyverse Skills: Advanced Filters with Dplyr - Quarto Document

• Watch Practicing Your Tidyverse Skills: if_else() Functions with dplyr

• Watch Practicing your Tidyverse Skills: case_when() Functions with dplyr

• Posit Cheat Sheets

• Work through chapter 3 (Starting with Data) of DSWR

• Work through chapter 4 (Data Manipulation) of DSWR

• In-class work on dplyr-CH2-handout

• In-class work on dplyr-CH3-handout

• In-class work on dplyr-CH4-handout

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Week 4: (No Class Sep 7 - Class on Sep 8—11)

• Lecture Slides

• Complete the Introduction to Modeling chapter of Modeling with Data in the Tidyverse — DataCamp — Due NLT 5:00 pm Sep 7

• Before class read chapter 5 (Basic Regression) of MD — pgs 119-160

• In class go over this document

• Complete the Modeling with Basic Regression chapter of Modeling with Data in the Tidyverse — DataCamp — Due NLT 5:00 pm Sep 8

• Complete PS-04 due by 5:00 pm Sep 10

Optional

• Read chapter 4 (Data Importing and “Tidy” Data) of MD — pgs 99-117

• Read the Git and GitHub chapter from Hadley Wickham’s book R Packages

• Brian Caffo’s take on R IDEs

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Week 5: (Sep 14—18)

• Lecture Slides

• Complete the Modeling with Multiple Regression chapter of Modeling with Data in the Tidyverse — DataCamp—Due NLT 5:00 pm Sep 13

• Before class read chapter 6 (Multiple Regression) of MD — pgs 161-191

• Regression with a single categorical variable handout.

• Complete the Model Assessment and Selection chapter of Modeling with Data in the Tidyverse — DataCamp — Due NLT 5:00 pm Sep 14

• Complete PS-05 by 5:00 pm Sep 17

• Exam #1 Sep 18

Optional

• Complete Correlation and Regression in R (DataCamp)

• For additional ideas with Quarto documents watch Hello, Quarto: A World of Possibilities (for Reproducible Publishing)

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Week 6: (Sep 21—25)

• Lecture Slides

• Complete the Summary Statistics chapter of Introduction to Statistics in R — DataCamp — Due NLT 5:00 pm Sep 20

• Before class read/review chapter 6 (Multiple Regression) of MD — pgs 161-191

• Class notes for one quantitative and one qualitative predictor

• Go over in class Misc Regression

• Complete the Random Numbers and Probability chapter of Introduction to Statistics in R — DataCamp — Due NLT 5:00 pm Sep 21

• Complete PS-06 by 5:00 pm Sep 24

Optional

• Answer the questions at the end of Misc Regression for extra credit

• Work on Is this Discrimination?

• Some ideas for how to answer the Is this Discrimination?

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Week 7: (Sep 28—Oct 2)

• Lecture Slides

• Complete the The binomial distribution chapter in Foundations of Probability in R — DataCamp — Due NLT 5:00 pm Sep 27

• Complete the Laws of probability chapter in Foundations of Probability in R — DataCamp — Due NLT 5:00 pm Sep 28

• Probability

Optional

• In Class Problems

• Foundations of Probability with some Extras

• Complete the Improving the Report chapter of Reporting with R Markdown— DataCamp

• Complete the Customizing the Report chapter of Reporting with R Markdown — DataCamp

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Week 8: (Fall Break Oct 5—6; Class Oct 8—9)

• Lecture Slides

• Complete the Bayesian statistics chapter in Foundations of Probability in R — DataCamp — Due NLT 5:00 pm Oct 7

• Complete the Related distributions chapter in Foundations of Probability in R — DataCamp — Due NLT 5:00 pm Oct 8

• Quiz #2 Oct 9

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Week 9: (Oct 12—16)

• Complete the More Distributions and the Central Limit Theorem chapter in Introduction to Statistics in R — DataCamp — Due NLT 5:00 pm Oct 11

• Before class read chapter 7 (Sampling) of MD — pgs 195-232

• Complete (will go over most questions in class) Sampling Distributions Lab by 5:00 pm Oct 12 — not graded Partial Solution

• Start PS-07 due by 5:00 pm Oct 15

• Test #2 Oct 16

Optional

• Sampling Distributions

• Read Chapter 4 of MSWR — Sampling Distributions; Problems 2, 5, 12-16

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Week 10: (Oct 19—23)

• Lecture Slides

• Chapter 5 notes

• Read Chapter 5 of MSWR

• Complete the Bootstrapping for Estimating a Parameter chapter in Inference for Numerical Data in R — DataCamp — Due NLT 5:00 pm Oct 18

• Before class read chapter 8 (Bootstrapping and Confidence Intervals) of MD — pgs 233-305

• Complete the Introducing the t-distribution chapter in Inference for Numerical Data in R — DataCamp — Due NLT 5:00 pm Oct 19

• Complete the Inference for Difference in Two Parameters chapter in Inference for Numerical Data in R — DataCamp — Due NLT 5:00 pm Oct 20

• Bootstrap Example

Optional

• Misc infer Examples

• Inference Examples used in class

• Bootstrap Percentile CI examples

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Week 11: (Oct 26—30)

• Lecture Slides for weeks 11, 12, and 13

• Before class review chapter 8 (Bootstrapping and Confidence Intervals) of MD — pgs 233-305

• Read Chapter 7 of MSWR

• Chapter 7 notes

• Bootstrap \(t\)

• Complete PS-08 by 5:00 pm Oct 29

• Quiz #3 Oct 30

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Optional

• Misc infer Examples

• Inference Examples used in class

• Bootstrap Percentile CI examples

• Inference for Numerical Data in R

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Week 12: (Nov 2—6)

• NPC Nov 5-9

• Lecture Slides for weeks 11, 12, and 13

• Before class read Chapter 9 (Hypothesis Testing) of MD — pgs 307-360

• Read about Permutation Testing

• Complete the Introduction to ideas of inference chapter of Foundations of Inference — DataCamp — Due NLT 5:00 pm Nov 4

• Complete the Completing a randomization test: gender discrimination chapter of Foundations of Inference — DataCamp — Due NLT 5:00 pm Nov 5

• Complete the Hypothesis testing errors: opportunity cost chapter of Foundations of Inference — DataCamp — Due NLT 5:00 pm Nov 6

Optional

• Misc infer Examples

• Inference Examples used in class

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Week 13: (Nov 9—13)

• NPC Nov 5-9

• Lecture Slides for weeks 11, 12, and 13

• Complete the Inference for a Single Parameter chapter in Inference for Categorical Data in R — DataCamp — Due NLT 5:00 pm Nov 8

• Before class review Chapter 9 (Hypothesis Testing) of MD — pgs 307-360

• Permutation Examples

• Complete the Proportions: Testing and Power chapter in Inference for Categorical Data in R — DataCamp — Due NLT 5:00 pm Nov 9

• One Proportion

• Complete PS-09 by 5:00 pm Nov 12

• Exam #3 Nov 13

Optional

• Complete the problems in the R Markdown file and publish your solution to RPubs.

• Misc R Markdown Examples

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Week 14: (Nov 16—20)

• Lecture Slides

• Reading material for weeks 14 and 15

• Complete the Comparing Many Parameters: Independence chapter in Inference for Categorical Data in R — DataCamp — Due NLT 5:00 pm Nov 15

• Watch Goodness-Of-Fit video on ASULEARN

• Goodness-Of-Fit

• In class Example 1 and Example 2

Optional

• Complete the problems in the R Markdown file and publish your solution to RPubs

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Week 15: (Nov 23—24; Thanksgiving Break Nov 25—27; Classes Nov 30—Dec 1)

• Lecture Slides

• Reading material for weeks 14 and 15

• Complete the Comparing Many Parameters: Goodness of Fit chapter in Inference for Categorical Data in R— DataCamp — Due NLT 5:00 pm Nov 22

• Watch Chi-Square Test of Independence video on ASULEARN

• Watch Chi-Square Test of Homogeneity video on ASULEARN

• Goodness-Of-Fit

• In class Example 1 and Example 2

• Course Review—Dec 1

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Final Exam

• Section -101 (11:00 am Class): Friday, Dec 4, 11 am - 1:30 pm

• Section -102 (1:00 pm Class): Saturday, Dec 5, 11 am - 1:30 pm

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Last Updated on: Jul 28, 2026 at 12:49:14 PM

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