Tentative STT 3850 Course Schedule - Spring 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: (Jan 12—15)

• 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 Jan 14

• 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: (No Class Monday Jan 19; Class Jan 20—22)

• Lecture Slides

• Complete the Data Visualization chapter of Introduction to the Tidyverse — DataCamp — Due NLT 5:00 pm Jan 19

• 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 Jan 20

• Complete PS-02 due by 5:00 pm Jan 21

• Quiz #1 Jan 22

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: (Jan 26—Jan 29)

• 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 Jan 25

• Complete the Grouping and Summarizing chapter of Introduction to the Tidyverse — DataCamp — Due NLT 5:00 pm Jan 26

• Scales

• Complete PS-03 by 5:00 pm Jan 28

• In-class work on dplyr-CH1-handout

• Quiz #2 - Jan 29

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: (Feb 2—5)

• Lecture Slides

• Complete the Introduction to Modeling chapter of Modeling with Data in the Tidyverse — DataCamp — Due NLT 5:00 pm Feb 1

• 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 Feb 2

• Complete PS-04 due by 5:00 pm Feb 4

• Quiz #3 Feb 5

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: (Feb 9—12)

• Lecture Slides

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

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

• Regression with a single categorical variable handout.

• Class notes for one quantitative and one qualitative predictor

• Complete the Model Assessment and Selection chapter of Modeling with Data in the Tidyverse — DataCamp — Due NLT 5:00 pm Feb 9

• Complete PS-05 by 5:00 pm Feb 11

• Quiz #4 Feb 12

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: (Feb 16—19)

• Lecture Slides

• Complete the Summary Statistics chapter of Introduction to Statistics in R — DataCamp — Due NLT 5:00 pm Feb 15

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

• 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 Feb 16

• Complete PS-06 by 5:00 pm Feb 18

• Quiz #5 Feb 19

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: (Feb 23—26)

• Lecture Slides

• Complete the The binomial distribution chapter in Foundations of Probability in R — DataCamp — Due NLT 5:00 pm Feb 22

• Complete the Laws of probability chapter in Foundations of Probability in R — DataCamp — Due NLT 5:00 pm Feb 23

• Probability

• Quiz #6 Feb 26

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: (Mar 2—5)

• Lecture Slides

• Complete the Bayesian statistics chapter in Foundations of Probability in R — DataCamp — Due NLT 5:00 pm Mar 1

• Complete the Related distributions chapter in Foundations of Probability in R — DataCamp — Due NLT 5:00 pm Mar 2

• Quiz #7 Mar 5

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Spring Break: Mar 9—13

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Week 9: (Mar 16 – 19)

• Complete the More Distributions and the Central Limit Theorem chapter in Introduction to Statistics in R — DataCamp — Due NLT 5:00 pm Mar 15

• 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 Mar 16 — not graded Partial Solution

• Start PS-07 due by 5:00 pm Mar 18

• Quiz #8 Mar 19

Optional

• Sampling Distributions

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

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Week 10: (Mar 23—26)

• 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 Mar 22

• 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 Mar 23

• Complete the Inference for Difference in Two Parameters chapter in Inference for Numerical Data in R — DataCamp — Due NLT 5:00 pm Mar 24

• Bootstrap Example

• Quiz #9 Mar 26

Optional

• Misc infer Examples

• Inference Examples used in class

• Bootstrap Percentile CI examples

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Week 11: (Mar 30—Apr 2)

• 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 Apr 1

• Quiz #10 Apr 2

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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: (Apr 6—9)

• Lecture Slides for weeks 11, 12, and 13

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

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

• Read about Permutation Testing

• Complete the Completing a randomization test: gender discrimination chapter of Foundations of Inference — DataCamp — Due NLT 5:00 pm Apr 6

• Complete the Hypothesis testing errors: opportunity cost chapter of Foundations of Inference — DataCamp — Due NLT 5:00 pm Apr 7

• Quiz #11 - Apr 9

Optional

• Misc infer Examples

• Inference Examples used in class

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Week 13: (Apr 13—16)

• 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 Apr 12

• 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 Apr 13

• Complete PS-09 by 5:00 pm Apr 15

• Quiz #12 Apr 16

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: (Apr 20—23)

• 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 Apr 19

• Watch Goodness-Of-Fit video on ASULEARN

• Goodness-Of-Fit

• In class Examples

• Quiz #13 Apr 23

Optional

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

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Week 15: (Apr 27—29)

• 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 Apr 26

• Watch Chi-Square Test of Independence video on ASULEARN

• Watch Chi-Square Test of Homogeneity video on ASULEARN

• Goodness-Of-Fit

• In class Examples

• Course Review

• Quiz #14 Apr 29

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Final Exam

• Section -101 (11:00 am Class): May 5: 11:00 am—1:30 pm

• Section -102 (1:00 pm Class): May 6: 11:00 am—1:30 pm

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Last Updated on: Dec 10, 2025 at 01:29:41 PM

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