Course Schedule and Class Materials

Important: class schedule is subject to change, contingent on mitigating circumstances and the progress we make as a class. If there are any changes, I will announce them on the Teams site.

Unit 1: Distributions

Lecture 1.1: Class welcome (Wednesday, March 20)

Reading to do before class: Chapter 1, 2.1 and 2.2, and 3

Topics covered:

  • What are data and variables?
  • How to display quantitative and qualitative variables
  • Contingency tables

Lab 1.1: R and Quarto familiarization (Thursday, March 21)

  • Lab files: Lab 1.1
    • Make sure to extract (unzip) the lab files before attempting to modify them!

Lecture 1.2: Characteristics of distributions (Friday, March 22)

Reading to do before class: Chapter 2.3-5 and 4

Topics covered:

  • How to describe the shape, center, and spread of a distribution

  • How to compare distributions

  • Dealing with problem distributions (outliers, reexpression)

  • Lecture webpage: Lecture 1.2

  • Lecture activity: Lecture 1.2 activity

  • Quality YouTube content: Tidyverse music video

Online lab 1.1 (due Sunday, March 24 at 11:59:00 pm)

Lecture 1.3: Comparing distributions and the Normal distribution (Monday, March 25)

Reading to do before class: Chapter 5

Topics covered:

Lab 1.2: Advanced Quarto editing (Tuesday, March 26)

  • Lab files: Lab 1.2
    • Make sure to extract (unzip) the lab files before attempting to modify them!

Unit 1 homework (due March 31 at 23:59:00 pm)

Unit 2: Relationships between variables

Lecture 2.1: Association and correlation (Wednesday, March 27)

Reading to do before class: Chapter 6

Topics covered:

Online lab 2.1 (due on Friday, March 29 at 23:59:00)

Lecture 2.2: Simple Linear Regression (Monday, April 1)

Reading to do before class: Chapter 7

Topics covered:

  • Line of best fit: least squares

  • The linear model

  • What are residuals

  • Regression assumptions

  • Lecture webpage: Lecture 2.2

  • Lecture activity: Lecture 2.2 activity

Lab 2.1: Working with regressions (Tuesday, April 2)

  • Lab files: Lab 2.1
    • Make sure to extract (unzip) the lab files before attempting to modify them!

Lecture 2.3: Regression Wisdom (Wednesday, April 3)

Reading to do before class: Chapter 8

Topics covered:

  • Beware extrapolation

  • Outliers and leverage

  • Lurking variables

  • Straightening scatterplots

  • Lecture webpage: Lecture 2.3

  • Lecture activity: Lecture 2.3 activity

Online lab 2.2 (due on Friday, April 5 at 23:59:00)

Unit 2 homework - midterm project - progress check (due Sunday, April 7 at 23:59:00)

Lecture 2.4: Multiple Regression (Monday, April 8)

Reading to do before class: Chapter 9

Topics covered:

  • What is multiple regression?

  • Interpreting multiple regression coefficients

  • Partial regression plots

  • Indicator variables

  • Lecture webpage: Lecture 2.4

  • Lecture activity: Lecture 2.4 activity

Lab 2.2: Interpreting coefficients (Tuesday, April 9)

  • Lab files: Lab 2.2
    • Make sure to extract (unzip) the lab files before attempting to modify them!

Online lab 2.3 (due on Friday, April 12 at 11:59:00)

Unit 2 homework - midterm project (due Sunday, April 14 at 11:59:00)

Unit 3: Measuring uncertainty

Lecture 3.1: Confidence intervals - proportions (Wednesday, April 10)

Reading to do before class: Chapter 13

Topics covered:

  • What is a sampling distribution?

  • When does the normal model apply?

  • Constructing a confidence interval

  • Interpreting a confidence interval

  • Lecture webpage: Lecture 3.1

  • Lecture activity: Lecture 3.1 activity

Lecture 3.2: Confidence intervals - means (Monday, April 15)

Reading to do before class: Chapter 14

Topics covered:

  • The Central Limit Theorem

  • Confidence interval for means

  • Interpreting a confidence interval

  • Final thoughts on confidence intervals

  • Lecture webpage: Lecture 3.2

  • Lecture activity: Lecture 3.2 activity

Lab 3.1: Bootstrapping (Tuesday, April 16)

  • Lab files: [Lab 3.1]
    • Make sure to extract (unzip) the lab files before attempting to modify them!

Lecture 3.3: Hypothesis testing (Wednesday, April 17)

Reading to do before class: Chapter 15

Topics covered:

  • What are hypotheses?

  • \(p\) values

  • \(p\) values and decisions – how to make a decision

  • Lecture webpage: Lecture 3.3

  • Lecture activity: Lecture 3.3 activity

Unit 3 homework - progress check (due Sunday, April 21 at 23:59:00)

Lecture 3.4: Hypothesis testing wisdom (Monday, April 22)

Reading to do before class: Chapter 16

Topics covered:

  • Interpreting p-values

  • Alpha and critical values

  • Practical vs. statistical significance

  • Type I and II errors

  • Power of a test

  • Lecture webpage: [Lecture 3.4]

  • Lecture activity: [Lecture 3.4 activity]

Lab 3.2: Sampling (Tueday, April 23)

  • Lab files: [Lab 3.2]

Unit 3 homework (due Sunday, April 28 at 23:59:00)

Unit 4: Statistical inference

Lecture 4.1: Comparing groups (Wednesday, April 24)

Reading to do before class: Chapter 17

Topics covered:

  • Confidence intervals for comparing two samples

  • Assumptions and conditions for two-sample hypothesis tests

  • Two-sample \(z\) test

  • Two-sample \(t\) test

  • Lecture webpage: [Lecture 4.1]

  • Lecture activity: [Lecture 4.1 activity]

Lecture 4.2: Returning to regression (Monday, April 29)

Reading to do before class: Chapter 20

Topics covered:

  • Regression inference and intuition

  • The regression table

  • Confidence and prediction intervals

  • Lecture webpage: [Lecture 4.2]

  • Lecture activity: [Lecture 4.2 activity]

Lab 4.1: Model building (Tueday, April 30)

  • Lab files: [Lab 3.2]

Lecture 4.3: Interpretation activity (Thursday, May 2)

  • How to read academic statistical results

  • Locating the model

  • Interpreting the test

  • Determining possible weaknesses of the model

  • Lecture paper discussion: Paper

  • Lecture activity: [Lecture 4.3 activity]

Unit 4 homework - paper analysis (due Sunday, May 5 at 23:59:00)

  • Homework files: [Unit 4 homework - paper analysis instructions]

Final project (due Wednesday, May 8 at 23:59:00)