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 Teams.

Unit 1: Distributions

Lecture 1.1: Class welcome (Thursday, October 23)

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, October 23)

Online lab 1.1 (due Sunday, October 26 at 11:59:00 pm)

Lecture 1.2: Characteristics of distributions (Tuesday, October 28)

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

Lecture 1.3: Comparing distributions and the Normal distribution (Thursday, October 30)

Reading to do before class: Chapter 5

Topics covered:

Lab 1.2: Advanced Quarto editing (Thursday, October 30)

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

Unit 1 homework - progress check (due Thursday, October 30 at 23:59:00)

Online lab 1.2 (due on Friday, October 31 at 23:59:00)

Unit 1 homework (due Sunday, November 2 at 23:59:00 pm)

  • Homework files: Unit 1 homework
  • Homework sample solutions: [Unit 1 homework sample solutions]

Unit 2: Relationships between variables

Lecture 2.1: Association and correlation (Tuesday, November 4)

Reading to do before class: Chapter 6

Topics covered:

Lecture 2.2: Simple Linear Regression (Thursday, November 6)

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 using dplyr (Thursday, November 6)

Lecture 2.3: Regression Wisdom (Friday, November 7)

  • Note the special class time!

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, November 7 at 23:59:00)

Unit 2 homework - progress check (due Sunday, November 9 at 23:59:00)

Lecture 2.4: Multiple Regression (Tuesday, November 11)

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]

Unit 2 homework (due Sunday, November 16 at 11:59:00)

Unit 3: Measuring uncertainty

Lecture 3.1: Confidence intervals - proportions (Thursday, November 13)

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]

Lab 2.2: Interpreting coefficients (Thursday, November 13)

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

Lecture 3.2: Confidence intervals - means (Tuesday, November 18)

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]

  • No lecture activity

Lecture 3.3: Hypothesis testing (Thursday, November 20)

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]

Lab 3.1: Bootstrapping (Thursday, November 20)

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

Lecture 3.4: Hypothesis testing wisdom (Tuesday, November 25)

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]

In-class Unit 3 exam: Thursday, November 27 from 6:00 pm to 7:15 pm

Unit 4: Statistical inference

Lecture 4.1: Comparing groups (Thursday, November 27)

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 (Tuesday, December 2)

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]

Final class 4.3: Interpretation activity (Thursday, December 4)

  • How to read academic statistical results

  • Locating the model

  • Interpreting the test

  • Determining possible weaknesses of the model

  • Final class activity: [Final class 4.3 activity]

Online lab 4.1 (due on Friday, December 6 at 11:59:00)

  • Your choice of any DataCamp course (as long as it relates to statistics)
    • Send the completion certificate from the end of the course to the lab manager, Jingyu Wang, on Teams

Final project - progress check (due Sunday, December 8 at 23:59:00)

  • Homework files: [Final project instructions]

Final project (due Wednesday, December 10 at 23:59:00)

  • Homework files: [Final project instructions]