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 (Monday, August 24)

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

Lecture 1.2: Characteristics of distributions (Wednesday, August 26)

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

Lab 1.1: R and Quarto familiarization (Wednesday, August 26)

Online lab 1.1 (due Sunday, August 30 at 11:59:00 pm)

Lecture 1.3: Comparing distributions and the Normal distribution (Monday, August 31)

Reading to do before class: Chapter 5

Topics covered:

  • Standard deviation and standardizing values

  • Normal models

  • Normal percentiles

  • Lecture webpage: Lecture 1.3

Lab 1.2: Advanced Quarto editing (Wednesday, September 2)

Unit 1 homework - progress check (due Thursday, September 3 at 23:59:00)

Online lab 1.2 (due on Friday, September 4 at 23:59:00)

Unit 1 homework (due Sunday, September 6 23:59:00 pm)

  • Homework files: Unit 1 homework instructions
    • Make sure to extract (unzip) the homework files before attempting to modify them!
  • Homework sample solutions: Unit 1 homework sample solutions

Unit 2: Relationships between variables

Lecture 2.1: Association and correlation (Wednesday, September 2)

Reading to do before class: Chapter 6

Topics covered:

  • Scatterplots

  • Correlations

  • Does correlation imply causation?

  • Lecture webpage: Lecture 2.1

Lecture 2.2: Simple Linear Regression (Monday, September 7)

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 2.3: Regression Wisdom (Wednesday, September 9)

Podcast for class: https://freakonomics.com/podcast/is-rainy-day-joint-pain-all-in-your-head/

Reading to do before class: Chapter 8

Topics covered:

  • Beware extrapolation

  • Outliers and leverage

  • Lurking variables

  • Straightening scatterplots

  • Lecture webpage: Lecture 2.3

Lab 2.1: Working with regressions using dplyr (Wednesday, September 9)

  • Lab files: [Lab 2.1]

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

Online lab 2.2 (due on Friday, September 11 at 23:59:00)

Unit 2 homework - progress check (due Sunday, September 13 at 23:59:00)

Lecture 2.4: Multiple Regression (Monday, September 14)

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

Unit 2 homework (due Sunday, September 20 at 11:59:00)

Unit 3: Measuring uncertainty

Lecture 3.1: Confidence intervals - proportions (Wednesday, September 16)

Podcast for class: https://www.hiddenbrain.org/podcast/where-truth-lies/

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

Lab 2.2: Interpreting coefficients (Wednesday, September 16)

  • Lab files: [Lab 2.2]

Lecture 3.2: Confidence intervals - means (Monday, September 21)

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 3.3: Hypothesis testing (Wednesday, September 23)

Podcast for class: https://international.schwab.com/story/survey-says-with-guests-w-joseph-campbell-emily-oster

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]

Lab 3.1: Bootstrapping (Wednesday, September 23)

  • Lab files: [Lab 3.1]

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

Lecture 3.4: Hypothesis testing wisdom (Monday, September 28)

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]

Unit 4: Statistical inference

Lecture 4.1: Comparing groups (Wednesday, September 30)

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]

In-class Unit 3 exam: Wednesday, September 30 from 1:15 pm to 2:30 pm

Lecture 4.2: Returning to regression (Monday, October 12)

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]

Lab 4.1: Interpretation activity (Wednesday, October 14)

  • [Reading]
  • [Activity]

Online lab 4.1 (due on Friday, October 16 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, October 18 at 23:59:00)

  • Homework files: [Final project instructions]

Final project (due Wednesday, October 21 at 23:59:00)

  • Homework files: [Final project instructions]