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)
- Lab files: Lab 1.1
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:
Standard deviation and standardizing values
Normal models
Normal percentiles
Lecture webpage: Lecture 1.3
Lecture activity: Lecture 1.3 activity
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)
- Homework files: Unit 1 homework instructions
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:
Scatterplots
Correlations
Does correlation imply causation?
Lecture webpage: Lecture 2.1
Lecture activity: Lecture 2.1 activity
- Sample solution: Lecture 2.1 activity sample solution
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
- Sample solution: Lecture 2.2 activity sample solution
Lab 2.1: Working with regressions using dplyr (Thursday, November 6)
- Lab files: Lab 2.1
- Make sure to extract (unzip) the lab files before attempting to modify them!
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
- Sample solution: Lecture 2.3 activity sample solution
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)
- Homework files: Unit 2 homework instructions
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
- Sample solution: Lecture 2.4 activity sample solution
Unit 2 homework (due Sunday, November 16 at 11:59:00)
- Homework files: Unit 2 homework instructions
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
- Sample solution: Lecture 3.1 activity sample solution
Lab 2.2: Interpreting coefficients (Thursday, November 13)
- Lab files: Lab 2.2
- Sample solution: Lab 2.2 sample solution
- 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
Lecture activity: Lecture 3.2 activity
- Sample solution: Lecture 3.2 activity sample solution
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
- Sample solution: Lecture 3.3 activity sample solution
Lab 3.1: Bootstrapping (Thursday, November 20)
- Lab files: Lab 3.1
- Sample solution: Lab 3.1 sample solution
- 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 (Tuesday, December 2)
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 (Tursday, December 4)
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: Interpretation activity (Thursday, December 4)
- Lab files: Lab 4.1
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