# 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:

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 (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)

- Homework files: Unit 1 homework
- Homework sample solutions: Unit 1 homework sample solutions

# Unit 2: Relationships between variables

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

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

## 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)

- Homework files: Unit 2 homework instructions

## 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)

- Homework files: Unit 2 homework instructions

# 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)

- Homework files: Unit 3 homework instructions

## 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)

- Homework files: Unit 3 homework instructions

# 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)

- Homework files: Final project instructions