# 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 (Tuesday, August 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 (Wednesday, August 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 (Thursday, August 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

## Online lab 1.1 (due Sunday, August 25 at 11:59:00 pm)

## Lecture 1.3: Comparing distributions and the Normal distribution (Tuesday, August 27)

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 (Wednesday, August 28)

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

## Unit 1 homework (due September 1 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 (Thursday, August 29)

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, August 30 at 23:59:00)

## Lecture 2.2: Simple Linear Regression (Tuesday, September 3)

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

## Online lab 2.2 (due on Friday, September 6 at 23:59:00)

## Unit 2 homework - progress check (due Sunday, September 8 at 23:59:00)

- Homework files: Unit 2 homework instructions

## Lab 2.1: Working with tables in Quarto (Wednesday, September 11)

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

## Lecture 2.3: Regression Wisdom (Thursday, September 12)

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]

## Lecture 2.4: Multiple Regression (Friday, September 13 @ 10:00 am)

*Note the special class time!*

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]

## Online lab 2.3 (due on Friday, September 13 at 11:59:00)

## Unit 2 homework (due Tuesday, September 17 at 11:59:00)

- Homework files: [Unit 2 homework instructions]

# Unit 3: Measuring uncertainty

## Lecture 3.1: Confidence intervals - proportions (Thursday, September 19)

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 (Friday, September 20)

*Note the special class time!*

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]

## Lecture 3.3: Hypothesis testing (Saturday, September 21)

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, September 22 at 23:59:00)

- Homework files: [Unit 3 homework instructions]

## Lecture 3.4: Hypothesis testing wisdom (Tuesday, September 24)

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 3.1: Bootstrapping (Wednesday, September 25)

- Lab files: [Lab 3.2]

## Unit 3 homework (due Sunday, October 6 at 23:59:00)

- Homework files: [Unit 3 homework instructions]

# Unit 4: Statistical inference

## Lecture 4.1: Comparing groups (Thursday, September 26)

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, October 8)

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 (Wednesday, October 9)

- Lab tbd

## Final class 4.3: Interpretation activity (Thursday, October 10)

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, October 11 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

## Final project - progress check (due Sunday, October 13 at 23:59:00)

- Homework files: [Final project instructions]

## Final project (due Wednesday, October 16 at 23:59:00)

- Homework files: [Final project instructions]