1. Prepare (due M 2/21)
   1. Content below
   2. Sakai quizzes
2. Peer Instructions – See on the class forum
3. Homework (due Su 2/27)
4. Worked Example

Content

7.A – Relational Database (24 min.)

7.B

1. SQL Querying (21 min.)
2. SQL with Python and Pandas (12 min.)

Optional Supplements

• SQLite Command Line Interface If you have a Mac/Linux machine, you should already be able to launch by just entering “sqlite3” in a terminal. If you have a Windows machine, you can download the command line interface from the Precompiled Binaries for Windows on the SQLite download page.
• Python SQLite3 API Documentation
• Pandas SQL Documentation
• w3resource SQLite Tutorial

1. Prepare (due M 2/7)
   1. Content below
   2. Sakai quizzes
2. Peer Instructions – See on the class forum
3. Homework (due Su 2/13)
4. Worked Example

Content

5.A – Confidence Intervals and Bootstrapping

1. Intro Confidence Intervals (17 min.)
2. Confidence Intervals in Python (17 min.)

5.B – Hypothesis Testing

1. Intro Hypothesis Testing and Proportions (14 min.)
2. Hypothesis Testing Means and More (33 min.)

Optional Supplements

You can access an excellent free online textbook on OpenIntro Statistics here, co-authored by Duke faculty. You can pay a suggested but adjustable price for a tablet-friendly pdf, but you can also just get the regular pdf for free. For Module 3B, the following optional readings may be particularly helpful supplements:

• Chapter 5.2 Confidence intervals for a proportion. This provides introductory material on confidence intervals elaborating on 3B.A.1.
• Chapter 5.3 Hypothesis testing for a proportion. This elaborates on the introduction to hypothesis testing from 3B.B.1.
• Chapters 7.1, 7.3, and 7.5 cover material from 3B.B.2 on using t-tests for a single mean, the difference of two means, and many pairwise means respectively.
• Chapter 6.3 discusses the chi-square test for categorical data introduced in 3B.B.2.

In addition, here is the documentation for the scipy.stats library that implements most of the functionality described here as well as many other useful statistical functions.