- Level Foundation
- Duration 11 hours
- Course by Wesleyan University
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Offered by
About
This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you.Modules
Observational and Experimental Data
2
Videos
- Lesson 1: Observational Data
- Lesson 2: Experimental Data
1
Readings
- Some Guidance for Learners New to the Specialization
Confounding Variables
1
Videos
- Lesson 3: Confounding Variables
Intro to Multivariate Methods
1
Videos
- Lesson 4: Introduction to Multivariate Methods
Assignment
1
Peer Review
- Writing About Your Data
4
Readings
- Getting Set up for Assignments
- Tumblr Instructions
- How to Write About Data
- Writing About Your Data: Example Assignment
Software Setup and Course Supporting Materials
6
Readings
- SAS or Python - Which to Choose?
- Getting Started with SAS
- Getting Started with Python
- Course Codebooks
- Course Data Sets
- Uploading Your Own Data to SAS
SAS Lessons
3
Videos
- SAS Lesson 1: More on Confounding Variables
- SAS Lesson 2: Testing a Basic Linear Regression Mode
- SAS Lesson 3: Categorical Explanatory Variables
1
Readings
- SAS Program Code for Video Examples
Python Lessons
3
Videos
- Python Lesson 1: More on Confounding Variables
- Python Lesson 2: Testing a Basic Linear Regression Model
- Python Lesson 3: Categorical Explanatory Variables
1
Readings
- Python Program Code for Video Examples
Linear Regression Assumptions
1
Videos
- Lesson 4: Linear Regression Assumptions
1
Readings
- Outlier Decision Tree
Centering Explanatory Variables
1
Videos
- Lesson 5: Centering Explanatory Variables
Assignment
1
Peer Review
- Test a Basic Linear Regression Model
SAS Lessons
5
Videos
- SAS Lesson 1: Multiple Regression
- SAS Lesson 2: Confidence Intervals
- SAS Lesson 3: Polynomial Regression
- SAS Lesson 4: Evaluating Model Fit, pt. 1
- SAS Lesson 5: Evaluating Model Fit, pt. 2
1
Readings
- SAS Program Code for Video Examples
Python Lessons
5
Videos
- Python Lesson 1: Multiple Regression
- Python Lesson 2: Confidence Intervals
- Python Lesson 3: Polynomial Regression
- Python Lesson 4: Evaluating Model Fit, pt. 1
- Python Lesson 5: Evaluating Model Fit, pt. 2
1
Readings
- Python Program Code for Video Examples
Assignment
1
Peer Review
- Test a Multiple Regression Model
SAS Lesson
1
Videos
- SAS Lesson 1: Categorical Explanatory Variables with More Than Two Categories
1
Readings
- SAS Program Code for Video Examples
Python Lesson
1
Videos
- Python Lesson 1: Categorical Explanatory Variables with More Than Two Categories
1
Readings
- Python Program Code for Video Examples
A Few Things to Keep in Mind
1
Videos
- Lesson 2: A Few Things to Keep in Mind
SAS Videos
2
Videos
- SAS Lesson 3: Logistic Regression for a Binary Response Variable, pt 1
- SAS Lesson 4: Logistic Regression for a Binary Response Variable, pt. 2
Python Videos
2
Videos
- Python Lesson 3: Logistic Regression for a Binary Response Variable, pt. 1
- Python Lesson 4: Logistic Regression for a Binary Response Variable, pt. 2
Assignment
1
Peer Review
- Test a Logistic Regression Model
Video credits
4
Readings
- Week 1 Video Credits
- Week 2 Video Credits
- Week 3 Video Credits
- Week 4 Video Credits

Jen Rose

Lisa Dierker