Regression Models

About this course: Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing.

Created by:  Johns Hopkins University

• Taught by:  Brian Caffo, PhD, Professor, Biostatistics

  Bloomberg School of Public Health

• Taught by:  Roger D. Peng, PhD, Associate Professor, Biostatistics

  Bloomberg School of Public Health

• Taught by:  Jeff Leek, PhD, Associate Professor, Biostatistics

  Bloomberg School of Public Health

Basic Info Course 7 of 10 in the Data Science Specialization Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.3 stars Average User Rating 4.3See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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Syllabus


WEEK 1


Week 1: Least Squares and Linear Regression
This week, we focus on least squares and linear regression.


9 videos, 11 readings expand

1. Reading: Welcome to Regression Models
2. Reading: Book: Regression Models for Data Science in R
3. Reading: Syllabus
4. Reading: Pre-Course Survey
5. Reading: Data Science Specialization Community Site
6. Reading: Where to get more advanced material
7. Reading: Regression
8. Video: Introduction to Regression
9. Video: Introduction: Basic Least Squares
10. Reading: Technical details
11. Video: Technical Details (Skip if you'd like)
12. Video: Introductory Data Example
13. Reading: Least squares
14. Video: Notation and Background
15. Video: Linear Least Squares
16. Video: Linear Least Squares Coding Example
17. Video: Technical Details (Skip if you'd like)
18. Reading: Regression to the mean
19. Video: Regression to the Mean
20. Reading: Practical R Exercises in swirl Part 1
21. Ungraded Programming: swirl Lesson 1: Introduction
22. Ungraded Programming: swirl Lesson 2: Residuals
23. Ungraded Programming: swirl Lesson 3: Least Squares Estimation

Graded: Quiz 1

WEEK 2


Week 2: Linear Regression & Multivariable Regression
This week, we will work through the remainder of linear regression and then turn to the first part of multivariable regression.


10 videos, 5 readings expand

1. Reading: *Statistical* linear regression models
2. Video: Statistical Linear Regression Models
3. Video: Interpreting Coefficients
4. Video: Linear Regression for Prediction
5. Reading: Residuals
6. Video: Residuals
7. Video: Residuals, Coding Example
8. Video: Residual Variance
9. Reading: Inference in regression
10. Video: Inference in Regression
11. Video: Coding Example
12. Video: Prediction
13. Reading: Looking ahead to the project
14. Video: Really, really quick intro to knitr
15. Reading: Practical R Exercises in swirl Part 2
16. Ungraded Programming: swirl Lesson 1: Residual Variation
17. Ungraded Programming: swirl Lesson 2: Introduction to Multivariable Regression
18. Ungraded Programming: swirl Lesson 3: MultiVar Examples

Graded: Quiz 2

WEEK 3


Week 3: Multivariable Regression, Residuals, & Diagnostics
This week, we'll build on last week's introduction to multivariable regression with some examples and then cover residuals, diagnostics, variance inflation, and model comparison.


14 videos, 5 readings, 1 practice quiz expand

1. Reading: Multivariable regression
2. Video: Multivariable Regression part I
3. Video: Multivariable Regression part II
4. Video: Multivariable Regression Continued
5. Video: Multivariable Regression Examples part I
6. Video: Multivariable Regression Examples part II
7. Video: Multivariable Regression Examples part III
8. Video: Multivariable Regression Examples part IV
9. Reading: Adjustment
10. Video: Adjustment Examples
11. Reading: Residuals
12. Video: Residuals and Diagnostics part I
13. Video: Residuals and Diagnostics part II
14. Video: Residuals and Diagnostics part III
15. Reading: Model selection
16. Video: Model Selection part I
17. Video: Model Selection part II
18. Video: Model Selection part III
19. Reading: Practical R Exercises in swirl Part 3
20. Ungraded Programming: swirl Lesson 1: MultiVar Examples2
21. Ungraded Programming: swirl Lesson 2: MultiVar Examples3
22. Ungraded Programming: swirl Lesson 3: Residuals Diagnostics and Variation
23. Practice Quiz: (OPTIONAL) Regression practice

Graded: Quiz 3

WEEK 4


Week 4: Logistic Regression and Poisson Regression
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.


7 videos, 6 readings expand

1. Reading: GLMs
2. Video: GLMs
3. Reading: Logistic regression
4. Video: Logistic Regression part I
5. Video: Logistic Regression part II
6. Video: Logistic Regression part III
7. Reading: Count Data
8. Video: Poisson Regression part I
9. Video: Poisson Regression part II
10. Reading: Mishmash
11. Video: Hodgepodge
12. Reading: Practical R Exercises in swirl Part 4
13. Ungraded Programming: swirl Lesson 1: Variance Inflation Factors
14. Ungraded Programming: swirl Lesson 2: Overfitting and Underfitting
15. Ungraded Programming: swirl Lesson 3: Binary Outcomes
16. Ungraded Programming: swirl Lesson 4: Count Outcomes
17. Reading: Post-Course Survey

Graded: Quiz 4
Graded: Regression Models Course Project