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 |
Syllabus
WEEK 1
Week 1: Least Squares and Linear Regression
This week, we focus on least squares and linear regression.
9 videos, 11 readings
- Reading: Welcome to Regression Models
- Reading: Book: Regression Models for Data Science in R
- Reading: Syllabus
- Reading: Pre-Course Survey
- Reading: Data Science Specialization Community Site
- Reading: Where to get more advanced material
- Reading: Regression
- Video: Introduction to Regression
- Video: Introduction: Basic Least Squares
- Reading: Technical details
- Video: Technical Details (Skip if you'd like)
- Video: Introductory Data Example
- Reading: Least squares
- Video: Notation and Background
- Video: Linear Least Squares
- Video: Linear Least Squares Coding Example
- Video: Technical Details (Skip if you'd like)
- Reading: Regression to the mean
- Video: Regression to the Mean
- Reading: Practical R Exercises in swirl Part 1
- Ungraded Programming: swirl Lesson 1: Introduction
- Ungraded Programming: swirl Lesson 2: Residuals
- 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
- Reading: *Statistical* linear regression models
- Video: Statistical Linear Regression Models
- Video: Interpreting Coefficients
- Video: Linear Regression for Prediction
- Reading: Residuals
- Video: Residuals
- Video: Residuals, Coding Example
- Video: Residual Variance
- Reading: Inference in regression
- Video: Inference in Regression
- Video: Coding Example
- Video: Prediction
- Reading: Looking ahead to the project
- Video: Really, really quick intro to knitr
- Reading: Practical R Exercises in swirl Part 2
- Ungraded Programming: swirl Lesson 1: Residual Variation
- Ungraded Programming: swirl Lesson 2: Introduction to Multivariable Regression
- 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
- Reading: Multivariable regression
- Video: Multivariable Regression part I
- Video: Multivariable Regression part II
- Video: Multivariable Regression Continued
- Video: Multivariable Regression Examples part I
- Video: Multivariable Regression Examples part II
- Video: Multivariable Regression Examples part III
- Video: Multivariable Regression Examples part IV
- Reading: Adjustment
- Video: Adjustment Examples
- Reading: Residuals
- Video: Residuals and Diagnostics part I
- Video: Residuals and Diagnostics part II
- Video: Residuals and Diagnostics part III
- Reading: Model selection
- Video: Model Selection part I
- Video: Model Selection part II
- Video: Model Selection part III
- Reading: Practical R Exercises in swirl Part 3
- Ungraded Programming: swirl Lesson 1: MultiVar Examples2
- Ungraded Programming: swirl Lesson 2: MultiVar Examples3
- Ungraded Programming: swirl Lesson 3: Residuals Diagnostics and Variation
- 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
- Reading: GLMs
- Video: GLMs
- Reading: Logistic regression
- Video: Logistic Regression part I
- Video: Logistic Regression part II
- Video: Logistic Regression part III
- Reading: Count Data
- Video: Poisson Regression part I
- Video: Poisson Regression part II
- Reading: Mishmash
- Video: Hodgepodge
- Reading: Practical R Exercises in swirl Part 4
- Ungraded Programming: swirl Lesson 1: Variance Inflation Factors
- Ungraded Programming: swirl Lesson 2: Overfitting and Underfitting
- Ungraded Programming: swirl Lesson 3: Binary Outcomes
- Ungraded Programming: swirl Lesson 4: Count Outcomes
- Reading: Post-Course Survey
Graded: Quiz 4
Graded: Regression Models Course Project
FAQs
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Johns Hopkins University
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
Pricing
| Audit | Purchase Course | |
|---|---|---|
| Access to course materials | Available | Available |
| Access to graded materials | Not available | Available |
| Receive a final grade | Not available | Available |
| Earn a shareable Course Certificate | Not available | Available |
Ratings and Reviews
Regression analysis is something that is kind of easy for people to understand (outcome and predictor - people get that!). It's easy to explain to people. So much practice using the lm function!
Knowledge on Statistics will help in better understanding.
The course is comprehensive in its presentation. Ideas can be easily grasp and replicated.
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BF
This a challenging course, overall I think it was good, but the material could be a bit better presented.