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.
This course is part of the Data Science Specialization
Regression Models
Offered By
About this Course
4.4
2,268 ratings
What you will learn
Describe novel uses of regression models such as scatterplot smoothing
Investigate analysis of residuals and variability
Understand ANOVA and ANCOVA model cases
Use regression analysis, least squares and inference
Skills you will gain
Model SelectionGeneralized Linear ModelLinear RegressionRegression Analysis
Syllabus - What you will learn from this course
Week
1Week 1: Least Squares and Linear Regression
This week, we focus on least squares and linear regression.
9 videos (Total 74 min), 11 readings, 4 quizzes
9 videos
Introduction: Basic Least Squares6m
Technical Details (Skip if you'd like)2m
Introductory Data Example12m
Notation and Background7m
Linear Least Squares6m
Linear Least Squares Coding Example7m
Technical Details (Skip if you'd like)11m
Regression to the Mean11m
11 readings
Welcome to Regression Models10m
Book: Regression Models for Data Science in R10m
Syllabus10m
Pre-Course Survey10m
Data Science Specialization Community Site10m
Where to get more advanced material10m
Regression10m
Technical details10m
Least squares10m
Regression to the mean10m
Practical R Exercises in swirl Part 110m
1 practice exercise
Quiz 120m
Week
2Week 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 (Total 70 min), 5 readings, 4 quizzes
10 videos
Interpreting Coefficients3m
Linear Regression for Prediction10m
Residuals5m
Residuals, Coding Example14m
Residual Variance7m
Inference in Regression5m
Coding Example6m
Prediction9m
Really, really quick intro to knitr3m
5 readings
*Statistical* linear regression models10m
Residuals10m
Inference in regression10m
Looking ahead to the project10m
Practical R Exercises in swirl Part 210m
1 practice exercise
Quiz 220m
Week
3Week 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 (Total 168 min), 5 readings, 5 quizzes
14 videos
Multivariable Regression part II10m
Multivariable Regression Continued8m
Multivariable Regression Examples part I19m
Multivariable Regression Examples part II22m
Multivariable Regression Examples part III7m
Multivariable Regression Examples part IV7m
Adjustment Examples17m
Residuals and Diagnostics part I5m
Residuals and Diagnostics part II9m
Residuals and Diagnostics part III9m
Model Selection part I7m
Model Selection part II22m
Model Selection part III12m
5 readings
Multivariable regression10m
Adjustment10m
Residuals10m
Model selection10m
Practical R Exercises in swirl Part 310m
2 practice exercises
Quiz 314m
(OPTIONAL) Data analysis practice with immediate feedback (NEW! 10/18/2017)8m
Week
4Week 4: Logistic Regression and Poisson Regression
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.
7 videos (Total 95 min), 6 readings, 6 quizzes
7 videos
GLMs21m
Logistic Regression part I17m
Logistic Regression part II3m
Logistic Regression part III8m
Poisson Regression part I12m
Poisson Regression part II12m
Hodgepodge18m
6 readings
GLMs10m
Logistic regression10m
Count Data10m
Mishmash10m
Practical R Exercises in swirl Part 410m
Post-Course Survey10m
1 practice exercise
Quiz 412m
4.4
403 Reviews22%
83%
14%
Top Reviews
By MM•Mar 13th 2018
Great course, very informative, with lots of valuable information and examples. Prof. Caffo and his team did a very good job in my opinion. I've found very useful the course material shared on github.
By KA•Dec 17th 2017
Excellent course that is jam-packed with useful material! It is quite challenging and gives a thorough grounding in how to approach the process of selecting a linear regression model for a data set.
Instructors
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
About 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.
About the Data Science Specialization
Ask the right questions, manipulate data sets, and create visualizations to communicate results.
This Specialization covers the concepts and tools you'll need throughout the entire data science pipeline, from asking the right kinds of questions to making inferences and publishing results. In the final Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, students will have a portfolio demonstrating their mastery of the material.
Frequently Asked Questions
When will I have access to the lectures and assignments?
Once you enroll for a Certificate, you’ll have access to all videos, quizzes, and programming assignments (if applicable). Peer review assignments can only be submitted and reviewed once your session has begun. If you choose to explore the course without purchasing, you may not be able to access certain assignments.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
What is the refund policy?
Is financial aid available?
More questions? Visit the Learner Help Center.
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