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.
Offered By
Johns Hopkins University
About this Course
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Learner Career Outcomes
23%
started a new career after completing these courses
25%
got a tangible career benefit from this course
14%
got a pay increase or promotion
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
Learner Career Outcomes
23%
started a new career after completing these courses
25%
got a tangible career benefit from this course
14%
got a pay increase or promotion
100% online
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Approx. 17 hours to complete
English
Subtitles: English, Korean, Vietnamese
Syllabus - What you will learn from this course
12 hours to complete
Week 1: Least Squares and Linear Regression
This week, we focus on least squares and linear regression.
12 hours to complete
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
11 hours to complete
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.
11 hours to complete
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
13 hours to complete
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.
13 hours to complete
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
17 hours to complete
Week 4: Logistic Regression and Poisson Regression
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.
17 hours to complete
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
477 ReviewsTop reviews from Regression Models
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.
It really helped me to have a better understanding of these Regression Models. However, I've noticed that there is a video recording repeated: Week 3, Model Selection. Part 3 is included in Part 2.
Instructors
Brian Caffo, PhD
Professor, BiostatisticsBloomberg School of Public Health
901,944 Learners
21 Courses
Roger D. Peng, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
886,577 Learners22 Courses
Jeff Leek, PhD
Associate Professor, BiostatisticsBloomberg School of Public Health
895,478 Learners20 CoursesOffered by
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.
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