4.4
stars
3,307 ratings

Brian Caffo, PhD +2 more instructors

136,029 already enrolled

Offered By

Johns Hopkins University

About this Course

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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.

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Earn a Certificate upon completion

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Approx. 54 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish

What you will learn

Use regression analysis, least squares and inference
Understand ANOVA and ANCOVA model cases
Investigate analysis of residuals and variability
Describe novel uses of regression models such as scatterplot smoothing

Skills you will gain

Model Selection
Generalized Linear Model
Linear Regression
Regression Analysis

Flexible deadlines

Reset deadlines in accordance to your schedule.

Shareable Certificate

Earn a Certificate upon completion

100% online

Start instantly and learn at your own schedule.

Approx. 54 hours to complete

English

Subtitles: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish

Instructors

Instructor rating

4.51/5 (98 Ratings)

Offered 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.

Syllabus - What you will learn from this course

Content Rating92%(11,734 ratings)

Week

Week 1

13 hours to complete

Week 1: Least Squares and Linear Regression

This week, we focus on least squares and linear regression.

13 hours to complete

9 videos (Total 74 min), 11 readings, 4 quizzes

9 videos

Introduction to Regression6m

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 130m

Week

Week 2

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

Statistical Linear Regression Models2m

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 230m

Week

Week 3

14 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.

14 hours to complete

14 videos (Total 168 min), 5 readings, 5 quizzes

14 videos

Multivariable Regression part I9m

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 330m

(OPTIONAL) Data analysis practice with immediate feedback (NEW! 10/18/2017)30m

Week

Week 4

16 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.

16 hours to complete

7 videos (Total 95 min), 6 readings, 6 quizzes

Reviews

4.4

568 reviews

5 stars
64.24%
4 stars
23.05%
3 stars
7.55%
2 stars
2.99%
1 star
2.14%

TOP REVIEWS FROM REGRESSION MODELS

by JVOct 15, 2017

It is very interesting, however is difficult to follow the math explanations, it could be more easy with practical examples.... like the final assignment, it was difficult to me.

by CJJan 3, 2018

The best course in my mind, but I am chocked about how Data Science people approach regression type of problems, it is almost 100% data mining and no theory!! I wonder where it will take us..

by AKDec 9, 2016

I was hoping to learn about PROBIT models. I know they are very similar to LOGIT ones, but still... the pace is a little bit too fast and I think it requires more time than what it says.

by BKFeb 9, 2016

This was a tough class covering a lot of material. The last week on logistic regression completely lost me. If you're new to stats like me you might want to take it more than once.

View all reviews

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Regression Models