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Regression Models

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Gain insight into a topic and learn the fundamentals.
4.4

3,376 reviews

5 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
93%
Most learners liked this course
Gain insight into a topic and learn the fundamentals.
4.4

3,376 reviews

5 weeks to complete
at 10 hours a week
Flexible schedule
Learn at your own pace
93%
Most learners liked this course

What you'll 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

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Assessments

5 assignments¹

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Taught in English

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There are 4 modules in 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.

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

What's included

9 videos11 readings1 assignment3 programming assignments

9 videos•Total 74 minutes
  • Introduction to Regression•6 minutes
  • Introduction: Basic Least Squares•6 minutes
  • Technical Details (Skip if you'd like)•3 minutes
  • Introductory Data Example•12 minutes
  • Notation and Background•8 minutes
  • Linear Least Squares•7 minutes
  • Linear Least Squares Coding Example•8 minutes
  • Technical Details (Skip if you'd like)•12 minutes
  • Regression to the Mean•12 minutes
11 readings•Total 110 minutes
  • Welcome to Regression Models•10 minutes
  • Book: Regression Models for Data Science in R•10 minutes
  • Syllabus•10 minutes
  • Pre-Course Survey•10 minutes
  • Data Science Specialization Community Site•10 minutes
  • Where to get more advanced material•10 minutes
  • Regression•10 minutes
  • Technical details•10 minutes
  • Least squares•10 minutes
  • Regression to the mean•10 minutes
  • Practical R Exercises in swirl Part 1•10 minutes
1 assignment•Total 20 minutes
  • Quiz 1•20 minutes
3 programming assignments•Total 540 minutes
  • swirl Lesson 1: Introduction•180 minutes
  • swirl Lesson 2: Residuals•180 minutes
  • swirl Lesson 3: Least Squares Estimation•180 minutes

This week, we will work through the remainder of linear regression and then turn to the first part of multivariable regression.

What's included

10 videos5 readings1 assignment3 programming assignments

10 videos•Total 70 minutes
  • Statistical Linear Regression Models•3 minutes
  • Interpreting Coefficients•4 minutes
  • Linear Regression for Prediction•11 minutes
  • Residuals•6 minutes
  • Residuals, Coding Example•14 minutes
  • Residual Variance•7 minutes
  • Inference in Regression•5 minutes
  • Coding Example•7 minutes
  • Prediction•10 minutes
  • Really, really quick intro to knitr•4 minutes
5 readings•Total 50 minutes
  • *Statistical* linear regression models•10 minutes
  • Residuals•10 minutes
  • Inference in regression•10 minutes
  • Looking ahead to the project•10 minutes
  • Practical R Exercises in swirl Part 2•10 minutes
1 assignment•Total 30 minutes
  • Quiz 2•30 minutes
3 programming assignments•Total 540 minutes
  • swirl Lesson 1: Residual Variation•180 minutes
  • swirl Lesson 2: Introduction to Multivariable Regression•180 minutes
  • swirl Lesson 3: MultiVar Examples•180 minutes

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.

What's included

14 videos5 readings2 assignments3 programming assignments

14 videos•Total 168 minutes
  • Multivariable Regression part I•9 minutes
  • Multivariable Regression part II•11 minutes
  • Multivariable Regression Continued•8 minutes
  • Multivariable Regression Examples part I•19 minutes
  • Multivariable Regression Examples part II•22 minutes
  • Multivariable Regression Examples part III•7 minutes
  • Multivariable Regression Examples part IV•7 minutes
  • Adjustment Examples•18 minutes
  • Residuals and Diagnostics part I•5 minutes
  • Residuals and Diagnostics part II•9 minutes
  • Residuals and Diagnostics part III•9 minutes
  • Model Selection part I•7 minutes
  • Model Selection part II•23 minutes
  • Model Selection part III•12 minutes
5 readings•Total 50 minutes
  • Multivariable regression•10 minutes
  • Adjustment•10 minutes
  • Residuals•10 minutes
  • Model selection•10 minutes
  • Practical R Exercises in swirl Part 3•10 minutes
2 assignments•Total 60 minutes
  • (OPTIONAL) Data analysis practice with immediate feedback (NEW! 10/18/2017)•30 minutes
  • Quiz 3•30 minutes
3 programming assignments•Total 540 minutes
  • swirl Lesson 1: MultiVar Examples2•180 minutes
  • swirl Lesson 2: MultiVar Examples3•180 minutes
  • swirl Lesson 3: Residuals Diagnostics and Variation•180 minutes

This week, we will work on generalized linear models, including binary outcomes and Poisson regression.

What's included

7 videos6 readings1 assignment4 programming assignments1 peer review

7 videos•Total 95 minutes
  • GLMs•21 minutes
  • Logistic Regression part I•18 minutes
  • Logistic Regression part II•4 minutes
  • Logistic Regression part III•9 minutes
  • Poisson Regression part I•13 minutes
  • Poisson Regression part II•13 minutes
  • Hodgepodge•19 minutes
6 readings•Total 60 minutes
  • GLMs•10 minutes
  • Logistic regression•10 minutes
  • Count Data•10 minutes
  • Mishmash•10 minutes
  • Practical R Exercises in swirl Part 4•10 minutes
  • Post-Course Survey•10 minutes
1 assignment•Total 30 minutes
  • Quiz 4•30 minutes
4 programming assignments•Total 720 minutes
  • swirl Lesson 1: Variance Inflation Factors•180 minutes
  • swirl Lesson 2: Overfitting and Underfitting•180 minutes
  • swirl Lesson 3: Binary Outcomes•180 minutes
  • swirl Lesson 4: Count Outcomes•180 minutes
1 peer review•Total 60 minutes
  • Regression Models Course Project•60 minutes

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Instructors

Instructor ratings
4.5 (108 ratings)
Johns Hopkins University
30 Courses•1,718,479 learners

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Learner reviews

  • 5 stars

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  • 4 stars

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Showing 3 of 3376

DJ
·

Reviewed on Aug 1, 2017

Great introductory course on Regression Models. Super practical and well explained. Definitely doing the exercises and final project is a must to get all the learnings!

SC
·

Reviewed on Mar 14, 2017

Good course on the theories behind regression, followed by significant applications and how to use them in R. Lectures are very dry, but the information within them is very useful.

JV
·

Reviewed on Oct 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.

Frequently asked questions

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.