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Regression Models
  • Level Foundation
  • Duration 54 hours
  • Course by Johns Hopkins University
  • Offered by

About

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.

Modules

Introduction

    • 6 Readings
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6 Readings

  • Welcome to Regression Models
  • Book: Regression Models for Data Science in R
  • Syllabus
  • Pre-Course Survey
  • Data Science Specialization Community Site
  • Where to get more advanced material

Introduction to regression and least squares

    • 4 Videos
    • 2 Readings
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4 Videos

  • Introduction to Regression
  • Introduction: Basic Least Squares
  • Technical Details (Skip if you'd like)
  • Introductory Data Example

2 Readings

  • Regression
  • Technical details

Linear least squares

    • 4 Videos
    • 1 Readings
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4 Videos

  • Notation and Background
  • Linear Least Squares
  • Linear Least Squares Coding Example
  • Technical Details (Skip if you'd like)

1 Readings

  • Least squares

Regression to the Mean

    • 1 Videos
    • 1 Readings
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1 Videos

  • Regression to the Mean

1 Readings

  • Regression to the mean

Practical R Exercises in swirl

    • 1 Readings
    • 3 Programming
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3 Programming

  • swirl Lesson 1: Introduction
  • swirl Lesson 2: Residuals
  • swirl Lesson 3: Least Squares Estimation

1 Readings

  • Practical R Exercises in swirl Part 1

Week 1 Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 1

Statistical linear regression models

    • 3 Videos
    • 1 Readings
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3 Videos

  • Statistical Linear Regression Models
  • Interpreting Coefficients
  • Linear Regression for Prediction

1 Readings

  • *Statistical* linear regression models

Residuals

    • 3 Videos
    • 1 Readings
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3 Videos

  • Residuals
  • Residuals, Coding Example
  • Residual Variance

1 Readings

  • Residuals

Inference in regression

    • 3 Videos
    • 1 Readings
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3 Videos

  • Inference in Regression
  • Coding Example
  • Prediction

1 Readings

  • Inference in regression

For the project

    • 1 Videos
    • 1 Readings
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1 Videos

  • Really, really quick intro to knitr

1 Readings

  • Looking ahead to the project

Practical R Exercises in swirl

    • 1 Readings
    • 3 Programming
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3 Programming

  • swirl Lesson 1: Residual Variation
  • swirl Lesson 2: Introduction to Multivariable Regression
  • swirl Lesson 3: MultiVar Examples

1 Readings

  • Practical R Exercises in swirl Part 2

Week 2 Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 2

Multivariable regression

    • 3 Videos
    • 1 Readings
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3 Videos

  • Multivariable Regression part I
  • Multivariable Regression part II
  • Multivariable Regression Continued

1 Readings

  • Multivariable regression

Multivariable regression tips and tricks

    • 4 Videos
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4 Videos

  • Multivariable Regression Examples part I
  • Multivariable Regression Examples part II
  • Multivariable Regression Examples part III
  • Multivariable Regression Examples part IV

Adjustment

    • 1 Videos
    • 1 Readings
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1 Videos

  • Adjustment Examples

1 Readings

  • Adjustment

Residuals again

    • 3 Videos
    • 1 Readings
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3 Videos

  • Residuals and Diagnostics part I
  • Residuals and Diagnostics part II
  • Residuals and Diagnostics part III

1 Readings

  • Residuals

Model selection

    • 3 Videos
    • 1 Readings
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3 Videos

  • Model Selection part I
  • Model Selection part II
  • Model Selection part III

1 Readings

  • Model selection

Practical R Exercises in swirl

    • 1 Readings
    • 3 Programming
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3 Programming

  • swirl Lesson 1: MultiVar Examples2
  • swirl Lesson 2: MultiVar Examples3
  • swirl Lesson 3: Residuals Diagnostics and Variation

1 Readings

  • Practical R Exercises in swirl Part 3

Week 3 Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 3

(OPTIONAL) Practice exercise in regression modeling

    • 1 Assignment
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1 Assignment

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

Week 4 New Videos

    • 1 Videos
    • 1 Readings
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1 Videos

  • GLMs

1 Readings

  • GLMs

Logistic Regression

    • 3 Videos
    • 1 Readings
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3 Videos

  • Logistic Regression part I
  • Logistic Regression part II
  • Logistic Regression part III

1 Readings

  • Logistic regression

Poisson Regression

    • 2 Videos
    • 1 Readings
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2 Videos

  • Poisson Regression part I
  • Poisson Regression part II

1 Readings

  • Count Data

Hodgepodge

    • 1 Videos
    • 1 Readings
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1 Videos

  • Hodgepodge

1 Readings

  • Mishmash

Practical R Exercises in swirl

    • 1 Readings
    • 4 Programming
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4 Programming

  • swirl Lesson 1: Variance Inflation Factors
  • swirl Lesson 2: Overfitting and Underfitting
  • swirl Lesson 3: Binary Outcomes
  • swirl Lesson 4: Count Outcomes

1 Readings

  • Practical R Exercises in swirl Part 4

Week 4 Quiz

    • 1 Assignment
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1 Assignment

  • Quiz 4

Course Project

    • 1 PeerReview
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1 Peer Review

  • Regression Models Course Project

Share Your Feedback

    • 1 Readings
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1 Readings

  • Post-Course Survey

Auto Summary

Discover the essentials of regression models in this foundational Data Science & AI course by Coursera. Learn key techniques like regression analysis, least squares, and inference, along with ANOVA, ANCOVA, and modern model selection. Perfect for aspiring data scientists, this 3240-minute course offers a comprehensive dive into one of the most crucial statistical tools. Enroll with a Starter subscription and elevate your data analysis skills.

Brian Caffo, PhD
Instructors

Brian Caffo, PhD

Roger D. Peng, PhD
Instructors

Roger D. Peng, PhD

Jeff Leek, PhD
Instructors

Jeff Leek, PhD