Regression Models

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About 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.

Created by:  Johns Hopkins University
  • Taught by:  Brian …

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When you enroll for courses through Coursera you get to choose for a paid plan or for a free plan

  • Free plan: No certicification and/or audit only. You will have access to all course materials except graded items.
  • Paid plan: Commit to earning a Certificate—it's a trusted, shareable way to showcase your new skills.

About 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.

Created by:  Johns Hopkins University
  • Taught by:  Brian Caffo, PhD, Professor, Biostatistics

    Bloomberg School of Public Health
  • Taught by:  Roger D. Peng, PhD, Associate Professor, Biostatistics

    Bloomberg School of Public Health
  • Taught by:  Jeff Leek, PhD, Associate Professor, Biostatistics

    Bloomberg School of Public Health
Basic Info Course 7 of 10 in the Data Science Specialization Language English How To Pass Pass all graded assignments to complete the course. User Ratings 4.3 stars Average User Rating 4.3See what learners said Coursework

Each course is like an interactive textbook, featuring pre-recorded videos, quizzes and projects.

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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


WEEK 1


Week 1: Least Squares and Linear Regression
This week, we focus on least squares and linear regression.


9 videos, 11 readings expand


  1. Reading: Welcome to Regression Models
  2. Reading: Book: Regression Models for Data Science in R
  3. Reading: Syllabus
  4. Reading: Pre-Course Survey
  5. Reading: Data Science Specialization Community Site
  6. Reading: Where to get more advanced material
  7. Reading: Regression
  8. Video: Introduction to Regression
  9. Video: Introduction: Basic Least Squares
  10. Reading: Technical details
  11. Video: Technical Details (Skip if you'd like)
  12. Video: Introductory Data Example
  13. Reading: Least squares
  14. Video: Notation and Background
  15. Video: Linear Least Squares
  16. Video: Linear Least Squares Coding Example
  17. Video: Technical Details (Skip if you'd like)
  18. Reading: Regression to the mean
  19. Video: Regression to the Mean
  20. Reading: Practical R Exercises in swirl Part 1
  21. Ungraded Programming: swirl Lesson 1: Introduction
  22. Ungraded Programming: swirl Lesson 2: Residuals
  23. Ungraded Programming: swirl Lesson 3: Least Squares Estimation

Graded: Quiz 1

WEEK 2


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.


10 videos, 5 readings expand


  1. Reading: *Statistical* linear regression models
  2. Video: Statistical Linear Regression Models
  3. Video: Interpreting Coefficients
  4. Video: Linear Regression for Prediction
  5. Reading: Residuals
  6. Video: Residuals
  7. Video: Residuals, Coding Example
  8. Video: Residual Variance
  9. Reading: Inference in regression
  10. Video: Inference in Regression
  11. Video: Coding Example
  12. Video: Prediction
  13. Reading: Looking ahead to the project
  14. Video: Really, really quick intro to knitr
  15. Reading: Practical R Exercises in swirl Part 2
  16. Ungraded Programming: swirl Lesson 1: Residual Variation
  17. Ungraded Programming: swirl Lesson 2: Introduction to Multivariable Regression
  18. Ungraded Programming: swirl Lesson 3: MultiVar Examples

Graded: Quiz 2

WEEK 3


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 videos, 5 readings, 1 practice quiz expand


  1. Reading: Multivariable regression
  2. Video: Multivariable Regression part I
  3. Video: Multivariable Regression part II
  4. Video: Multivariable Regression Continued
  5. Video: Multivariable Regression Examples part I
  6. Video: Multivariable Regression Examples part II
  7. Video: Multivariable Regression Examples part III
  8. Video: Multivariable Regression Examples part IV
  9. Reading: Adjustment
  10. Video: Adjustment Examples
  11. Reading: Residuals
  12. Video: Residuals and Diagnostics part I
  13. Video: Residuals and Diagnostics part II
  14. Video: Residuals and Diagnostics part III
  15. Reading: Model selection
  16. Video: Model Selection part I
  17. Video: Model Selection part II
  18. Video: Model Selection part III
  19. Reading: Practical R Exercises in swirl Part 3
  20. Ungraded Programming: swirl Lesson 1: MultiVar Examples2
  21. Ungraded Programming: swirl Lesson 2: MultiVar Examples3
  22. Ungraded Programming: swirl Lesson 3: Residuals Diagnostics and Variation
  23. Practice Quiz: (OPTIONAL) Regression practice

Graded: Quiz 3

WEEK 4


Week 4: Logistic Regression and Poisson Regression
This week, we will work on generalized linear models, including binary outcomes and Poisson regression.


7 videos, 6 readings expand


  1. Reading: GLMs
  2. Video: GLMs
  3. Reading: Logistic regression
  4. Video: Logistic Regression part I
  5. Video: Logistic Regression part II
  6. Video: Logistic Regression part III
  7. Reading: Count Data
  8. Video: Poisson Regression part I
  9. Video: Poisson Regression part II
  10. Reading: Mishmash
  11. Video: Hodgepodge
  12. Reading: Practical R Exercises in swirl Part 4
  13. Ungraded Programming: swirl Lesson 1: Variance Inflation Factors
  14. Ungraded Programming: swirl Lesson 2: Overfitting and Underfitting
  15. Ungraded Programming: swirl Lesson 3: Binary Outcomes
  16. Ungraded Programming: swirl Lesson 4: Count Outcomes
  17. Reading: Post-Course Survey

Graded: Quiz 4
Graded: Regression Models Course Project

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