Regression Models

4.4
1,880 ratings
336 reviews

Course 7 of 10 in the Data Science Specialization

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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Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Clock

Approx. 14 hours to complete

Suggested: 4 hours/week
CommentDots

English

Sous-titres : English

Ce que vous allez apprendre

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

Compétences que vous acquerrez

Linear RegressionGeneralized Linear ModelR ProgrammingLogistic Regression
Globe

Cours en ligne à 100 %

Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Clock

Approx. 14 hours to complete

Suggested: 4 hours/week
CommentDots

English

Sous-titres : English

Syllabus - What you will learn from this course

1

Section
Clock
12 hours to complete

Week 1: Least Squares and Linear Regression

This week, we focus on least squares and linear regression....
SvgReading
9 videos (Total 74 min), 11 readings, 4 quizzes
Video9 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
SvgReading11 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
Quiz1 practice exercises
Quiz 120m

2

Section
Clock
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....
SvgReading
10 videos (Total 70 min), 5 readings, 4 quizzes
Video10 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
SvgReading5 readings
*Statistical* linear regression models10m
Residuals10m
Inference in regression10m
Looking ahead to the project10m
Practical R Exercises in swirl Part 210m
Quiz1 practice exercises
Quiz 220m

3

Section
Clock
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. ...
SvgReading
14 videos (Total 168 min), 5 readings, 5 quizzes
Video14 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
SvgReading5 readings
Multivariable regression10m
Adjustment10m
Residuals10m
Model selection10m
Practical R Exercises in swirl Part 310m
Quiz2 practice exercises
Quiz 314m
(OPTIONAL) Data analysis practice with immediate feedback (NEW! 10/18/2017)8m

4

Section
Clock
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. ...
SvgReading
7 videos (Total 95 min), 6 readings, 6 quizzes
Video7 videos
GLMs21m
Logistic Regression part I17m
Logistic Regression part II3m
Logistic Regression part III8m
Poisson Regression part I12m
Poisson Regression part II12m
Hodgepodge18m
SvgReading6 readings
GLMs10m
Logistic regression10m
Count Data10m
Mishmash10m
Practical R Exercises in swirl Part 410m
Post-Course Survey10m
Quiz1 practice exercises
Quiz 412m
4.4
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83%

got a tangible career benefit from this course
Money

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Top Reviews

By MMMar 13th 2018

Great course, very informative, with lots of valuable information and examples. Prof. Caffo and his team did a very good job in my opinion. I've found very useful the course material shared on github.

By KADec 17th 2017

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.

Instructors

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Brian Caffo, PhD

Professor, Biostatistics
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Roger D. Peng, PhD

Associate Professor, Biostatistics
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Jeff Leek, PhD

Associate Professor, Biostatistics

About 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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