#1 Specialization

Launch Your Career in Data Science. A nine-course introduction to data science, developed and taught by leading professors.

Johns Hopkins University

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.

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

Linear RegressionGeneralized Linear ModelR ProgrammingLogistic Regression

Section

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

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

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

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

Quiz 120m

Section

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

10 videos (Total 70 min), 5 readings, 4 quizzes

Interpreting Coefficients3m

Linear Regression for Prediction10m

Residuals5m

Residuals, Coding Example14m

Residual Variance7m

Inference in Regression5m

Coding Example6m

Prediction9m

Really, really quick intro to knitr3m

*Statistical* linear regression models10m

Residuals10m

Inference in regression10m

Looking ahead to the project10m

Practical R Exercises in swirl Part 210m

Quiz 220m

Section

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 (Total 168 min), 5 readings, 5 quizzes

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

Multivariable regression10m

Adjustment10m

Residuals10m

Model selection10m

Practical R Exercises in swirl Part 310m

Quiz 314m

(OPTIONAL) Data analysis practice with immediate feedback (NEW! 10/18/2017)8m

Section

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

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

GLMs21m

Logistic Regression part I17m

Logistic Regression part II3m

Logistic Regression part III8m

Poisson Regression part I12m

Poisson Regression part II12m

Hodgepodge18m

GLMs10m

Logistic regression10m

Count Data10m

Mishmash10m

Practical R Exercises in swirl Part 410m

Post-Course Survey10m

Quiz 412m

4.4

started a new career

got a tangible career benefit from this course

got a pay increase or promotion

By MM•Mar 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 KA•Dec 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.

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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