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.
Regression Models
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Regression Models
This course is part of multiple programs.
Instructors: Brian Caffo, PhD
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4.4
(3,358 reviews)
53 hours to complete
3 weeks at 17 hours a week
Flexible schedule
Learn at your own pace
92%
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
Details to know
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Assessments
5 assignments
Taught in English
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There are 4 modules in this course
This week, we focus on least squares and linear regression.
What's included
9 videos11 readings1 assignment3 programming assignments
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
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
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
Instructors
Instructor ratings
4.5 (105 ratings)Offered by
Recommended if you're interested in Probability and Statistics
University of Michigan
Macquarie University
Johns Hopkins University
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