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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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
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(108 ratings)Offered by
Explore more from Probability and Statistics
Wesleyan University
Duke University
University of Michigan
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VS
Reviewed on Apr 22, 2018
Great course to get the basics on Linear Models and Inference. Great Introduction to Logistic Regression and Poisson Regression. Good emphasis in Diagnostics of the main assumptions
AA
Reviewed on Feb 28, 2017
This course has been the most difficult in the Dara Science track so far, but you get a more in depth knowledge in data analysis and interpretation based on statistical models.
KA
Reviewed on Dec 16, 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.
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