Regression Models
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.
75/100
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- What the provider tells you
- 24/45
- Who stands behind it
- 35/35
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- 16/20
Scores how much the provider publishes and who stands behind it — not how well it is taught.
What you'll learn
- understand the fundamentals of regression analysis
- apply least squares method for inference
- learn about special cases of regression like ANOVA and ANCOVA
- analyze residuals and variability
- explore modern techniques in model selection
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