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.
About this Course
Skills you will gain
- 5 stars64.19%
- 4 stars23.07%
- 3 stars7.57%
- 2 stars2.98%
- 1 star2.17%
TOP REVIEWS FROM REGRESSION MODELS
The best course in my mind, but I am chocked about how Data Science people approach regression type of problems, it is almost 100% data mining and no theory!! I wonder where it will take us..
Excellent overview of a very broad and complex topic with plenty of useful applications within R. The course project does an outstanding job at teaching the pitfalls of omitted variable bias.
Great subject, was a bit frustrated with some of the material (seemed rushed and not well prepared). Great assignment, but too restrictive on the max number of pages allowed. Wasted a lot of time.
Good course on the theories behind regression, followed by significant applications and how to use them in R. Lectures are very dry, but the information within them is very useful.
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