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.
- 5 stars64.20%
- 4 stars23.06%
- 3 stars7.57%
- 2 stars2.98%
- 1 star2.17%
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.
I appreciate coefficients interpretation and variance influence to choose among models.
Running code takes a few seconds, understanding the model's outputs is a much hard
This module was the maximum. I learned how powerful the use of Regression Models techniques in Data Science analysis is. I thank Professor Brian Caffo for sharing his knowledge with us. Thank you!
Regression analysis is something that is kind of easy for people to understand (outcome and predictor - people get that!). It's easy to explain to people. So much practice using the lm function!