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%
It is very interesting, however is difficult to follow the math explanations, it could be more easy with practical examples.... like the final assignment, it was difficult to me.
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!
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
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