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.
The mission of The Johns Hopkins University is to educate its students and cultivate their capacity for life-long learning, to foster independent and original research, and to bring the benefits of discovery to the world.
- 5 stars64.21%
- 4 stars23.07%
- 3 stars7.58%
- 2 stars2.99%
- 1 star2.14%
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
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 have been involved with regression models for a long time.
I was amazed on the capabilities that have been developed in R. I think that an open Source software is the way to build knowledge
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.