### About The 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.

### Frequently Asked Questions

Will I get a Statement of Accomplishment after completing this class?

Yes. Students who successfully complete the class will receive a Statement of Accomplishment signed by the instructor.

What resources will I need for this class?

Students must have the latest version of R and RStudio installed.

How does this course fit into the Data Science Specialization?

This is the seventh course in the sequence. Although it isn't a requirement, we recommend that you first take The Data Scientist's Toolbox and R Programming.

Yes. Students who successfully complete the class will receive a Statement of Accomplishment signed by the instructor.

What resources will I need for this class?

Students must have the latest version of R and RStudio installed.

How does this course fit into the Data Science Specialization?

This is the seventh course in the sequence. Although it isn't a requirement, we recommend that you first take The Data Scientist's Toolbox and R Programming.

### Recommended Background

R programming, mathematical aptitude. The content in the R Programming and Statistical Inference courses covers the necessary background. The material from Statistical inference could be taken concurrently with this class.