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.

Regression Models is course 7 of 10 in the Data Science Specialization..

### Syllabus

**WEEK 1**

Least Squares and Linear Regression

This week, we focus on least squares and linear regression.

Graded: Quiz 1

**WEEK 2**

Linear Regression & Multivariable Regression

This week, we will work through the remainder of linear regression and then turn to the first part of multivariable regression.

Graded: Quiz 2

**WEEK 3**

Multivariable Regression, Residuals, & Diagnostics

This week, we’ll build on last week’s introduction to multivariable regression with some examples and then cover residuals, diagnostics, variance inflation, and model comparison.

Graded: Quiz 3

**WEEK 4**

Week 4: Logistic Regression and Poisson Regression

This week, we will work on generalized linear models, including binary outcomes and Poisson regression.

Graded: Quiz 4

Graded: Regression Models Course Project – Peer Review

ENROLL IN COURSE