**Linear Regression, GLMs** and **GAMs** with **R** demonstrates how to use **R** to extend the basic assumptions and constraints of **linear** **regression** to specify, model, and interpret the results of generalized **linear** (**GLMs**) and generalized additive (**GAMs**) models. The course demonstrates the estimation of **GLMs** and **GAMs** by working through a series of practical

## What you’ll learn

- Understand the assumptions of ordinary least squares (OLS) linear regression.
- Specify, estimate and interpret linear (regression) models using R.
- Understand how the assumptions of OLS regression are modified (relaxed) in order to specify, estimate and interpret generalized linear models (GLMs).
- Specify, estimate and interpret GLMs using R.
- Understand the mechanics and limitations of specifying, estimating and interpreting generalized additive models (GAMs).

### Requirements

- Students will need to install R and R Commander software but ample instruction for doing so is provided.

Who this course is for:

- This course would be useful for anyone involved with linear modeling estimation, including graduate students and/or working professionals in quantitative modeling and data analysis.
- The focus, and majority of content, of this course is on generalized additive modeling. Anyone who wishes to learn how to specify, estimate and interpret GAMs would especially benefit from this course.

**Linear Regression, GLMs** and **GAMs** with **R** demonstrates how to use **R** to extend the basic assumptions and constraints of **linear** **regression** to specify, model, and interpret the results of generalized **linear** (**GLMs**) and generalized additive (**GAMs**) models.

**GLMs** and **GAMs** with **R**. With the help of this course you can How to extend **linear** **regression** to specify and estimate generalized **linear** models and additive models.. This course was created by Geoffrey Hubona & Ph.D.. It was rated 4.7 out of 5 by approx 8478 ratings.

**regression** to specify, style, and interpret the result of generalized **linear** (**GLMs**) and generalized additive (**GAMs**) fashions.