# Linear Regression, GLMs and GAMs with R

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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.

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