Linear Regression, GLMs and GAMs with R

Linear Regression, GLMs and GAMs with R, available at $44.99, has an average rating of 4.35, with 69 lectures, based on 249 reviews, and has 2368 subscribers.

You will learn about 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). This course is ideal for individuals who are 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. or 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. It is particularly useful 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. or 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.

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Summary

Title: Linear Regression, GLMs and GAMs with R

Price: $44.99

Average Rating: 4.35

Number of Lectures: 69

Number of Published Lectures: 69

Number of Curriculum Items: 69

Number of Published Curriculum Objects: 69

Original Price: $64.99

Quality Status: approved

Status: Live

What You Will 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).

Who Should Attend

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.

Target Audiences

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. The course demonstrates the estimation of GLMs and GAMs by working through a series of practical examples from the book Generalized Additive Models: An Introduction with R by Simon N. Wood (Chapman & Hall/CRC Texts in Statistical Science, 2006). Linear statistical models have a univariate response modeled as a linear function of predictor variables and a zero mean random error term. The assumption of linearity is a critical (and limiting) characteristic. Generalized linear models (GLMs) relax this assumption of linearity. They permit the expected value of the response variable to be a smoothed (e.g. non-linear) monotonic function of the linear predictors. GLMs also relax the assumption that the response variable is normally distributed by allowing for many distributions (e.g. normal, poisson, binomial, log-linear, etc.). Generalized additive models (GAMs) are extensions of GLMs. GAMs allow for the estimation of regression coefficients that take the form of non-parametric smoothers. Nonparametric smoothers like lowess (locally weighted scatterplot smoothing) fit a smooth curve to data using localized subsets of the data. This course provides an overview of modeling GLMs and GAMs using R. GLMs, and especially GAMs, have evolved into standard statistical methodologies of considerable flexibility. The course addresses recent approaches to modeling, estimating and interpreting GAMs. The focus of the course is on modeling and interpreting GLMs and especially GAMs with R. Use of the freely available R software illustrates the practicalities of linear, generalized linear, and generalized additive models.

Course Curriculum

Chapter 1: Introduction to Course and to Linear Modeling

Lecture 1: Introduction to Course

Lecture 2: Preliminaries: Installing R, RStudio, R Commander, Course Materials and Exercise

Lecture 3: Beginning Agenda (slides)

Lecture 4: What is Linear Modeling? (slides, part 1)

Lecture 5: Assumptions of Linear Modeling (slides, part 2)

Lecture 6: Desirable Properties of Beta-hat (slides, part 3)

Lecture 7: Example: Estimate Age of Universe (slides)

Lecture 8: Example: Estimate Age of Universe Live in R (part 1)

Lecture 9: Example: Estimate Age of Universe Live in R (part 2)

Lecture 10: Example: Estimating Age of the Universe (part 3)

Lecture 11: Finish Example and More Notes on Linear Modeling

Lecture 12: Linear Modeling Exercises

Chapter 2: Generalized Linear Models (GLMs) Part 1

Lecture 1: Introduction to GLMs (slides, part 1)

Lecture 2: Introduction to GLMs (slides, part 2)

Lecture 3: Introduction to GLMs (slides, part 3)

Lecture 4: Introduction to GLMs (slides, part 4)

Lecture 5: Example: Binomial (Proportion) Model with Heart Disease (part 1)

Lecture 6: Example: Binomial (Proportion) Model with Heart Disease (part 2)

Lecture 7: Example: Binomial (Proportion) Model with Heart Disease (part 3)

Lecture 8: Example: Binomial (Proportion) Model with Heart Disease (part 4)

Lecture 9: GLM Exercises

Chapter 3: Generalized Linear Models Part 2

Lecture 1: Current Agenda

Lecture 2: Linear Regression Exercise Solutions (part 1)

Lecture 3: Linear Regression Exercise Solutions (part 2)

Lecture 4: GLM Exercise Solutions (part 3)

Lecture 5: Example: Poisson Model with Count Data (part 1)

Lecture 6: Example: Poisson Model with Count Data (part 2)

Lecture 7: Example: Binary Response Variable (part 1)

Lecture 8: Example: Binary Response Variable (part 2)

Lecture 9: Exercise: GLM to GAM

Lecture 10: Example: Log-Linear Model for Categorical Data

Lecture 11: More on Deviance and Overdispersion (slides)

Chapter 4: Generalized Additive Models Explained

Lecture 1: What are GAMS? (Crawley, slides, part 1)

Lecture 2: What are GAMs? (Crawley, slides, part 2)

Lecture 3: Demonstrate GAM Ozone Data (part 1)

Lecture 4: Demonstrate GAM Ozone Data (part 2)

Lecture 5: General Approaches for Fitting GAMs (slides)

Lecture 6: What are GAMs? (Wood, slides, part 1)

Lecture 7: Univariate Polynomial GAMs (Wood, slides, part 2)

Lecture 8: Univariate Polynomial GAMs (Wood, slides, part 3)

Lecture 9: GAMs as 4th Order Polynomials (slides, part 1)

Lecture 10: GAMs as 4th Order Polynomials (slides, part 2)

Lecture 11: GAMs as Regression Splines (slides)

Lecture 12: Cubic Splines (slides, part 1)

Lecture 13: Cubic Splines (slides, part 2)

Lecture 14: Function to Establish Basis for Spline (slides)

Lecture 15: Build-a-GAM (slides, part 1)

Lecture 16: Build-a-GAM (slides, part 2)

Lecture 17: Build-a-GAM (slides, part 3)

Lecture 18: Build-a-GAM Demonstration in R Script

Lecture 19: Build-a-GAM Cross Validation

Lecture 20: Bivariate GAMs with 2 Explanatory Independent Variables (slides, part 1)

Lecture 21: Bivariate GAMs with 2 Explanatory Independent Variables (slides, part 2)

Lecture 22: Exercises

Chapter 5: Detailed GAM Examples

Lecture 1: Current Agenda (slides)

Lecture 2: Cherry Trees and Finer Control (slides, part 1)

Lecture 3: Finer Control of GAM (slides, part 2)

Lecture 4: Using Smoothers with More than One Predictor (slides)

Lecture 5: More on Alternative Smoothing Bases (slides)

Lecture 6: Parametric Model Terms (slides)

Lecture 7: Example: Brain Imaging (part 1)

Lecture 8: Example: Brain Imaging (part 2)

Lecture 9: Example: Brain Imaging (part 3)

Lecture 10: Example: Brain Imaging (part 4)

Lecture 11: Example: Brain Imaging (part 5)

Lecture 12: Example: Air Pollution in Chicago (part 1)

Lecture 13: Example: Air Pollution in Chicago (part 2)

Lecture 14: Air Pollution in Chicago (part 3)

Lecture 15: More Exercises

Instructors

Geoffrey Hubona, Ph.D.
Associate Professor of MIS and Data Analytics

Rating Distribution

1 stars: 8 votes
2 stars: 9 votes
3 stars: 37 votes
4 stars: 96 votes
5 stars: 99 votes

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