Comprehensive Linear Modeling with R

Comprehensive Linear Modeling with R, available at $39.99, has an average rating of 4.25, with 104 lectures, based on 143 reviews, and has 2386 subscribers.

You will learn about Understand, use and apply, estimate, interpret and validate: ANOVA; regression; survival analysis; GLMs; smoothers and GAMs; longitudinal, mixed-effects, split-plot and nested model designs using their own data and R software. Achieve proficiency using the popular no-cost and versatile R Commander GUI as an interface to the broad statistical and graphical capabilities in R. Know and use tests for simple, conditional, and simultaneous inference. Apply various graphs and plots to validate linear models. Be able to compare and choose the 'best' among multiple competing models. This course is ideal for individuals who are This course is aimed at graduate students and working quantitative and data-analytic professionals who seek to acquire a wide range of linear (and non-linear) modeling skills using R. or People who only have a Mac computer available to use should know that the R Commander interface is written in the R-specific RGtk2 language (based on GTK+) which is known to be problematic running on a Mac computer. It is particularly useful for This course is aimed at graduate students and working quantitative and data-analytic professionals who seek to acquire a wide range of linear (and non-linear) modeling skills using R. or People who only have a Mac computer available to use should know that the R Commander interface is written in the R-specific RGtk2 language (based on GTK+) which is known to be problematic running on a Mac computer.

Enroll now: Comprehensive Linear Modeling with R

Summary

Title: Comprehensive Linear Modeling with R

Price: $39.99

Average Rating: 4.25

Number of Lectures: 104

Number of Published Lectures: 104

Number of Curriculum Items: 104

Number of Published Curriculum Objects: 104

Original Price: $84.99

Quality Status: approved

Status: Live

What You Will Learn

Understand, use and apply, estimate, interpret and validate: ANOVA; regression; survival analysis; GLMs; smoothers and GAMs; longitudinal, mixed-effects, split-plot and nested model designs using their own data and R software.
Achieve proficiency using the popular no-cost and versatile R Commander GUI as an interface to the broad statistical and graphical capabilities in R.
Know and use tests for simple, conditional, and simultaneous inference.
Apply various graphs and plots to validate linear models.
Be able to compare and choose the 'best' among multiple competing models.

Who Should Attend

This course is aimed at graduate students and working quantitative and data-analytic professionals who seek to acquire a wide range of linear (and non-linear) modeling skills using R.
People who only have a Mac computer available to use should know that the R Commander interface is written in the R-specific RGtk2 language (based on GTK+) which is known to be problematic running on a Mac computer.

Target Audiences

This course is aimed at graduate students and working quantitative and data-analytic professionals who seek to acquire a wide range of linear (and non-linear) modeling skills using R.
People who only have a Mac computer available to use should know that the R Commander interface is written in the R-specific RGtk2 language (based on GTK+) which is known to be problematic running on a Mac computer.

Comprehensive Linear Modeling with R provides a wide overview of numerous contemporary linear and non-linear modeling approaches for the analysis of research data. These include basic, conditional and simultaneous inference techniques; analysis of variance (ANOVA); linear regression; survival analysis; generalized linear models (GLMs); parametric and non-parametric smoothers and generalized additive models (GAMs); longitudinal and mixed-effects, split-plot and other nested model designs. The course showcases the use of R Commander in performing these tasks. R Commander is a popular GUI-based “front-end” to the broad range of embedded statistical functionality in R software. R Commander is an ‘SPSS-like’ GUI that enables the implementation of a large variety of statistical and graphical techniques using both menus and scripts. Please note that the R Commander GUI is written in the RGtk2 R-specific visual language (based on GTK+) which is known to have problems running on a Mac computer.

The course progresses through dozens of statistical techniques by first explaining the concepts and then demonstrating the use of each with concrete examples based on actual studies and research data. Beginning with a quick overview of different graphical plotting techniques, the course then reviews basic approaches to establish inference and conditional inference, followed by a review of analysis of variance (ANOVA). The course then progresses through linear regression and a section on validating linear models. Then generalized linear modeling (GLM) is explained and demonstrated with numerous examples. Also included are sections explaining and demonstrating linear and non-linear models for survival analysis, smoothers and generalized additive models (GAMs), longitudinal models with and without generalized estimating equations (GEE), mixed-effects, split-plot, and nested designs. Also included are detailed examples and explanations of validating linear models using various graphical displays, as well as comparing alternative models to choose the ‘best’ model. The course concludes with a section on the special considerations and techniques for establishing simultaneous inference in the linear modeling domain.

The rather long course aims for complete coverage of linear (and some non-linear) modeling approaches using R and is suitable for beginning, intermediate and advanced R users who seek to refine these skills. These candidates would include graduate students and/or quantitative and/or data-analytic professionals who perform linear (and non-linear) modeling as part of their professional duties.

Course Curriculum

Chapter 1: Data Analysis with R Commander Graphical Displays

Lecture 1: Introduction to Course

Lecture 2: Notes About: (1) R and (2) R Commander and (3) Materials

Lecture 3: Don't Overlook Sectional Exercises !

Lecture 4: Graphical Displays using R Commander (part 1)

Lecture 5: Materials and Agenda Topics

Lecture 6: Graphical Displays using Rcmdr (part 2)

Lecture 7: Graphical Displays using Rcmdr (part 3)

Lecture 8: Graphical Displays using Rcmdr (part 4)

Lecture 9: Graphical Displays using Rcmdr (part 5)

Lecture 10: Graphical Displays using Rcmdr (part 6)

Lecture 11: Graphical Displays using Rcmdr (part 7)

Lecture 12: Graphical Displays using Rcmdr (part 8)

Chapter 2: Simple and Conditional Inference

Lecture 1: What is Inference ? (slides)

Lecture 2: Inference about Roomwidth using Rcmdr

Lecture 3: Roomwidth Inference Continued

Lecture 4: Simple Inference: Waves Data

Lecture 5: Simple Inference: Waves Non-Parametric

Lecture 6: Simple Inference: Piston Rings

Lecture 7: Conditional Inference: Roomwidths Revisited

Lecture 8: Conditional Inference: Roomwidths Continued

Lecture 9: Conditional Inference: Gastrointestinal Damage

Lecture 10: Conditional Inference: Birth Defects

Lecture 11: Inference Exercises

Lecture 12: Inference Exercise Answers (part 1)

Lecture 13: Inference Exercise Answers (part 2)

Chapter 3: Analysis of Variance (ANOVA)

Lecture 1: Partial Exercise Solution (part 1)

Lecture 2: Partial Exercise Solution (part 2)

Lecture 3: Analysis of Variance (ANOVA) Studies (slides)

Lecture 4: Weight Gain in Rats (Rcmdr)

Lecture 5: Finish Weight Gain then Foster Feeding in Rats

Lecture 6: Water Hardness Revisited

Lecture 7: Male Egyptian Skulls (part 1)

Lecture 8: Male Egyptian Skulls (part 2)

Lecture 9: More Exercises

Chapter 4: Linear Modeling

Lecture 1: What is Linear Modeling? (slides)

Lecture 2: Estimating the Age of the Universe (slides and script, part 1)

Lecture 3: Estimating the Age of the Universe (script, part 2)

Lecture 4: Age of the Universe (script, part 3)

Lecture 5: Cloud Seeding (slides and script, part 1)

Lecture 6: Cloud Seeding (script, part 2)

Lecture 7: Cloud Seeding (script, part 3)

Lecture 8: Cloud Seeding Diagnostic Plots (part 4)

Chapter 5: Validating Linear Models (aka 'Model Checking')

Lecture 1: Model Checking (part 1)

Lecture 2: Model Checking (part 2)

Lecture 3: Model Checking (part 3)

Lecture 4: Model Checking (part 4)

Lecture 5: Model Checking (part 5)

Lecture 6: Model Checking (part 6)

Chapter 6: Generalized Linear Modeling (GLMs)

Lecture 1: Generalized Linear Models (slides)

Lecture 2: ESR and Plasma Proteins (part 1)

Lecture 3: ESR and Plasma Proteins (part 2)

Lecture 4: ESR and Plasma Proteins (part 3)

Lecture 5: Women's Role in Society (part 1)

Lecture 6: Women's Role in Society (part 2)

Lecture 7: Women's Role in Society (part 3)

Lecture 8: Colonic Polyps

Lecture 9: Driving and Back Pain

Chapter 7: Survival Analysis

Lecture 1: What is Survival Analysis? (slides)

Lecture 2: Glioma Radioimmunotherapy

Lecture 3: Breast Cancer Survival

Chapter 8: Smoothers and Generalized Additive Modeling (GAMs)

Lecture 1: Smoothers and GAMs (slides, part 1)

Lecture 2: Smoothers and GAMs (slides, part 2)

Lecture 3: Air Pollution in U.S. Cities

Lecture 4: Kyphosis (part 1)

Lecture 5: Kyphosis (part 2)

Lecture 6: Non-Parametric Smoothers (part 1)

Lecture 7: Lowess Smoothers (part 2)

Lecture 8: Lowess Smoothers (part 3)

Lecture 9: GAM with Binary Isolation Data

Lecture 10: GAM Examples using mgcv Package (part 1)

Lecture 11: GAM Examples using mgcv Package (part 2)

Lecture 12: GAM Examples using mgcv Package (part 3)

Lecture 13: Strongly Humped Data (part 1)

Lecture 14: Strongly Humped Data (part 2)

Chapter 9: Linear Mixed-Effects Models

Lecture 1: Linear Mixed-Effects Models (slides, part 1)

Lecture 2: Linear Mixed-Effects Models (slides, part 2)

Lecture 3: Beat the Blues Slides and Data

Lecture 4: Beat the Blues Study (part 2)

Lecture 5: Beat the Blues Study Boxplots and Data Transformation (part 3)

Lecture 6: Run Beat the Blues Models (part 1)

Lecture 7: Run Beat the Blues Models (part 2)

Chapter 10: Generalized Estimating Equations (GEE)

Lecture 1: Generalized Estimating Equations (GEE) (slides, part 1)

Lecture 2: Generalized Estimating Equations (GEE) (slides, part 2)

Lecture 3: GEE with Beat the Blues as Binomial GLM (part 1)

Lecture 4: GEE with Beat the Blues as Binomial GLM (part 2)

Lecture 5: Respiratory Illness with Binary Response Variable (part 1)

Lecture 6: Respiratory Illness with Binary Response Variable (part 2)

Lecture 7: Respiratory Illness with Binary Response Variable (part 3)

Lecture 8: Respiratory Illness with Binary Response Variable (part 4)

Chapter 11: Split-Plot and Nested Designs

Instructors

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

Rating Distribution

1 stars: 7 votes
2 stars: 4 votes
3 stars: 27 votes
4 stars: 53 votes
5 stars: 52 votes

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