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Course Name: Comprehensive Linear Modeling with R

Subtitle: Learn to model with R: ANOVA, regression, GLMs, survival analysis, GAMs, mixed-effects, split-plot and nested designs

Instructor: Taught by Geoffrey Hubona, Professor of Information Systems

Category: Business

Subcategory: Data & Analytics

Provided by: Udemy

Price: $99 (before discount)

Free coupon code: See above (no charge for coupon)

Review info & popularity

As of March 9, 2016…

Students: 39390 students enrolled

Ratings: 2 reviews

Rank: ranked #439 in Udemy Business Courses

Brief course description

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.

(Read more about this course on the official course page.)

Geoffrey Hubona bio

Dr. Geoffrey Hubona has been a full-time faculty member at 3 major state universities in the Eastern United States for 20 years, teaching dozens of different statistics, business information systems, and computer science courses to undergraduate, master’s and PhD students. He earned a PhD in Information Systems and Computer Science (1993) from the University of South Florida in Tampa, FL (1993); an MA in Economics (1990) from USF; an MBA in Finance (1979) from George Mason University in Fairfax, VA; and a BA in Psychology (1972) from the University of Virginia in Charlottesville, VA. He held full-time faculty positions at the University of Maryland Baltimore County (1993-1996), Virginia Commonwealth University (1996-2001), and Georgia State University (2001-2010). He is the founder of the Georgia R School (2010), a non-profit online educational institution that teaches research methods and quantitative analysis techniques such as linear and non-linear modeling, multivariate methods, data mining, programming and simulation, and structural equation modeling and partial least squares (PLS) path modeling. Dr. Hubona is recognized as an expert of the analytical, open-source R software suite and of various path modeling software packages, including SmartPLS. He has published dozens of research articles that explain and use these techniques for the analysis of data, and, with software co-development partner Dean Lim, has created a popular cloud-based PLS software application, PLS-GUI.

(Learn more about this instructor on the official course page.)

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Final details for this Udemy course

Languages: English

Skill level: All Levels

Lectures: 104 lessons

Duration: 14.5 Hours of video

What you get: 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.

Target audience: 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.

Requirements: Students will need to install R and R Commander using the ample video and written instructions that are provided for doing so.

Access: Lifetime access

Peace of mind: 30 day money back guarantee

Availability: available online, as well as on iOS and Android

Download options: check course to see if you can download lessons