Comprehensive Linear Modeling with R

Learn to model with R: ANOVA, regression, GLMs, survival analysis, GAMs, mixed-effects, split-plot and nested designs
4.3 (21 ratings) Instead of using a simple lifetime average, Udemy calculates a
course's star rating by considering a number of different factors
such as the number of ratings, the age of ratings, and the
likelihood of fraudulent ratings.
787 students enrolled
$19
$60
68% off
Take This Course
  • Lectures 104
  • Length 14.5 hours
  • Skill Level All Levels
  • Languages English
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
Wishlisted Wishlist

How taking a course works

Discover

Find online courses made by experts from around the world.

Learn

Take your courses with you and learn anywhere, anytime.

Master

Learn and practice real-world skills and achieve your goals.

About This Course

Published 12/2015 English

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.


What are the requirements?

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

What am I going to get from this course?

  • 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.

What is the 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.
  • 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.

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.

Curriculum

Section 1: Data Analysis with R Commander Graphical Displays
Introduction to Course
Preview
01:45
Notes About: (1) R and (2) R Commander and (3) Materials
Preview
10:24
Don't Overlook Sectional Exercises !
Preview
02:12
Materials and Agenda Topics
Preview
11:01
Graphical Displays using R Commander (part 1)
Preview
09:27
Graphical Displays using Rcmdr (part 2)
07:30
Graphical Displays using Rcmdr (part 3)
08:59
Graphical Displays using Rcmdr (part 4)
08:07
Graphical Displays using Rcmdr (part 5)
10:54
Graphical Displays using Rcmdr (part 6)
07:16
Graphical Displays using Rcmdr (part 7)
06:48
Graphical Displays using Rcmdr (part 8)
08:49
Section 2: Simple and Conditional Inference
07:55

Statistical inference is the process of deducing properties of an underlying distribution by analysis of data. Inferential statistical analysis infers properties about a population: this includes testing hypotheses and deriving estimates. The population is assumed to be larger than the observed data set; in other words, the observed data is assumed to be sampled from a larger population.

Inference about Roomwidth using Rcmdr
Preview
11:44
Roomwidth Inference Continued
09:37
Simple Inference: Waves Data
09:46
Simple Inference: Waves Non-Parametric
10:14
Simple Inference: Piston Rings
12:30
Conditional Inference: Roomwidths Revisited
08:30
Conditional Inference: Roomwidths Continued
08:32
Conditional Inference: Gastrointestinal Damage
07:40
Conditional Inference: Birth Defects
05:40
Inference Exercises
01:33
Inference Exercise Answers (part 1)
08:51
Inference Exercise Answers (part 2)
06:37
Section 3: Analysis of Variance (ANOVA)
Partial Exercise Solution (part 1)
07:29
Partial Exercise Solution (part 2)
08:55
08:57

Analysis of variance (ANOVA) is a collection of statistical models used to analyze the differences among group means and their associated procedures (such as "variation" among and between groups). In the ANOVA setting, the observed variance in a particular variable is partitioned into components attributable to different sources of variation. In its simplest form, ANOVA provides a statistical test of whether or not the means of several groups are equal, and therefore generalizes the t-test to more than two groups.

Weight Gain in Rats (Rcmdr)
Preview
08:47
Finish Weight Gain then Foster Feeding in Rats
11:25
Water Hardness Revisited
07:15
Male Egyptian Skulls (part 1)
06:49
Male Egyptian Skulls (part 2)
08:02
More Exercises
00:28
Section 4: Linear Modeling
07:39

In statistics, linear regression is an approach for modeling the relationship between a scalar dependent variable y and one or more explanatory variables (or independent variables) denoted X. The case of one explanatory variable is called simple linear regression. For more than one explanatory variable, the process is called multiple linear regression

Estimating the Age of the Universe (slides and script, part 1)
Preview
06:15
Estimating the Age of the Universe (script, part 2)
Preview
07:54
Age of the Universe (script, part 3)
06:48
Cloud Seeding (slides and script, part 1)
11:43
Cloud Seeding (script, part 2)
09:07
Cloud Seeding (script, part 3)
07:44
Cloud Seeding Diagnostic Plots (part 4)
05:50
Section 5: Validating Linear Models (aka 'Model Checking')
Model Checking (part 1)
06:37
Model Checking (part 2)
Preview
07:46
Model Checking (part 3)
07:34
Model Checking (part 4)
07:23
Model Checking (part 5)
08:01
Model Checking (part 6)
06:49
Section 6: Generalized Linear Modeling (GLMs)
09:20

In statistics, the generalized linear model (GLM) is a flexible generalization of ordinary linear regression that allows for response variables that have error distribution models other than a normal distribution. The GLM generalizes linear regression by allowing the linear model to be related to the response variable via a link function and by allowing the magnitude of the variance of each measurement to be a function of its predicted value.

ESR and Plasma Proteins (part 1)
Preview
11:55
ESR and Plasma Proteins (part 2)
11:50
ESR and Plasma Proteins (part 3)
12:14
Women's Role in Society (part 1)
08:44
Women's Role in Society (part 2)
07:34
Women's Role in Society (part 3)
06:36
Colonic Polyps
06:57
Driving and Back Pain
08:34
Section 7: Survival Analysis
12:06

Survival analysis is a branch of statistics for analyzing the expected duration of time until one or more events happen, such as death in biological organisms and failure in mechanical systems. This topic is called reliability theory or reliability analysis inengineering, duration analysis or duration modelling in economics, and event history analysis in sociology. Survival analysis attempts to answer questions such as: what is the proportion of a population which will survive past a certain time? Of those that survive, at what rate will they die or fail? Can multiple causes of death or failure be taken into account? How do particular circumstances or characteristics increase or decrease the probability of survival?

Glioma Radioimmunotherapy
Preview
08:53
Breast Cancer Survival
11:08
Section 8: Smoothers and Generalized Additive Modeling (GAMs)
08:50

A smoother is a statistical technique for estimating a real valued function by using its noisy observations, when no parametric model for this function is known. The estimated function is smooth, or non-linear, and the level of smoothness is set by a single parameter.

05:03

In statistics, a generalized additive model (GAM) is a generalized linear model in which the linear predictor depends linearly on unknown smooth functions of some predictor variables, and interest focuses on inference about these smooth functions.

Air Pollution in U.S. Cities
Preview
11:46
06:16

Kyphosis (from Greek κυφός kyphos, a hump) refers to the abnormally excessive convex kyphotic curvature of the spine as it occurs in the thoracic and sacral regions. (Inward concave curving of the cervical and lumbar regions of the spine is called lordosis.) Kyphosis can be called roundback or Kelso's hunchback. It can result from degenerative diseases such as arthritis; developmental problems, most commonlyScheuermann's disease; osteoporosis with compression fractures of the vertebra; Multiple myeloma or trauma.

Kyphosis (part 2)
09:16
07:05

LOESS and LOWESS (locally weighted scatterplot smoothing) are two strongly related non-parametric regression methods that combine multiple regression models in a k-nearest-neighbor-based meta-model. "LOESS" is a later generalization of LOWESS; although it is not a true initialism, it may be understood as standing for "LOcal regrESSion".

LOESS and LOWESS thus build on "classical" methods, such as linear and nonlinear least squares regression.

Lowess Smoothers (part 2)
08:34
Lowess Smoothers (part 3)
07:37
GAM with Binary Isolation Data
09:52
GAM Examples using mgcv Package (part 1)
07:07
GAM Examples using mgcv Package (part 2)
09:13
GAM Examples using mgcv Package (part 3)
06:56
Strongly Humped Data (part 1)
07:06
Strongly Humped Data (part 2)
08:47
Section 9: Linear Mixed-Effects Models
08:12

A mixed model is a statistical model containing both fixed effects and random effects. These models are useful in a wide variety of disciplines in the physical, biological and social sciences. They are particularly useful in settings where repeated measurements are made on the same statistical units (longitudinal study), or where measurements are made on clusters of related statistical units.

Linear Mixed-Effects Models (slides, part 2)
07:55
Beat the Blues Slides and Data
Preview
09:01
Beat the Blues Study (part 2)
07:08
07:10

In descriptive statistics, a box plot or boxplot is a convenient way of graphically depicting groups of numerical data through their quartiles. Box plots may also have lines extending vertically from the boxes (whiskers) indicating variability outside the upper and lower quartiles, hence the terms box-and-whisker plot and box-and-whisker diagram. Outliers may be plotted as individual points. Box plots are non-parametric: they display variation in samples of a statistical population without making any assumptions of the underlying statistical distribution.

Run Beat the Blues Models (part 1)
05:33
Run Beat the Blues Models (part 2)
07:24
Section 10: Generalized Estimating Equations (GEE)
10:02

In statistics, a generalized estimating equation (GEE) is used to estimate the parameters of a generalized linear model with a possible unknown correlation between outcomes.

Parameter estimates from the GEE are consistent even when the covariance structure is misspecified, under mild regularity conditions. The focus of the GEE is on estimating the average response over the population ("population-averaged" effects) rather than theregression parameters that would enable prediction of the effect of changing one or more covariates on a given individual. GEEs are usually used in conjunction with Huber–White standard error estimates, also known as "robust standard error" or "sandwich variance" estimates. In the case of a linear model with a working independence variance structure, these are known as "heteroscedasticity consistent standard error" estimators. Indeed, the GEE unified several independent formulations of these standard error estimators in a general framework.

Generalized Estimating Equations (GEE) (slides, part 2)
06:54
GEE with Beat the Blues as Binomial GLM (part 1)
Preview
07:08
GEE with Beat the Blues as Binomial GLM (part 2)
08:05
Respiratory Illness with Binary Response Variable (part 1)
06:10
Respiratory Illness with Binary Response Variable (part 2)
08:34
Respiratory Illness with Binary Response Variable (part 3)
08:38
Respiratory Illness with Binary Response Variable (part 4)
10:06
Section 11: Split-Plot and Nested Designs

Students Who Viewed This Course Also Viewed

  • Loading
  • Loading
  • Loading

Instructor Biography

Geoffrey Hubona, Ph.D., Professor of Information Systems

Dr. Geoffrey Hubona held full-time tenure-track, and tenured, assistant and associate professor faculty positions at 3 major state universities in the Eastern United States from 1993-2010. In these positions, he taught dozens of various statistics, business information systems, and computer science courses to undergraduate, master's and Ph.D. students. He earned a Ph.D. in Business Administration (Information Systems and Computer Science) from the University of South Florida (USF) in Tampa, FL (1993); an MA in Economics (1990), also 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 was a full-time assistant professor at the University of Maryland Baltimore County (1993-1996) in Catonsville, MD; a tenured associate professor in the department of Information Systems in the Business College at Virginia Commonwealth University (1996-2001) in Richmond, VA; and an associate professor in the CIS department of the Robinson College of Business at Georgia State University (2001-2010). He is the founder of the Georgia R School (2010-2014) and of R-Courseware (2014-Present), online educational organizations that teach research methods and quantitative analysis techniques. These research methods techniques include 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 an expert of the analytical, open-source R software suite and of various PLS 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.

Ready to start learning?
Take This Course