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Comprehensive Linear Modeling with R

By: Geoffrey Hubona

  • 14:17:42
  • 117
  • 15
  • Language: English

Course Summary

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 re

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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.

Pre-Requisites

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

Curriculum

  • Exercise Files
  • Introduction to Course
    01:46
  • Notes About: (1) R and (2) R Commander and (3) Materials
    10:25
  • Don't Overlook Sectional Exercises !
    02:13
  • Materials and Agenda Topics
    11:02
  • Graphical Displays using R Commander (part 1)
    09:28
  • Graphical Displays using Rcmdr (part 2)
    07:31
  • Graphical Displays using Rcmdr (part 3)
    09:00
  • Graphical Displays using Rcmdr (part 4)
    08:07
  • Graphical Displays using Rcmdr (part 5)
    10:55
  • Graphical Displays using Rcmdr (part 6)
    07:17
  • Graphical Displays using Rcmdr (part 7)
    06:49
  • Graphical Displays using Rcmdr (part 8)
    08:50
  • Exercise Files
  • What is Inference ? (slides)
    07:56
  • Inference about Roomwidth using Rcmdr
    11:44
  • Roomwidth Inference Continued
    09:38
  • Simple Inference: Waves Data
    09:46
  • Simple Inference: Waves Non-Parametric
    10:14
  • Simple Inference: Piston Rings
    12:30
  • Conditional Inference: Roomwidths Revisited
    08:31
  • Conditional Inference: Roomwidths Continued
    08:32
  • Conditional Inference: Gastrointestinal Damage
    07:41
  • Conditional Inference: Birth Defects
    05:41
  • Inference Exercises
    01:33
  • Inference Exercise Answers (part 1)
    08:52
  • Inference Exercise Answers (part 2)
    06:37
  • Exercise Files
  • Partial Exercise Solution (part 1)
    07:29
  • Partial Exercise Solution (part 2)
    08:56
  • Analysis of Variance (ANOVA) Studies (slides)
    08:58
  • Weight Gain in Rats (Rcmdr)
    08:47
  • Finish Weight Gain then Foster Feeding in Rats
    11:25
  • Water Hardness Revisited
    07:16
  • Male Egyptian Skulls (part 1)
    06:49
  • Male Egyptian Skulls (part 2)
    08:03
  • More Exercises
    00:29
  • Exercise Files
  • What is Linear Modeling? (slides)
    07:39
  • Estimating the Age of the Universe (slides and script, part 1)
    06:15
  • Estimating the Age of the Universe (script, part 2)
    07:54
  • Age of the Universe (script, part 3)
    06:49
  • Cloud Seeding (slides and script, part 1)
    11:43
  • Cloud Seeding (script, part 2)
    09:08
  • Cloud Seeding (script, part 3)
    07:44
  • Cloud Seeding Diagnostic Plots (part 4)
    05:50
  • Exercise Files
  • Model Checking (part 1)
    06:37
  • Model Checking (part 2)
    07:47
  • Model Checking (part 3)
    07:34
  • Model Checking (part 4)
    07:24
  • Model Checking (part 5)
    08:01
  • Model Checking (part 6)
    06:49
  • Exercise Files
  • Generalized Linear Models (slides)
    09:21
  • ESR and Plasma Proteins (part 1)
    11:55
  • ESR and Plasma Proteins (part 2)
    11:51
  • ESR and Plasma Proteins (part 3)
    12:15
  • Women's Role in Society (part 1)
    08:45
  • 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
  • Exercise Files
  • What is Survival Analysis? (slides)
    12:07
  • Glioma Radioimmunotherapy
    08:54
  • Breast Cancer Survival
    11:09
  • Exercise Files
  • Smoothers and GAMs (slides, part 1)
    08:51
  • Smoothers and GAMs (slides, part 2)
    05:03
  • Air Pollution in U.S. Cities
    11:47
  • Kyphosis (part 1)
    06:17
  • Kyphosis (part 2)
    09:16
  • Non-Parametric Smoothers (part 1)
    07:05
  • 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)
    07:08
  • GAM Examples using mgcv Package (part 2)
    09:13
  • GAM Examples using mgcv Package (part 3)
    06:57
  • Strongly Humped Data (part 1)
    07:07
  • Strongly Humped Data (part 2)
    08:47
  • Exercise Files
    00:00
  • Linear Mixed-Effects Models (slides, part 1)
    08:12
  • Linear Mixed-Effects Models (slides, part 2)
    07:55
  • Beat the Blues Slides and Data
    09:02
  • Beat the Blues Study (part 2)
    07:08
  • Beat the Blues Study Boxplots and Data Transformation (part 3)
    07:11
  • Run Beat the Blues Models (part 1)
    05:34
  • Run Beat the Blues Models (part 2)
    07:24
  • Exercise Files
    00:00
  • Generalized Estimating Equations (GEE) (slides, part 1)
    10:02
  • Generalized Estimating Equations (GEE) (slides, part 2)
    06:55
  • GEE with Beat the Blues as Binomial GLM (part 1)
    07:09
  • 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
  • Exercise Files
    00:00
  • Exercise Files 2
    00:00
  • Irrigation Study Split-Plot Design (part 1)
    10:06
  • Irrigation Study Split-Plot Design (part 2)
    10:20
  • Comparing the Irrigation Split-Plot Models
    07:39
  • Hierarchical Variance Components (part 1)
    07:44
  • Hierarchical Variance Components (part 2)
    06:24
  • Mixed-Effects Temporal Pseudo-Replication and Exercises
    13:44
  • Farms Domain: Comparing Mixed versus Linear Intercepts and Slopes (part 1)
    09:11
  • Farms Domain: Comparing Mixed versus Linear Intercepts and Slopes (part 2)
    11:29
  • Farms Domain: Comparing Models (part 3)
    05:56
  • Childhood Diseases Revisited: Model Checking
    10:29
  • Exercise Files
    00:00
  • Simultaneous Inference for Multiple Comparisons (part 1)
    08:41
  • Simultaneous Inference for Multiple Comparisons (part 2)
    08:34
  • Deer Browsing (part 1)
    06:54
  • Deer Browsing (part 2)
    05:59
  • Cloud Seeding Revisited
    08:51

About the Author

Geoffrey Hubona, 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.

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Comprehensive Linear Modeling with R

  • 14:17:42
  • 117
  • 15
  • Language: English
  • 15 days Money back Gurantee
  • Unlimited Access
  • Android, iPhone and iPad Access
  • Certificate of Completion

Course Summary

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 re

Read More

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.

Pre-Requisites

  • 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.

About the Author

Geoffrey Hubona, 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.

More From Author