Skip to content
Modeling in R

Modeling in R

This course will show you how to use R to create statistical models and use them to analyze data.

Overview

In this course you will learn how to use R to build statistical models and how to use those models to analyze data.Topics include commonly used statistical methods such as multiple regression, logistic regression, the Poisson model for count data and more. This course will cover a variety of techniques, and at different levels, to meet the needs of different groups of users. The goal is to provide guidance in using R to implement various modeling procedures, not to provide conceptual development of the statistical methods. Consequently, some knowledge of linear models is required (statistics.com has courses in all of them).

  • Intermediate, Advanced
  • 4 Weeks
  • Expert Instructor
  • Tuiton-Back Guarantee
  • 100% Online
  • TA Support

Learning Outcomes

Students who complete this course will learn how to use R to implement various modeling procedures – the emphasis is on the software, not the theoretical background of the models. You will explore linear and logistic regression, generalized linear models, general estimating equations and how to use R to analyze longitudinal data. Most of these modeling techniques are covered in separate courses at Statistics.com.  If you take this course before studying these models you will probably not gain a full understanding of the more advanced techniques, but you will be better positioned, software-wise, to implement them when and if you take those courses.

  • Fit linear regression model
  • Fit logistic regression model
  • Generalize to the generalized linear model
  • Fit Poisson model
  • Fit mixed model to longitudinal data

Who Should Take This Course

Anyone who is familiar with R and wants to learn how to use it to build and use statistical models.

Important
The course covers a variety of techniques and at different levels, to meet the needs of different groups of users.  Those with minimal-to-moderate statistics preparation will want to spend time on the more extensive presentation of linear regression, and not attempt to complete all the more advanced segments on other methods.  Those with more experience in statistics may not require as much time in the early stages, but will be better able to work with the more advanced segments. The goal is to provide guidance in using R to implement various modeling procedures, and not to provide conceptual development of the statistical methods. Most of the modeling techniques described here are covered in separate courses at Statistics.com.

If you take this course first, you will probably not gain a full understanding of the more advanced techniques, but you will be better positioned, software-wise, to implement them when and if you take those courses. If you take the other courses first, you will have a better understanding of the concepts behind the techniques before tackling them in R, but will be less prepared software-wise when you take the conceptual courses. Either approach will work, but each has its own costs and benefits.

Our Instructors

Institute Staff

Institute Staff

This course will be taught by instructors at The Institute, however an instructor for this course has not been chosen at this juncture.

Course Syllabus

Week 1

Linear Regression, Logistic Regression

  • Multiple linear regression with R
  • Simple examples, dummy explanatory variables, interpreting regression coefficients; finding a parsimonious model

Week 2

Generalized Linear Models With R

  • Logistic regression with R
  • The need for a different model when the response variable is binary, the logistic transform and fitting the model to some simple examples, deviance residuals
  • Multiple regression and logistic regression as special cases of the generalized linear model
  • The Poisson model for count data.
  • The problem of overdispersion

Week 3

Analyzing Longitudinal Data Using R

  • Examples of longitudinal data
  • Simple graphics for longitudinal data and simple inference using the summary measure approach
  • The ‘long form’ of longitudinal data
  • Mixed-effects models for longitudinal data

Week 4

Generalized Estimating Equations

  • Modeling the correlational structure of the repeated measurements
  • The generalized estimating equation approach for non-normal response variables in longitudinal data
  • The dropout problem

Class Dates

2024

07/26/2024 to 08/23/2024
Instructors: Institute Staff

2025

07/25/2025 to 08/22/2025
Instructors: Institute Staff

Prerequisites

Some familiarity with statistical modeling is needed. See also the “Important” note in “Who Should Take This Course” above.

R for Statistical Analysis

This course will teach you how to use R for basic statistical procedures.
  • Skill: Intermediate, Advanced
  • Credit Options: CAP, CEU
Karolis Urbonas
Susan Kamp
Stephen McAllister
Amir Aminimanizani
Elena Rose
Leonardo Nagata
Richard Jackson

Frequently Asked Questions

  • What is your satisfaction guarantee and how does it work?

  • Can I transfer or withdraw from a course?

  • Who are the instructors at Statistics.com?

Visit our knowledge base and learn more.

Register For This Course

Modeling in R

Additional Information

Homework

Homework in this course consists of guided data analysis problems using software and guided data modeling problems using software.

In addition to assigned readings, this course also has practice exercises, and the instructor’s expert write-ups on important concepts.

Course Text

Course materials will be provided by the instructor.

Software

Students must have access to R.

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout. Use promo code ACADEMIC where prompted during registration.

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Supplemental Information

Literacy, Accessibility, and Dyslexia

At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:

 

Chrome

 

Firefox

 

Safari

  • Navidys (for colorblindness, dyslexia, and reading difficulties)
  • HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)

Register For This Course

Modeling in R