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.

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

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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).

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.

Instructors

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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.
See Instructor Bio

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

2020

Jun 19, 2020 to Jul 17, 2020

2021

No classes scheduled at this time.

2022

No classes scheduled at this time.

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Prerequisites

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

Recommended

We recommended, but do not require as eligibility to enroll in this course, an understanding of the material covered in these following courses.

R for Statistical Analysis

This course will teach you how to use R for basic statistical procedures.
Topic: Data Science, Using R | Skill: Intermediate | Credit Options: CAP, CEU

What Our Students Say​

I need all the modeling practice I can get in R. I thought this class was very helpful to that end and I plan to take additional courses as a result

Todd Kirschner
Cross Factor Capital

This course will greatly contribute to my work as an environmental data scientist and division director.  This course was a great introduction of how to use R to fit the models and how to interpret the R output!

Jonathan Jenkins

Frequently Asked Questions

What is your satisfaction guarantee and how does it work?

We offer a “Student Satisfaction Guarantee​” that includes a tuition-back guarantee, so go ahead and take our courses risk free. That’s our commitment to student satisfaction. Students may cancel, transfer, or withdraw from a course under certain conditions. If you’re not satisfied with a course, you may withdraw from the course and receive a tuition refund.

Please see our knowledge center for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: http://www.schev.edu

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

Generalized Linear Models

This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.
Topic: Statistics, Statistical Modeling | Skill: Intermediate, Advanced | Credit Options: CEU
Class Start Dates: Apr 10, 2020

Logistic Regression

This course will teach you logistic regression ordinary least squares (OLS) methods to model data with binary outcomes rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure. NOTE: Jan. 10 session is still open – register for a later class and email ourcourses@statistics.com to switch.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Jul 17, 2020

Modeling Count Data

This course will teach you regression models for count data, models with a response or dependent variable data in the form of a count or rate, Poisson regression, the foundation for modeling counts, and extensions and modifications to the basic model.
Topic: Statistics, Statistical Modeling | Skill: Intermediate, Advanced | Credit Options: CAP, CEU
Class Start Dates: Oct 23, 2020

R Programming – Introduction Part 1

This course provides an easy introduction to programming in R. NOTE: Jan. 10 session is still open – register for a later class and email ourcourses@statistics.com to switch.
Topic: Data Science, Using R | Skill: Introductory | Credit Options: ACE, CAP, CEU
Class Start Dates: May 15, 2020, Sep 11, 2020

Additional Course Information

Organization of Course

This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

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. 

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

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.

New to Statistics.com?  Click here for a special introductory discount code.  

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.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

INFORMS-CAP
This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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Modeling in R
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