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
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
2023
Instructors:
Prerequisites
R for Statistical Analysis
- Skill: Intermediate, Advanced
- Credit Options: CAP, CEU
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Stephen McAllister
I learned more in the past 6 weeks than I did taking a full semester of statistics in college, and 10 weeks of statistics in graduate school. Seriously.
Amir Aminimanizani
This is the best online course I have ever taken. Very well prepared. Covers a lot of real-life problems. Good job, thank you very much!
Elena Rose
The more courses I take at Statistics.com, the more appreciation I have for the smart approach, quality of instructors, assistants, admin and program. Well done!
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This course greatly benefited me because I am interested in working in AI. It has given me solid foundational knowledge…After completing this last course, I feel I have gained valuable skills that will enhance my employability in Data Science, opening up diverse career opportunities.
Richard Jackson
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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
- Color Enhancer (for colorblindness)
- HelperBird (for colorblindness, dyslexia, and reading difficulties)
Firefox
- Mobile Dyslexic
- Color Vision Simulation (native accessibility feature)
- Other native accessibility features instructions
Safari
- Navidys (for colorblindness, dyslexia, and reading difficulties)
- HelperBird for Safari (for colorblindness, dyslexia, and reading difficulties)