R is unique among programming languages because it is designed for statistics. Unlike other general purpose languages that can work with data (e.g. Python), data analysis is at the core of what R does.
Given this, many people are disappointed when they struggle to figure out how to do inferential statistics with R. They say, “I know how to do a t-test in SPSS, I just want to do the same thing in R!” Sound familiar? If so, this course is designed for you.
This course won’t teach you to do fancy modeling, machine learning, or any other type of analysis currently en vogue. It will, however, teach you everything you need to know to do the core set of statistical tests that probably make up the majority of your work. In addition to t-tests, you’ll learn to do ANOVA, chi-square, correlation, regression, and more.
If learning to do inferential stats has been the barrier to you moving to R, this course can be the guide you need to make the switch. The time you invest in the course will repay itself tenfold, as you stop paying for expensive software and embrace the free (and more powerful!) alternative: R.
Are there any prerequisites for this course?
You should be familiar with two things before taking this course:
- The fundamentals of the tidyverse (see the content in the Fundamentals of R course).
- How inferential statistics course work. This course assumes you know what a t-test is. It is focused on showing you how to do a t-test (and much more!) in R.
Can I get a refund if I’m not satisfied?
Absolutely! If you are unsatisfied with the course for any reason, contact me and I will give you a full refund, no questions asked.
Is this course live or self-paced?
It is entirely self-paced, giving you the freedom to learn whenever and wherever you’d like to.
Does my access to the material expire at any point?
Nope! Sign up now and you’ll have access to the course forever.
About the Instructor
This course is taught by Dana Wanzer, Assistant Professor in the Psychology Department at the University of Wisconsin, Stout. An avid R user, Dana has extensive experience in advanced statistical techniques. She is well-versed in such techniques as exploratory and confirmatory factor analysis, structural equation modeling, multilevel modeling, and more.