statistics in r

Statistics in R – R for Statistical Analysis

Is the Statistics in R course for you?

  • Are you a R user?
  • Do you want to learn more about statistical programming?
  • Are you in a quantitative field?
  • You want to know how to perform statistical tests and regressions?
  • Do you want to hack the learning curve and stay ahead of your competition?

If YES came to your mind to some of those points – read on!


This tutorial will teach you anything you need to know about descriptive and inferential statistics as well as regression modeling in R.

While planing this course we were focusing on the most important inferential tests that cover the most common statistical questions.

After finishing this course you will understand when to use which specific test and you will also be able to perform these tests in R.

Furthermore you will also get a very good understanding of regression modeling in R. You will learn about multiple linear regressions as well as logistic regressions.

According to the teaching principles of R Tutorials every section is enforced with exercises for a better learning experience. You can download the code pdf of every section to try the presented code on your own.

Should you need a more basic course on R programming we would highly recommend our R Level 1 course. The Level 1 course covers all the basic coding strategies that are essential for your day to day programming.

Since data visualization is such an important part within data science you can also take a look at our Graphs in R course. This course gives you in depth knowledge about R plotting devices. Nowhere else on the web can you find such an extended course about this crucial topic.

What are the requirements?

  • solid understanding of R programming – up to my “R Level 1” course
  • R and R Studio ready on your computer
  • basic understanding of statistics (descriptive, inferential, regression)
  • high interest in data analysis

What am I going to get from this course?

  • Over 30 lectures and 3 hours of content!
  • know which statistical test to use for a given question
  • know how to perform the most important statistical tests in R
  • know how to perform regression modeling in R
  • have a very good understand of statistical testing and regressions

What is the target audience?

  • students who need data analysis in their work
  • data analysts
  • entrepreneurs with quantitative interests
  • everybody interested in statistics

What our students think

“The instructor does a great job of showing how to get things set up and running in R. I took an R course on Coursera and came here looking for a real tutorial where you could see someone type things into R. This class is excellent, I learned how to actually do things in R.”

“I liked the course as a result of preceding 3 consecutive courses of R by the same instructor, who developed the courses very systematic and step by step process and made me comfortable with the learning process. Thanks a lot, Martin.”

“1 month ago, I had never used R. I have statistics knowledge, so I wanted to learn a new tool to perform analysis with. I took R Basics as my first course and enjoyed it so much that I decided try R Statistics. Both courses are unbelievably well organized, and all of the videos flow seamlessly and logically from one topic to the next topic. The instructor is very clear and gives great explanations, so I never felt overwhelmed. Not to mention, the course covers a lot of very important and useful analytical techniques. By the end, I was confident enough to upload my own datasets and start working on them. I’m strongly considering taking R Level 1 next to continue to build my foundation.”

“The four courses viz., R Basics, R Level 1, Statistics in R and Graphics in R together form a comprehensive set of courses on R. Structured, logical and systematic approach provides the confidence that the learner desires very quickly. Statistics in R is very unique course not found elsewhere. Graphics in R provides overall and almost complete picture of various plotting packages in R and their relative significance. Good way to learn R. Martin, thanks a lot for creating these courses.”

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statistics in r

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