Master statistical programming with R. Learn regression, hypothesis testing, and more through practical exercises in this comprehensive tutorial.
This course is all about making statistics simple with R! You’ll dive into important tests and regression modeling while getting practical experience. Perfect for anyone looking to enhance their skills in statistical programming.
Statistics made easy with the open source R language. Learn about Regression, Hypothesis tests, R Commander ...
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.
What R you waiting for?
Martin
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
3 Lectures
19 Lectures
4 Lectures
11 Lectures
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The explanation was good, but could have used more detailed examples. More variation in dataset. Could have used own data rather than in built data. Overall it was good.
Yes, a very good match. Would like to see these applications used for much larger data sets to truly garner the power and how to use the results to make decisions
super
I was expecting to have access to the dataset used in the tutorial, along with more detailed information about each test. For instance, examples for each variable would be helpful, or guidance on how to install the necessary packages. Additionally, I have a suggestion: it would be beneficial if the teacher wrote the code simultaneously instead of just running it.
Clear descriptions stage by stage.
Takes some time to get used to the terminology for a beginner with little background, but a little self digging, this becomes a valuable resource.
Presentation is bit boring. Also, some of the things are already known but most importantly the presentation style might be made little better. However, hoping for better contents in the upcoming videos.
Much clearer than R Level 1. Examples were more well explained. I gained a good overview of implementing statistics with R. Responses to questions are not so prompt.
it is helping out a lot with a uni stats course
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Edwin K. L.
Excellent