Statistical analyses using the R program

What you will learn

run parametric and non-parametric correlation (Pearson, Spearman, Kendall)

perform partial correlation

run the chi-square test for association

run the independent sample t test

run the paired sample t test

execute the one-way analysis of variance

perform the two-way and three-way analysis of variance

run the one-way multivariate analysis of variance

run non-parametric tests for mean difference (Mann-Whitney, Kruskal-Wallis, Wilcoxon)

execute the multiple linear regression

compute the Cronbach’s alpha

compute other reliability indicators (Cohen’s kappa, Kendall’s W)


If you want to learn how to perform the most useful statistical analyses in the R program, you have come to the right place.

Now you don’t have to scour the web endlessly in order to find how to do a Pearson or Spearman correlation, an independent t test or a factorial ANOVA, how to perform a sequential regression analysis or how to compute the Cronbach’s alpha. Everything is here, in this course, explained visually, step by step.

So, what will you learn in this course?

First of all, you will learn how to perform association tests in R, both parametric and non-parametric: the Pearson correlation, the Spearman and Kendall correlation, the partial correlation and the chi-square test for independence.

The test of mean differences represent a vast part of this course, because of their great importance. We will approach the t tests, the analysis of variance (both univariate and multivariate) and a few non-parametric tests. For each technique we will present the preliminary assumption, run the procedure and carefully interpret all the results.

Next you will learn how to perform a multiple linear regression analysis. We have assign several big lectures to this topic, because we will also learn how to check the regression assumptions and how to run a sequential (or hierarchical) regression in R.

Finally, we will enter the territory of statistical reliability – you will learn how to compute three important reliability indicators in R.

So after graduating this course, you will get some priceless statistical analysis knowledge and skills using the R program. Don’t wait, enroll today and get ready for an exciting journey!






Test of Association

Pearson Correlation

Spearman and Kendall Correlation

Partial Correlation

Chi-Square Test For Independence

R Codes File for the First Chapter

Practical Exercises for the First Chapter

Mean Difference Tests

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Independent-Sample T Test

Paired-Sample T Test

Oneway ANOVA

Twoway ANOVA – Basics

Twoway ANOVA – Simple Main Effects

Threeway ANOVA – Basics

Threeway ANOVA – Simple Second Order Interaction Effects

Threeway ANOVA – Simple Main Effects


Mann-Whitney Test

Wilcoxon Test

Kruskal-Wallis Test

R Codes File for the Second Chapter

Practical Exercises for the Second Chapter

Predictive Techniques

Multiple Linear Regression – Basics

Multiple Linear Regression – Testing Assumptions

Multiple Regression with Dummy Variables

Sequential Regression

R Codes File for the Third Chapter

Practical Exercises for the Third Chapter

Reliabilty Analysis

Cronbach’s Alpha

Cohen’s Kappa

Kendall’s W

R Codes File for the Fourth Chapter

Practical Exercises for the Fourth Chapter

Course Materials

Download Links