Statistics with R - Intermediate Level

Statistical analyses using the R program
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  • Lectures 33
  • Length 2.5 hours
  • Skill Level Intermediate Level
  • Languages English
  • Includes Lifetime access
    30 day money back guarantee!
    Available on iOS and Android
    Certificate of Completion
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About This Course

Published 3/2016 English

Course Description

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!

What are the requirements?

  • R and R studio
  • knowledge of statistics

What am I going to get from this course?

  • 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)

Who is the target audience?

  • students
  • PhD candidates
  • academic researchers
  • business researchers
  • University teachers
  • anyone looking for a job in the statistical analysis field
  • anyone who is passionate about quantitative analysis

What you get with this course?

Not for you? No problem.
30 day money back guarantee.

Forever yours.
Lifetime access.

Learn on the go.
Desktop, iOS and Android.

Get rewarded.
Certificate of completion.


Section 1: Introduction
Section 2: Test of Association

How to run the Pearson correlation in R

How to perform the Spearman and Kendall

correlation analyses in R


How to run and interpret a partial correlation

How to perform the chi-square test for independence
All the codes used in the lectures 2-5, for your reference

Practical exercises for the lectures 2-5

Section 3: Mean Difference Tests
How to perform the independent-

sample t test

How to execute the paired-sample t test

How to run the one-way analysis of variance


How to perform the two-way analysis of variance - the basics


How to compute the simple main effects in a two-way ANOVA


How to perform the three-way analysis of variance - the basics

How to compute the simple second

order interaction effects in a three-way ANOVA


How to compute the simple main effects in a three-way ANOVA


How to run the one-way multivariate analysis of variance

How to perform the Mann-Whitney test
How to run the Wilcoxon test
How to perform the Kruskal-Wallis test
All the codes used in the lectures 8-19, for your reference

Practical exercises for the lectures 8-19

Section 4: Predictive Techniques

The essentials of the multiple linear regression


How to test the most important assumptions for a multiple regression


How to perform the multiple regression with dummy variables


How to run the sequential (hierarchical) multiple regression

All the codes used in the lectures

22-25, for your reference

Practical exercises for the lectures 22-25
Section 5: Reliabilty Analysis

How to compute the Cronbach's alpha


How to compute the Cohen's kappa


How to compute the Kendall's W

All the codes used in the lectures 28-30, for your reference
Practical exercises for the lectures 28-30
Section 6: Course Materials

Here you can download the CSV files and the R files.

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Instructor Biography

Bogdan Anastasiei, Learn with an Experienced University Teacher

My name is Bogdan Anastasiei and I am an assistant professor at the University of Iasi, Romania, Faculty of Economics and Business Administration. I teach Internet marketing and quantitative methods for business. I am also a business consultant. I have run quantitative risk analyses and feasibility studies for various local businesses and been implied in academic projects on risk analysis and marketing analysis. I have also written courses and articles on Internet marketing and online communication techniques. I have about 20 years experience in teaching and about 10 years experience in business consulting.

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