Statistics with R – Intermediate Level

Statistics with R – Intermediate Level, available at $49.99, has an average rating of 4.3, with 33 lectures, based on 384 reviews, and has 31746 subscribers.

You will learn about 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) This course is ideal for individuals who are students or PhD candidates or academic researchers or business researchers or University teachers or anyone looking for a job in the statistical analysis field or anyone who is passionate about quantitative analysis It is particularly useful for students or PhD candidates or academic researchers or business researchers or University teachers or anyone looking for a job in the statistical analysis field or anyone who is passionate about quantitative analysis.

Enroll now: Statistics with R – Intermediate Level

Summary

Title: Statistics with R – Intermediate Level

Price: $49.99

Average Rating: 4.3

Number of Lectures: 33

Number of Published Lectures: 33

Number of Curriculum Items: 33

Number of Published Curriculum Objects: 33

Original Price: $39.99

Quality Status: approved

Status: Live

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)

Who Should Attend

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

Target Audiences

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

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Test of Association

Lecture 1: Pearson Correlation

Lecture 2: Spearman and Kendall Correlation

Lecture 3: Partial Correlation

Lecture 4: Chi-Square Test For Independence

Lecture 5: R Codes File for the First Chapter

Lecture 6: Practical Exercises for the First Chapter

Chapter 3: Mean Difference Tests

Lecture 1: Independent-Sample T Test

Lecture 2: Paired-Sample T Test

Lecture 3: Oneway ANOVA

Lecture 4: Twoway ANOVA – Basics

Lecture 5: Twoway ANOVA – Simple Main Effects

Lecture 6: Threeway ANOVA – Basics

Lecture 7: Threeway ANOVA – Simple Second Order Interaction Effects

Lecture 8: Threeway ANOVA – Simple Main Effects

Lecture 9: Oneway MANOVA

Lecture 10: Mann-Whitney Test

Lecture 11: Wilcoxon Test

Lecture 12: Kruskal-Wallis Test

Lecture 13: R Codes File for the Second Chapter

Lecture 14: Practical Exercises for the Second Chapter

Chapter 4: Predictive Techniques

Lecture 1: Multiple Linear Regression – Basics

Lecture 2: Multiple Linear Regression – Testing Assumptions

Lecture 3: Multiple Regression with Dummy Variables

Lecture 4: Sequential Regression

Lecture 5: R Codes File for the Third Chapter

Lecture 6: Practical Exercises for the Third Chapter

Chapter 5: Reliabilty Analysis

Lecture 1: Cronbach's Alpha

Lecture 2: Cohen's Kappa

Lecture 3: Kendall's W

Lecture 4: R Codes File for the Fourth Chapter

Lecture 5: Practical Exercises for the Fourth Chapter

Chapter 6: Course Materials

Lecture 1: Download Links

Instructors

Bogdan Anastasiei
University Teacher and Consultant

Rating Distribution

1 stars: 8 votes
2 stars: 10 votes
3 stars: 53 votes
4 stars: 140 votes
5 stars: 173 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!

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