Statistics with R - Intermediate Level

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Bogdan Anastasiei

2:24:04

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1. Introduction

1. Introduction.mp4

05:09

2. Test of Association

1. Pearson Correlation.mp4

04:05

2. Spearman and Kendall Correlation.mp4

05:25

3. Partial Correlation.mp4

03:54

4. Chi-Square Test For Independence.mp4

05:42

5.1 intermediate section 1.pdf

5. R Codes File for the First Chapter.html

6.1 Practice intermediate section 1.pdf

6. Practical Exercises for the First Chapter.html

3. Mean Difference Tests

1. Independent-Sample T Test.mp4

07:56

2. Paired-Sample T Test.mp4

03:54

3. Oneway ANOVA.mp4

13:04

4. Twoway ANOVA - Basics.mp4

06:12

5. Twoway ANOVA - Simple Main Effects.mp4

14:11

6. Threeway ANOVA - Basics.mp4

06:31

7. Threeway ANOVA - Simple Second Order Interaction Effects.mp4

03:58

8. Threeway ANOVA - Simple Main Effects.mp4

07:30

9. Oneway MANOVA.mp4

10:19

10. Mann-Whitney Test.mp4

03:35

11. Wilcoxon Test.mp4

03:21

12. Kruskal-Wallis Test.mp4

03:19

13.1 intermediate section 2.pdf

13. R Codes File for the Second Chapter.html

14.1 Practice intermediate section 2.pdf

14. Practical Exercises for the Second Chapter.html

4. Predictive Techniques

1. Multiple Linear Regression - Basics.mp4

07:54

2. Multiple Linear Regression - Testing Assumptions.mp4

10:16

3. Multiple Regression with Dummy Variables.mp4

03:01

4. Sequential Regression.mp4

05:27

5.1 intermediate section 3.pdf

5. R Codes File for the Third Chapter.html

6.1 Practice intermediate section 3.pdf

6. Practical Exercises for the Third Chapter.html

5. Reliabilty Analysis

1. Cronbachs Alpha.mp4

02:49

2. Cohens Kappa.mp4

04:16

3. Kendalls W.mp4

02:16

4.1 intermediate section 4.pdf

4. R Codes File for the Fourth Chapter.html

5.1 Practice intermediate section 4.pdf

5. Practical Exercises for the Fourth Chapter.html

6. Course Materials

1. Download Links.html

Description

Statistical analyses using the R program

What You'll 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 is this for?

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 Need to Know?

R and R studio

knowledge of statistics

More details

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 donâ€™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 Cronbachâ€™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 â€“ 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. Donâ€™t wait, enroll today and get ready for an exciting journey!
Who this course is for:
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

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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 24 years experience in teaching and about 15 years experience in business consulting.

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