Statistics with R - Intermediate Level
4.0 (201 ratings)
3,318 students enrolled

# Statistics with R - Intermediate Level

Statistical analyses using the R program
4.0 (201 ratings)
3,318 students enrolled
Created by Bogdan Anastasiei
Last updated 3/2016
English
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Current price: \$34.99 Original price: \$49.99 Discount: 30% off
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This course includes
• 2.5 hours on-demand video
• 9 articles
• Access on mobile and TV
• Certificate of Completion
Training 5 or more people?

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)
Course content
Expand all 33 lectures 02:24:57
+ Test of Association
6 lectures 19:18

How to run the Pearson correlation in R

Pearson Correlation
04:05
How to perform the Spearman and Kendall

correlation analyses in R

Spearman and Kendall Correlation
05:25

How to run and interpret a partial correlation

Partial Correlation
03:54
How to perform the chi-square test for independence
Chi-Square Test For Independence
05:42
All the codes used in the lectures 2-5, for your reference
R Codes File for the First Chapter
00:06

Practical exercises for the lectures 2-5

Practical Exercises for the First Chapter
00:06
+ Mean Difference Tests
14 lectures 01:24:03
How to perform the independent-

sample t test

Preview 07:56
How to execute the paired-sample t test
Paired-Sample T Test
03:54

How to run the one-way analysis of variance

Oneway ANOVA
13:05

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

Twoway ANOVA - Basics
06:12

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

Twoway ANOVA - Simple Main Effects
14:11

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

Threeway ANOVA - Basics
06:31
How to compute the simple second

order interaction effects in a three-way ANOVA

Threeway ANOVA - Simple Second Order Interaction Effects
03:58

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

Threeway ANOVA - Simple Main Effects
07:30

How to run the one-way multivariate analysis of variance

Oneway MANOVA
10:19
How to perform the Mann-Whitney test
Mann-Whitney Test
03:35
How to run the Wilcoxon test
Wilcoxon Test
03:21
How to perform the Kruskal-Wallis test
Kruskal-Wallis Test
03:19
All the codes used in the lectures 8-19, for your reference
R Codes File for the Second Chapter
00:06

Practical exercises for the lectures 8-19

Practical Exercises for the Second Chapter
00:06
+ Predictive Techniques
6 lectures 26:50

The essentials of the multiple linear regression

Multiple Linear Regression - Basics
07:54

How to test the most important assumptions for a multiple regression

Multiple Linear Regression - Testing Assumptions
10:16

How to perform the multiple regression with dummy variables

Multiple Regression with Dummy Variables
03:01

How to run the sequential (hierarchical) multiple regression

Sequential Regression
05:27
All the codes used in the lectures

R Codes File for the Third Chapter
00:06
Practical exercises for the lectures 22-25
Practical Exercises for the Third Chapter
00:06
+ Reliabilty Analysis
5 lectures 09:33

How to compute the Cronbach's alpha

Cronbach's Alpha
02:49

How to compute the Cohen's kappa

Cohen's Kappa
04:16

How to compute the Kendall's W

Kendall's W
02:16
All the codes used in the lectures 28-30, for your reference
R Codes File for the Fourth Chapter
00:06
Practical exercises for the lectures 28-30
Practical Exercises for the Fourth Chapter
00:06
+ Course Materials
1 lecture 00:03

00:03
Requirements
• R and R studio
• knowledge of statistics
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!
Who this course is for:
• students
• PhD candidates