# Statistics with R - Advanced Level

Advanced statistical analyses using the R program
4.5 (11 ratings) â€¢ 268 students enrolled
Instructed by Bogdan Anastasiei
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• Lectures 37
• Length 4.5 hours
• Skill Level Expert Level
• Languages English
• Includes Lifetime access
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Available on iOS and Android
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Published 3/2016 English

### Course Description

If you want to learn how to perform real advanced 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 an analysis of covariance or a mixed analysis of variance, how to execute a binomial logistic regression, how to perform a multidimensional scaling or a factor analysis. Everything is here, in this course, explained visually, step by step.

So, whatâ€™s covered in this course?

First of all, we are going to study some more techniques to evaluate the mean differences. If you took the intermediate course- which I highly recommend you â€“ you learned about the t tests and the between-subjects analysis of variance. Now we will go to the next level and tackle the analysis of covariance, the within-subjects analysis of variance and the mixed analysis of variance.

Next, in the section about the predictive techniques, we will approach the logistic regression, which is used when the dependent variable is not continuous â€“ in other words, it is categorical. We are going to study three types of logistic regression: binomial, ordinal and multinomial.

Then we are going to deal with the grouping techniques. Here you will find out, in detail, how to perform the multidimensional scaling, the principal component analysis and the factor analysis, the simple and the multiple correspondence analysis, the cluster analysis (both k-means and hierarchical) , the simple and the multiple discriminant analysis.

So after finishing this course, you will be a real expert in statistical analysis with R â€“ you will know a lot of sophisticated, state-of-the art analysis techniques that will allow you to deeply scrutinize your data and get the most information out of it. So donâ€™t wait, enroll today!

### What are the requirements?

• R and R studio
• knowledge of advanced statistics

### What am I going to get from this course?

• perform the analysis of covariance
• run the one-way within-subjects analysis of variance
• run the two-way within-subjects analysis of variance
• run the mixed analysis of variance
• perform the non-parametric Friedman test
• execute the binomial logistic regression
• run the multinomial logistic regression
• perform the ordinal logistic regression
• perform the multidimensional scaling
• perform the principal component analysis and the factor analysis
• run the simple and multiple correspondence analysis
• run the cluster analysis (k-means and hierarchical)
• run the simple and multiple discriminant analysis

### Who is the target audience?

• students
• PhD candidates
• 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.

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Get rewarded.
Certificate of completion.

# Curriculum

Section 1: Introduction
Introduction
04:28
Section 2: Mean Difference Tests
11:59

The basics of the analysis of covariance - ANCOVA

06:31

How to check the assumptions of the analysis of covariance

15:04

The basics of the within-subjects analysis of variance

05:11

How to perform paired comparisons in the within-subjects analysis of variance

13:45
The within-within subjects

analysis of variance - basics

Within-Within Subjects ANOVA - Main Effects (1)
10:01
08:45
How to compute the main effects in the within-within subjects ANOVA

(continued)

15:57

Mixed analysis of variance - the basics

08:35

How to compute the main effects in the mixed analysis of variance

02:18

How to perform the Friedman test

00:06

All the codes used in the lectures 2-11, for your reference

00:06

Practical exercises for the lectures 2-11

Section 3: Predictive Techniques
13:51

The basics of the binomial logistic regression

03:37

How to compute the goodness-of-fit indicators for the binomial regression

08:23

The basics of the multinomial logistic regression

09:44

How to interpret the coefficients (antilogs) of the multinomial logistic regression

04:17

How to compute the goodness-of-fit indicators for a multinomial regression

11:58

The basics of the ordinal logistic regression

04:51

How to interpret the coefficients (antilogs) of the ordinal logistic regression

04:15

How to compute the goodness-of-fit indicators for an ordinal regression

01:46

How to check the assumption of proportional odds

00:06

All the codes used in the lectures 14-22, for your reference

00:06

Practical exercises for the lectures 14-22

Section 4: Grouping Methods
06:03

How to run a multidimensional scaling when data are NOT distances between objects

04:51

How to run a multidimensional scaling when data ARE distances between objects

09:43

The basics of the factor analysis technique

03:08

How to compute the sample adequacy indicators for the factor analysis, in R

10:43

How to perform a simple correspondence analysis in R

09:21

How to run a multiple correspondence analysis in R

08:32
How to run a hierarchical cluster

analysis

12:37
How to perform a K-means cluster
13:14

How tu run a simple discriminant analysis in R

11:17

How to execute a multiple discriminant analysis

00:06

All the codes used in the lectures 25-34, for your reference

00:06

Practical exercises for the lectures 25-34

Section 5: Course Materials
00:03

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