Statistics with R - Advanced Level

Advanced statistical analyses using the R program

Created byBogdan Anastasiei

Last updated 12/2020

English

What you'll learn

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

Course content

5 sections • 37 lectures • 4h 15m total length

Introduction4:28

The Analysis of Covariance11:59
The basics of the analysis of covariance - ANCOVA
ANCOVA - Checking Assumptions6:31
How to check the assumptions of the analysis of covariance
Within-Subjects ANOVA15:04
The basics of the within-subjects analysis of variance
Within-Subjects ANOVA - Paired Comparisons5:11
How to perform paired comparisons in the within-subjects analysis of variance
Within-Within Subjects ANOVA13:45
The within-within subjects
analysis of variance - basics
Within-Within Subjects ANOVA - Main Effects (1)10:01
Within-Within Subjects ANOVA - Main Effects (2)8:45
How to compute the main effects in the within-within subjects ANOVA
(continued)
Mixed ANOVA15:57
Mixed analysis of variance - the basics
Mixed ANOVA - Main Effects8:35
How to compute the main effects in the mixed analysis of variance
Friedman Test2:18
How to perform the Friedman test
R Codes File for the First Chapter0:06
All the codes used in the lectures 2-11, for your reference
Practical Exercises for the First Chapter0:06
Practical exercises for the lectures 2-11

Binomial Regression13:51
The basics of the binomial logistic regression
Binomial Regression - Goodness-of-Fit Measures3:37
How to compute the goodness-of-fit indicators for the binomial regression
Multinomial Regression Basics8:23
The basics of the multinomial logistic regression
Multinomial Regression - Interpreting the Coefficients9:44
How to interpret the coefficients (antilogs) of the multinomial logistic regression
Multinomial Regression - Goodness-of-Fit Measures4:17
How to compute the goodness-of-fit indicators for a multinomial regression
Ordinal Regression11:58
The basics of the ordinal logistic regression
Ordinal Regression - Interpreting the Coefficients4:51
How to interpret the coefficients (antilogs) of the ordinal logistic regression
Ordinal regression - Goodness-of-Fit Measures4:15
How to compute the goodness-of-fit indicators for an ordinal regression
Ordinal Regression - Assumption of Proportional Odds1:46
How to check the assumption of proportional odds
R Codes File for the Second Chapter0:06
All the codes used in the lectures 14-22, for your reference
Practical Exercises for the Second Chapter0:06
Practical exercises for the lectures 14-22

Multidimensional Scaling When Data Are Not Distances6:03
How to run a multidimensional scaling when data are NOT distances between objects
Multidimensional Scaling When Data Are Distances4:51
How to run a multidimensional scaling when data ARE distances between objects
Factor Analysis Basics9:43
The basics of the factor analysis technique
Factor Analysis - Sample Adequacy Measures3:08
How to compute the sample adequacy indicators for the factor analysis, in R
Simple Correspondence Analysis10:43
How to perform a simple correspondence analysis in R
Multiple Correspondence Analysis9:21
How to run a multiple correspondence analysis in R
Hierarchical Cluster8:32
How to run a hierarchical cluster
analysis
K-means Cluster12:37
How to perform a K-means cluster
Simple Discriminant Analysis13:14
How tu run a simple discriminant analysis in R
Multiple Discriminant Analysis11:17
How to execute a multiple discriminant analysis
R Codes File for the Third Chapter0:06
All the codes used in the lectures 25-34, for your reference
Practical Exercises for the Third Chapter0:06
Practical exercises for the lectures 25-34

Requirements

R and R studio
knowledge of advanced statistics

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 don’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, what’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 – 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 – 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 – 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 don’t wait, enroll today!

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

Statistics with R - Advanced Level

What you'll learn

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Course content

Introduction1 lecture • 4min

Mean Difference Tests12 lectures • 1hr 38min

Predictive Techniques11 lectures • 1hr 3min

Grouping Methods12 lectures • 1hr 30min

Course Materials1 lecture • 1min

Requirements

Description

Who this course is for: