Development
Data Science
R
Statistics with R - Advanced Level
4.1 (127 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
12,972 students enrolled

Statistics with R - Advanced Level

Advanced statistical analyses using the R program
4.1 (127 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
12,972 students enrolled
Created by Bogdan Anastasiei
Last updated 3/2016
English
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Statistics with R - Advanced Level
Current price: $34.99 Original price: $49.99 Discount: 30% off
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This course includes
  • 4.5 hours on-demand video
  • 7 articles
  • 6 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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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
Expand all 37 lectures 04:15:26
+ – Mean Difference Tests
12 lectures 01:38:18

The basics of the analysis of covariance - ANCOVA

The Analysis of Covariance
Preview 11:59

How to check the assumptions of the analysis of covariance

ANCOVA - Checking Assumptions
06:31

The basics of the within-subjects analysis of variance

Within-Subjects ANOVA
15:04

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

Within-Subjects ANOVA - Paired Comparisons
05:11
The within-within subjects

analysis of variance - basics

Within-Within Subjects ANOVA
13:45
Within-Within Subjects ANOVA - Main Effects (1)
10:01
How to compute the main effects in the within-within subjects ANOVA

(continued)

Within-Within Subjects ANOVA - Main Effects (2)
08:45

Mixed analysis of variance - the basics

Mixed ANOVA
15:57

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

Mixed ANOVA - Main Effects
08:35

How to perform the Friedman test

Friedman Test
02:18

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

R Codes File for the First Chapter
00:06

Practical exercises for the lectures 2-11

Practical Exercises for the First Chapter
00:06
+ – Predictive Techniques
11 lectures 01:02:54

The basics of the binomial logistic regression

Binomial Regression
13:51

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

Binomial Regression - Goodness-of-Fit Measures
03:37

The basics of the multinomial logistic regression

Multinomial Regression Basics
08:23

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

Multinomial Regression - Interpreting the Coefficients
09:44

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

Multinomial Regression - Goodness-of-Fit Measures
04:17

The basics of the ordinal logistic regression

Ordinal Regression
11:58

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

Ordinal Regression - Interpreting the Coefficients
04:51

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

Ordinal regression - Goodness-of-Fit Measures
04:15

How to check the assumption of proportional odds

Ordinal Regression - Assumption of Proportional Odds
01:46

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

R Codes File for the Second Chapter
00:06

Practical exercises for the lectures 14-22

Practical Exercises for the Second Chapter
00:06
+ – Grouping Methods
12 lectures 01:29:41

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

Multidimensional Scaling When Data Are Not Distances
06:03

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

Multidimensional Scaling When Data Are Distances
04:51

The basics of the factor analysis technique

Factor Analysis Basics
09:43

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

Factor Analysis - Sample Adequacy Measures
03:08

How to perform a simple correspondence analysis in R

Simple Correspondence Analysis
10:43

How to run a multiple correspondence analysis in R

Multiple Correspondence Analysis
09:21
How to run a hierarchical cluster

analysis

Hierarchical Cluster
08:32
How to perform a K-means cluster
K-means Cluster
12:37

How tu run a simple discriminant analysis in R

Simple Discriminant Analysis
13:14

How to execute a multiple discriminant analysis

Multiple Discriminant Analysis
11:17

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

R Codes File for the Third Chapter
00:06

Practical exercises for the lectures 25-34

Practical Exercises for the Third Chapter
00:06
+ – Course Materials
1 lecture 00:03

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

Download Links
00:03
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