Statistics with R Advanced Level_1

Statistics with R – Advanced Level, available at $49.99, has an average rating of 4.4, with 37 lectures, based on 213 reviews, and has 28179 subscribers.

You will learn about 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 This course is ideal for individuals who are students or PhD candidates or academic researchers or business researchers or University teachers or anyone looking for a job in the statistical analysis field or anyone who is passionate about quantitative analysis It is particularly useful for students or PhD candidates or academic researchers or business researchers or University teachers or anyone looking for a job in the statistical analysis field or anyone who is passionate about quantitative analysis.

Enroll now: Statistics with R – Advanced Level

Summary

Title: Statistics with R – Advanced Level

Price: $49.99

Average Rating: 4.4

Number of Lectures: 37

Number of Published Lectures: 37

Number of Curriculum Items: 37

Number of Published Curriculum Objects: 37

Original Price: $39.99

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Chapter 2: Mean Difference Tests

Lecture 1: The Analysis of Covariance

Lecture 2: ANCOVA – Checking Assumptions

Lecture 3: Within-Subjects ANOVA

Lecture 4: Within-Subjects ANOVA – Paired Comparisons

Lecture 5: Within-Within Subjects ANOVA

Lecture 6: Within-Within Subjects ANOVA – Main Effects (1)

Lecture 7: Within-Within Subjects ANOVA – Main Effects (2)

Lecture 8: Mixed ANOVA

Lecture 9: Mixed ANOVA – Main Effects

Lecture 10: Friedman Test

Lecture 11: R Codes File for the First Chapter

Lecture 12: Practical Exercises for the First Chapter

Chapter 3: Predictive Techniques

Lecture 1: Binomial Regression

Lecture 2: Binomial Regression – Goodness-of-Fit Measures

Lecture 3: Multinomial Regression Basics

Lecture 4: Multinomial Regression – Interpreting the Coefficients

Lecture 5: Multinomial Regression – Goodness-of-Fit Measures

Lecture 6: Ordinal Regression

Lecture 7: Ordinal Regression – Interpreting the Coefficients

Lecture 8: Ordinal regression – Goodness-of-Fit Measures

Lecture 9: Ordinal Regression – Assumption of Proportional Odds

Lecture 10: R Codes File for the Second Chapter

Lecture 11: Practical Exercises for the Second Chapter

Chapter 4: Grouping Methods

Lecture 1: Multidimensional Scaling When Data Are Not Distances

Lecture 2: Multidimensional Scaling When Data Are Distances

Lecture 3: Factor Analysis Basics

Lecture 4: Factor Analysis – Sample Adequacy Measures

Lecture 5: Simple Correspondence Analysis

Lecture 6: Multiple Correspondence Analysis

Lecture 7: Hierarchical Cluster

Lecture 8: K-means Cluster

Lecture 9: Simple Discriminant Analysis

Lecture 10: Multiple Discriminant Analysis

Lecture 11: R Codes File for the Third Chapter

Lecture 12: Practical Exercises for the Third Chapter

Chapter 5: Course Materials

Lecture 1: Download Links

Instructors

Bogdan Anastasiei
University Teacher and Consultant

Rating Distribution

1 stars: 6 votes

2 stars: 6 votes

3 stars: 27 votes

4 stars: 83 votes

5 stars: 91 votes

Frequently Asked Questions

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