Statistics with R – Advanced Level
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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