Explanation
R Statistics – Intermediate If you want to learn how to do the most useful statistical analysis in R, you’ve come to the right place. Now you don’t have to endlessly scour the web to find how to do a Pearson or Spearman correlation, an independent test or factorial ANOVA, how to do a sequential regression analysis or how to calculate Cronbach’s alpha. Everything is here, in this course, explained visually, step by step. First, you will learn how to perform association tests in R, both parametric and non-parametric: Pearson correlation, Spearman and Kendall correlation, partial correlation and chi-square test of independence. Mean variance tests represent a large part of this course, because of their great importance. We will approach t-tests, analysis of variance (both variance and variance) and several non-parametric tests.
For each technique we will introduce some preliminary assumptions, run the system and carefully interpret all the results. Next you will learn how to perform multiple linear regression analysis. We have dedicated several large lectures to this topic, because we will also learn how to check regression hypotheses and how to run regression (or hierarchy) in R. Finally, we will enter the territory of statistical confidence. – you will learn how to calculate three important and reliable indicators in R. So after completing this course, you will gain some invaluable statistical analysis knowledge and skills using the R program. Don’t wait, sign up today and get ready for an exciting trip!
What will you learn?
- Run parametric and non-parametric tests (Pearson, Spearman, Kendall)
- create a link section
- run the chi-square test for association
- Perform the independent t test
- Perform a paired sample t test
- Perform one-way analysis of variance
- perform two-way and three-way analysis of variance
- run a one-way analysis of variance
- Perform non-parametric tests of variance (Mann-Whitney, Kruskal-Wallis, Wilcoxon)
- perform multiple linear regressions
- calculate Cronbach’s alpha
- calculate other reliability indicators (Cohen’s kappa, Kendall’s W)
Who is this course for?
- students
- PhD candidates
- academic researchers
- business researchers
- University teachers
- anyone seeking employment in the field of statistical analysis
- anyone interested in quantitative analysis
Special Statistical R – Intermediate Level
- Publisher: Udemy
- Teacher: Bogdan Anastasia
- Language : English
- Level: Medium
- Number of courses: 33
- Duration : 2 hours and 24 minutes
Statistical Content with R – Intermediate Level
Requirements
- R and R studio
- statistical knowledge
Pictures
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Subtitle : English
Quality: 720p
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file size
1.85GB