Get access to all lessons in this course.

Welcome
 Welcome to Inferential Statistics with R
 Introduction to the Dataset

ttests
 Independent ttest
 Dependent ttest

OneWay ANOVA
 OneWay ANOVA
 Post Hoc Comparisons
 Other ANOVA Tests

ChiSquare
 ChiSquare
 Dealing with Small Cells

Correlation
 Correlation

Regression
 Linear Regression
 Multiple Regression
 Hierarchical Regression

Reliability
 Reliability

Reporting Results
 Extracting Output
 Reporting Results

Testing Assumptions
 Testing Assumptions
 Testing for Normality
 Testing for Homogeneity of Variance
 Violated Assumptions
Inferential Statistics with R
Violated Assumptions
This lesson is locked
This lesson is called Violated Assumptions, part of the Inferential Statistics with R course. This lesson is called Violated Assumptions, part of the Inferential Statistics with R course.
Transcript
Click on the transcript to go to that point in the video. Please note that transcripts are auto generated and may contain minor inaccuracies.
Your Turn
Try the various transformations on the age
variable. Do any of them improve the normality of the variable? Test using the shapiro_test()
function as you learned in the “Testing assumptions” lesson.
Learn More
Read more about the Winsorize() function in the DescTools package.
Read more about applying transformations in R.
Read more about performing the nonparametric equivalent statistics in the rstatix package:
wilcox_test() as the nonparametric equivalent of the independent samples ttest (note: this is synonymous of the MannWhitney U test)
sign_test() as the nonparametric equivalent of the dependent samples ttest
kruskall_test() as the nonparametric equivalent of the oneway ANOVA
friedman_test() as the nonparametric equivalent of the oneway repeated measures ANOVA
You need to be signedin to comment on this post. Login.