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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
Dependent ttest
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This lesson is called Dependent ttest, part of the Inferential Statistics with R course. This lesson is called Dependent ttest, part of the Inferential Statistics with R course.
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Your Turn
Perform a dependent samples ttest to test whether there is a difference in exam scores:
exam_1
andexam_2
. Is there a difference? What is the pvalue?Perform a dependent samples ttest to test whether there is a difference in
act_science
andact_mathematics
scores. Is there a difference? What is the pvalue?
Don’t forget: You’ll need to make your data long before running your ttest.
Learn More
We are using the rstatix
package for the t_test()
function (and other functions in later lessons). We use this package because it is tidyversefriendly while some base R functions for inferential statistics are not. If you did want to use the base R equivalent function for a ttest, it is t.test()
.
For the dependent ttest using the t_test()
function, you need to pivot the data set from wide to long using the pivot_longer()
function in the tidyr package. Read this vignette for more information on the new pivoting functions.
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soundarya soundararajan
March 25, 2021
Hi thanks for the great session. I am writing to bring to your kind notice that there is an error in the solution 2. where it reads names_prefix = "weight_", whereas it should read names_prefix="act_"
Thanks!