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-
Welcome
- Welcome to Inferential Statistics with R
- Introduction to the Dataset
-
t-tests
- Independent t-test
- Dependent t-test
-
One-Way ANOVA
- One-Way ANOVA
- Post Hoc Comparisons
- Other ANOVA Tests
-
Chi-Square
- Chi-Square
- 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 t-test
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This lesson is called Dependent t-test, part of the Inferential Statistics with R course. This lesson is called Dependent t-test, part of the Inferential Statistics with R course.
Transcript
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Your Turn
Perform a dependent samples t-test to test whether there is a difference in exam scores:
exam_1
andexam_2
. Is there a difference? What is the p-value?Perform a dependent samples t-test to test whether there is a difference in
act_science
andact_mathematics
scores. Is there a difference? What is the p-value?
Don’t forget: You’ll need to make your data long before running your t-test.
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 tidyverse-friendly while some base R functions for inferential statistics are not. If you did want to use the base R equivalent function for a t-test, it is t.test()
.
For the dependent t-test 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!