Some of the advanced features under the Graph Driven Test (GDT) or Hypothesis Test functions are only available for users with a College license, giving them the DataClassroom U feature set.
Read more about how to get the College features here.
Once in DataClassroom U mode, the advanced features will be shown in the test selection window, and the advanced-level tests will be included in the list:
Non-parametric tests
Non-parametric tests are the ones to choose if you know your data are not normally distributed. You can choose:
- The Mann-Whitney U test (also known as Wilcoxon rank-sum test) which is a non-parametric version of the T-test
- The Kruskal-Wallis test which is a non-parametric alternative to ANOVA
Multiple Linear Regression
Multiple Linear Regression allows you to simultaneously look at relationships between a single Dependent Variable and multiple Independent variables.
Paired T-Test
This tests for a difference in the average value of a numeric variable between two different groups, which are paired by an identifier (name, ID, etc.). For example when a measurement has been made before and after a treatment for each member of a set of individuals.
Repeated Measures ANOVA
This is similar to the Paired T-Test except more than two samples have been taken per identifier (e.g. a measurement has been taken 3 times for each individual).
ANCOVA
ANCOVA (ANalysis of COVAriance) tests for the effect of an independent categorical variable (IV) on a numeric dependent variable (DV) while controlling for the effect of a third variable (numeric covariate).
Running tests in R
The "Try it in R" button takes you to the "Bridge to R" window. Read more about that here.