Statistics - Analysis of variance (Anova)
Table of Contents
1 - About
Anova is just a special case of multiple regression.
There're many forms of ANOVA. It's a very common procedure in basic statistics.
Anova is more Appropriate when:
- there's true independent variable
It's most common application is to analyze data from randomized controlled experiments (ie experimental research) but it can be used in non-experimental context as well.
If we only generate two group means (only 2 means) then we can just do t-tests :
Anova is used more specifically for randomized experiments that generate more than 2 two group means (two means).
During an experimental research, if two group means are generated and that we want to compare those group means, then we'll engage in ANOVA.
- if the groups are all independent then we call that a “between groups ANOVA”.
- if the groups are all coming from the same subjects, then we call that “repeated measures ANOVA”.
During Independent t-test, there is multiple pairwise comparisons and this is a tedious task. There should be one procedure to do that in one step, and that's ANOVA.
2 - Articles Related
3 - Test
ANOVA typically involves NHST, but it doesn't have to
An ANOVA will tell with the F-ratio if:
- there is an effect overall
- there is significant difference somewhere
The Post-hoc tests is used to figure out exactly where there are significant differences.
4 - Type
5 - True / False
5.1 - True
- a form of multiple regression where the predictors are not correlated
- an analysis used when the relationship between the independent variable and the dependent variable are linear and additive
5.2 - False
Anova is an analysis used when the variables are correlated.