Testing correlation and regression between variables
I would like to finish this chapter by taking a look at how we can identify the relationship between two quantitative variables (regression), and the strength of said relationship (correlation).
When interested in the relationship between two quantitative variables, two factors need to be carefully considered. The question “is there a link between variables X and Y?” has to be approach simultaneously from two directions. First, the study of the correlation aims at identifying the strength of a relationship (if it exists). Then, the regression analysis will look to characterize the slope of the line that sums up the relationship between X and Y.
In R, typically, the correlation is tested with the function “cor.test()”, while the regression is performed with the function “lm()”, or “glm()” for more complex approaches.
INTRODUCTION
No, don't run away! It will be fine. Stats are cool.
ANOVA
Comparing the mean of more than two samples
CHI SQUARE TEST
*cue "Ride of the Valkyries"
STUDENT’S T-TESTS
Comparing the mean of two samples
KRUSKAL-WALLIS RANK SUM TEST
Comparing more than two samples with a non-parametric test
FISHER’S EXACT TEST
Comparing several observed distribution
WILCOXON TESTS
Comparing two samples with a non-parametric test
BINOMIAL TEST
Comparing observed percentages to theoretical probabilities
CONCLUSION
After this dreadful interlude, let's make some art!
