Select the most appropriate statistical (hypothesis) test based on the number of variables and their types with the help of a flowchart| Stats and R
Learn the structure of a hypothesis test by hand, illustrated by 4 easy steps using the critical value, p-value and confidence interval methods| Stats and R
Learn how to detect outliers in R thanks to descriptive statistics and via the Hampel filter, the Grubbs, the Dixon and the Rosner tests for outliers| Stats and R
Learn how to do the Wilcoxon test (non-parametric version of the Student's t-test) in R, used to compare 2 groups when the normality assumption is violated| Stats and R
Learn how to apply the Student's t-test by hand and in R in order to compare two independent or paired samples with known or unknown variances| Stats and R
Learn how to perform a descriptive analysis of your data by hand. You will learn how to compute both location and dispersion measures to describe your data| Stats and R
Learn the differences between a quantitative continuous, quantitative discrete, qualitative ordinal and qualitative nominal variable via concrete examples| Stats and R