Learn how to create professional graphics and plots in R (histogram, barplot, boxplot, scatter plot, line plot, density plot, etc.) with the ggplot2 package| 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 perform the one-sample t-test by hand and in R in order to compare a sample to a hypothesized value, with known or unknown population variance| 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
This article explains in details what is the normal or Gaussian distribution, its importance in statistics and how to test if your data is normally distributed| Stats and R
Learn how to perform a descriptive analysis of your data in R, from simple descriptive statistics to more advanced graphics used to describe your data at hand| Stats and R
This article explains how to distinguish a population from a sample, an important difference in statistics, namely for descriptive and inferential statistics| Stats and R
Learn the differences between a quantitative continuous, quantitative discrete, qualitative ordinal and qualitative nominal variable via concrete examples| Stats and R