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 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 compute a correlation coefficient (Pearson and Spearman) and perform a correlation test in R| Stats and R
Make the the most correlated variables stand out via a correlogram. See also how to enhance a correlation plot to show significant correlations among variables| Stats and R
This article is a practical guide about R Markdown, from why it is an important writing tool in R to how to compile and edit your first R Markdown document| 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
Test if two categorical variables are dependent via the Chi-square test of independence. See also how to compute it by hand and how to interpret the results| Stats and R
Learn when and how to use the Chi-square test of independence in R. See also how it works in practice and how to interpret the results of the Chi-square test| Stats and R
Discover the best RStudio addins, how to use them in practice and how they can help you when writing code in R or R Markdown| 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
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
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 about the five most common data types in R, numeric, integer, character, factor and logical. See also how to recognize the different data types in R| Stats and R
Learn the differences between a quantitative continuous, quantitative discrete, qualitative ordinal and qualitative nominal variable via concrete examples| Stats and R