STATISTICS WITH R

WHAT ARE THE KEY ADVANTAGES OF USING R FOR STATISTICAL ANALYSIS?

Statistics in R encompass a broad spectrum of functionalities and packages designed to perform various statistical analyses, data exploration, hypothesis testing and modeling tasks. R is extensively used in data analysis, academia, industry, scientific research and statistical computing due to its rich set of statistical tools and packages. Some key statistical functionalities in R include:

1. Descriptive Statistics

R offers functions to compute basic descriptive statistics such as mean, median, standard deviation, variance, range, quartiles, percentiles, and summary statistics for data exploration (`summary` function).
2. Hypothesis testing

R provides functions for conducting various statistical tests, including t-tests (`t.test`), chi-square tests (`chisq.test`), ANOVA (`aov`), F-tests (`var.test`), and non-parametric tests (such as `wilcox.test` or `kruskal.test`).
3. Probability distributions

R includes a wide array of functions to work with probability distributions (e.g., normal, uniform, binomial, Poisson) for generating random numbers, calculating probabilities, quantiles, and density functions.