R CODER

STATISTICS WITH R

Descriptive Statistics Distributions Hypothesis testing Simulation
One-way and two-way ANOVA in R with aov

ANOVA : one-way and two-way analysis of variance

Hypothesis testing

Chi-square distribution in R with dchisq, pchisq, qchisq and rchisq

Chi-square distribution

Distributions

Correlation test in R with cor.test

Correlation test with cor.test()

Hypothesis testing

F distribution in R with df, pf, qf and rf

F distribution

Distributions

Fisher's exact test in R with fisher.test

Fisher's exact test with fisher.test()

Hypothesis testing

Levene and Bartlett tests for equal variances in R

Levene's and Bartlett's tests for homogeneity of variances

Hypothesis testing

Linear regression in R with lm

Linear regression with lm()

Hypothesis testing

Logistic regression in R with glm

Logistic regression with glm()

Hypothesis testing

Student t distribution in R with dt, pt, qt and rt

Student's t distribution

Distributions

Wilcoxon tests in R with wilcox.test()

Wilcoxon tests with wilcox.test()

Hypothesis testing

Kolmogorov-Smirnov test in R with ks.test()

Kolmogorov-Smirnov test with ks.test()

Hypothesis testing

Coefficient of variation in R

Coefficient of variation

Dispersion measures

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.