R CODER

STATISTICS WITH R

Descriptive Statistics Distributions Hypothesis testing Simulation
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

range() function in R

Range of values

Dispersion measures

Chi-squared test in R

Pearson's Chi-squared test with chisq.test()

Hypothesis testing

Random samples and permutations in R

Random samples and permutations

Simulation

Interquartile range in R

Interquartile range

Dispersion measures

Median absolute deviation in R

Median absolute deviation

Dispersion measures

Lilliefors normality test in R

Lilliefors normality test

Hypothesis testing

Kruskal Wallis rank sum test in R

Kruskal Wallis rank sum test (H test)

Hypothesis testing

Covariance and correlation in R

Covariance and correlation

Association measures

Test for proportions in R with prop.test() function

Test for proportions with prop.test()

Hypothesis testing

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.