R CODER

STATISTICS WITH R

Descriptive Statistics Distributions Hypothesis testing Simulation
F test in R with var.test()

F test with var.test() to compare two variances

Hypothesis testing

T-test in R

T-test to compare means

Hypothesis testing

Shapiro Wilk test in R

Shapiro Wilk normality test

Hypothesis testing

Quantiles and percentiles in R

Quantiles

Location measures

Box Cox transformation in R

Box Cox transformation

Standard deviation and variance in R with the sd and var functions

Variance and standard deviation

Dispersion measures

Calculate the mean in R

Calculate the mean

Measures of central tendency

Calculate the median in R with the median function for discrete and continuous data

Calculate the median

Measures of central tendency

Calculate the mode in R for both discrete and continuous data

Mode estimation

Measures of central tendency

Binomial distribution in R with the dbinom, pbinom, qbinom and rbinom functions

Binomial distribution

Distributions

Poisson distribution in R with the dpois, ppois, qpois and rpois functions

Poisson distribution

Distributions

Use the dunif, punif, qunif and runif functions of the uniform distribution in R

Continuous uniform distribution

Distributions

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.