STATISTICS WITH R
![Wilcoxon tests in R with wilcox.test()](/images/featured/wilcox-test-r.png)
Wilcoxon tests with wilcox.test()
Hypothesis testing
![Kolmogorov-Smirnov test in R with ks.test()](/images/featured/ks-test-r.png)
Kolmogorov-Smirnov test with ks.test()
Hypothesis testing
![Coefficient of variation in R](/images/featured/coefficient-variation-r.png)
Coefficient of variation
Dispersion measures
![range() function in R](/images/featured/range-r.png)
Range of values
Dispersion measures
![Chi-squared test in R](/images/featured/chisq-test-r.png)
Pearson's Chi-squared test with chisq.test()
Hypothesis testing
![Random samples and permutations in R](/images/featured/sample-r.png)
Random samples and permutations
Simulation
![Interquartile range in R](/images/featured/iqr-r.png)
Interquartile range
Dispersion measures
![Median absolute deviation in R](/images/featured/mad-r.png)
Median absolute deviation
Dispersion measures
![Lilliefors normality test in R](/images/featured/lillie-test-r.png)
Lilliefors normality test
Hypothesis testing
![Kruskal Wallis rank sum test in R](/images/featured/kruskal-test-r.png)
Kruskal Wallis rank sum test (H test)
Hypothesis testing
![Covariance and correlation in R](/images/featured/covariance-correlation-r.png)
Covariance and correlation
Association measures
![Test for proportions in R with prop.test() function](/images/featured/prop-test-r.png)
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:
-
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). -
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 aswilcox.test
orkruskal.test
). -
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.