STATISTICS WITH R
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Wilcoxon tests with wilcox.test()
Hypothesis testing
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Kolmogorov-Smirnov test with ks.test()
Hypothesis testing
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Coefficient of variation
Dispersion measures
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Range of values
Dispersion measures
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Pearson's Chi-squared test with chisq.test()
Hypothesis testing
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Random samples and permutations
Simulation
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Interquartile range
Dispersion measures
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Median absolute deviation
Dispersion measures
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Lilliefors normality test
Hypothesis testing
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Kruskal Wallis rank sum test (H test)
Hypothesis testing
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Covariance and correlation
Association measures
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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.