Computing Expected Values for a contingency Table

A paired t-test is a statistical method used to determine whether there is a significant difference between two sets of related observations. It’s especially useful when the same subjects are measured under two different conditions

Tutorial on the Chi Square Test

Friedman Rank Sum Test Implementation of the Friedman Test using the Warpbreaks data set

This exercise demonstrates how the Poisson distribution can approximate a binomial distribution when trials are large and success probabilities are small. It includes step-by-step examples, comparisons, and practical use cases for real-world data

Tutorial on the Uniform Probability Distribution

Tutorial on the Binomial Text with R

This simulation explores the classic Gambler’s Ruin problem using R. It models a series of biased coin tosses between a gambler and a banker, tracks the gambler’s fortune over time, and analyzes the duration and outcomes of multiple trials.

Tutorial on Benford's Law

Tutorial on the Hypergeometric Probability Distribution

Tutorial Sheet for the Geometric Probability Distribution

Tutorial on the exponential probability distribution

Tutorial on the Weibull Probability Distribution - The Weibull distribution is widely used for life data analysis. Among its variations, the two-parameter Weibull distribution is the most common, though the three-parameter and one-parameter versions are also utilized for more detailed analysis.

Short tutorial on the Lognormal Probability Distribution

Introduction to Statistical Process Control

In the built-in data set named **`immer`**, the barley yield in years 1931 and 1932 for the same fields is recorded. Fertilizer treatments were applied in the interim. The study aimed to determine whether the treatment was effective.

Bartlett's test for Homogeneity of Variances

The Anderson-Darling test evaluates whether a sample follows a specific distribution, typically normal. It gives more weight to tail behaviour than other tests, making it sensitive to subtle deviations.

This tutorial explains how log transformation can correct non-normal data, helping restore normality for valid statistical analysis—especially useful with skewed data or small sample sizes.

The Kolmogorov-Smirnov (K-S) test is a non-parametric method used to compare a sample's distribution to a fully specified continuous theoretical distribution.