Non-parametric tests are used when standard assumptions are not available. These tests don’t rely on distributions, often ...

Normality testing is a fundamental component in statistical analysis, central to validating many inferential techniques that presume Gaussian behaviour of error terms ...

Multivariate normality testing plays a critical role in modern statistical analysis by evaluating whether a multivariate dataset conforms to the assumptions of a normal distribution. Such assessments ...

Nonparametric methods form an important core of statistical techniques and are typically used when data do not meet parametric assumptions. Understanding the foundation of these methods, as well as ...

Semiparametric single-index assumptions are widely used dimension reduction approaches that represent a convenient compromise between the parametric and fully nonparametric models for regressions or ...

When applying the Markov model, it is often assumed the transition matrix is stationary and of order one. This article considers the problem of applying the transformed likelihood ratio statistic and ...

This short course will cover the one sample t-test, the two sample t-test, matched-pairs t-test, one-way ANOVA (Analysis of Variance), two-way ANOVA and ANCOVA (Analysis of Covariance). These ...

The books Lies, Damn Lies, and Statistics (Wheeler, 1976) and Damned Lies and Statistics (Best, 2001) have raised questions about whether statistics can be trusted. A number of educated people today, ...

Having data is only half the battle. How do you know your data actually means something? With some simple Python code, you can quickly check if differences in data are actually significant. In ...