References and further reading

1. A non-parametric test is a statistical test that does not make any assumptions about the underlying distribution of the data. Non-parametric tests are useful when data does not follow a specific distribution, such as a normal distribution, or when the distribution is unknown. These tests rely on the ranks or order of the data rather than the actual values, making them more robust to outliers and less sensitive to the shape of the data distribution. Examples of non-parametric tests include the Wilcoxon rank-sum test, Kruskal-Wallis test, and the KS test.
2. The coefficient of determination, often denoted as R², is a statistical measure that represents the proportion of the variance in the dependent variable that can be explained by the independent variable(s) in a regression model. In other words, R² indicates how well the regression model fits the observed data. It ranges from 0 to 1, with a higher value suggesting a better fit. If R&...