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Binary logistic regression uses the sigmoid function to estimate the probability of the target variable when it is binary. However, this function does not allow direct probability estimates when dealing with nominal variables with more than two categories. In these cases, we will use multinomial logistic regression, which will use the softmax function to estimate the probabilities with respect to a reference category.

The tau-squared represents the variability of effects between the different populations from which the primary studies of a systematic review are derived, according to the assumption of the random effects model of meta-analysis. Its usefulness for weighting studies and for calculating prediction intervals is described, understanding how its significance goes beyond being a mere indicator of heterogeneity.

Cramer's V allows the strength of the association between two categorical (nominal) variables, not ordinal, to be quantified. It is especially useful when the variables have multiple categories, since it allows the strength of the association to be condensed into a single figure. Its values range from 0, no association, to 1, a perfect association.

Contrary to what it could be supposed, the inclusion of a large number of variables in a linear regression model can be counterproductive to its performance, producing overfitting of the data and decreasing the capacity for generalization. This is known as the curse of multidimensionality.