In discrete response models, overdispersion occurs when there is more correlation in the data than is allowed by the assumptions that the model makes.
In a classification model, the confusion matrix shows the counts of correct and erroneous classifications. In a binary classification problem, the matrix consists of 4 cells.
In a classic statistical experiment, treatment(s) and placebo are applied to randomly assigned subjects, and, at the end of the experiment, outcomes are compared.
Classification and regression trees, applied to data with known values for an outcome variable, derive models with rules like "If taxable income <$80,000, if no Schedule C income, if standard deduction taken, then no-audit."
The predictors in a predictive model are sometimes given different terms by different disciplines. Traditional statisticians think in terms of variables.
In logistic regression, we seek to estimate the relationship between predictor variables Xi and a binary response variable. Specifically, we want to estimate the probability p that the response variable will be a 0 or a 1.
Bayesian statistics typically incorporates new information (e.g. from a diagnostic test, or a recently drawn sample) to answer a question of the form "What is the probability that..."
Consider two (or more) samples subjected to different treatments. A permutation test assesses whether,
One avid reader took issue with a recent definition of "quasi experiment." I had defined it