Statistics is computationally intensive. Routine statistical tasks such as data extraction, graphical summary, and technical interpretation all require pervasive use of modern computing machinery. Obviously, these tasks can benefit greatly from a parallel computing environment where extensive calculations can be performed simultaneously.

In hypothesis testing, a type II error is due to a failure of rejecting an invalid null hypothesis. The probability of avoiding a type II error is called the power of the hypothesis test, and is denoted by the quantity 1 - β .

A multiple linear regression (MLR) model that describes a dependent variable y by independent variables x₁, x₂, ..., x_p (p > 1) is expressed by the equation as follows, where the numbers α and β_k (k = 1, 2, ..., p) are the parameters, and ϵ is the error term.

In an experiment study, various treatments are applied to test subjects and the response data is gathered for analysis. A critical tool for carrying out the analysis is the Analysis of Variance (ANOVA). It enables a researcher to differentiate treatment results based on easily computed statistical quantities from the treatment outcome.