Random variables have a variety of applications in statistical simulations. One application of this procedure characterizes subjects in a population with a probability distribution. For example, you might use a normal distribution to characterize the population of individuals' heights, or you might use a Bernoulli distribution of two possible outcomes to characterize the population of coin-flip results. Rank and Percentile Produces a table that contains the ordinal and percentage rank of each value in a data set. You can use this procedure to analyze the relative standing of values in a data set.
Regression Performs linear regression analysis.
Regression is used in a wide variety of applications that seek to analyze how a single dependent variable is affected by the values of one or more independent variables. For example, several factors affect an athlete's performance, including age, sex, height, and weight. Regression apportions shares in the performance measure to each of these four factors based on a set of performance data. You can then use the regression results to predict the performance of a new, untested athlete. This form of the t-test tests whether a sample's means are distinct.