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In this section we provide code for The Simple Regression Model 29 SPSS, SAS, and STATA to generate the linear regression equation for the miniature golfing example. com in a file named GOLF. In this data set and analysis, the independent variable X is named plays and the dependent variable Y is named points. The SPSS code that produces output for a linear regression estimating points earned from number of plays is regression/dep=points/method=enter plays. 4. Although each program formats its output differently, the outputs overlap considerably in the information they contain.

Within these conditions, linear models are flexible in many ways: 1. A variable might be a natural property of a participant, such as age or sex, or might be a property manipulated in an experiment, such as which of two or more experimental conditions into which the participant is placed through a random assignment procedure. Manipulated variables are typically categorical but may be numerical, such as the number of hours of practice at a task participants are given or the number of acts of violence on television a person is exposed to during an experiment.

An alternative formula uses the original values of X and Y rather than deviation scores: Cov(XY) = N N i=1 (Xi Yi ) N i=1 Xi )( −( N i=1 Yi ) N2 The covariance of any variable with itself is the variable’s variance. The variance of X we denote by Var(X). 3) The Simple Regression Model 25 Like the covariance, the variance is not usually interpreted. But the variance is the square of an inherently interpretable statistic called the standard deviation. It is the square root of the variance: sX = Var(X) The standard deviation is a widely used measure of a distribution’s variability or spread.

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