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The remaining subsystems are all implemented in the extensions rather than in the framework. These subsystems will always be implemented outside the framework. Either these subsystems are implemented in the SKIT-provided extension or they are implemented in a new extension that we create for a new project. To a certain extent, we are free to make design/implementation decisions that differ from the SKIT implementation, as long as the implementation still ful lls the requirements set forth by the framework-de ned interfaces. Actually, we always have the option of modifying the framework-de ned interfaces too, but that decision should not be made too hastily. Usually we will not need (or want) to modify the framework. Even when we do need to modify the framework, we will usually be making a change to the existing implementation or creating a new framework-de ned interface. Hopefully, we will rarely need to modify one of the existing framework-de ned interfaces. The SKIT-provided extension contains a reasonable implementation for each subsystem, but these implementations are not necessarily the best possible implementation with respect to features, performance, or exibility. The goal of the SKIT extension is merely to provide a suf ciently complex example implementation that veri es the operation of the framework as a whole, demonstrates the feasibility of a typical DDBE project implementation, and provides a quick start for new DDBE project development.

In sum, the partial regression coef cient, squared semipartial correlation, and the squared partial correlation all assess the unique contribution of a predictor in a regression equation containing other predictors They all re ect the conditional relationship of a predictor to the criterion; the relationship is conditional in that it depends speci cally on what other predictor(s) are partialed or held constant In fact, all three lead to precisely the same value of the test statistic for signi cance of the predictor s contribution Sets of Predictors The notion of unique contribution of a single predictor above and beyond other predictor(s) can be extended to the unique contribution of a set of predictors beyond other predictor(s) for example, from a second set of predictors X4 and X5, above and beyond a baseline set consisting of X1, X2, and X3.

Illustrated TCP/IP by Matthew G. Naugle Wiley Computer Publishing, John Wiley & Sons, Inc. ISBN: 0471196568 Pub Date: 11/01/98

A measure of gain in prediction by the addition of the second set of 2 predictors to the baseline set is computed First RY123 is com2 puted for the baseline set of predictor variables Then RY12345 is computed for the baseline plus second set of predictors The gain in prediction is the difference between the R2 based on the combined sets and the R2 based on the baseline set of 2 2 predictor variables, here RY12345 RY123 This difference is 2 the squared multiple semipartial correlation RY (45123) of X4 and X5 with Y with X1, X2, and X3 partialed out The squared partial correlation can also be extended to sets of predictors However, partial regression coef cients only pertain to the unique contribution of individual variables.

(g) q.4(.-\) = 0

WHAT IS MULTIPLE REGRESSION ANALYSIS 483 Three Uses of Multiple Regression Analysis: Description, Prediction, and Theory Testing 484 Two Foci of This 484 THE STRUCTURE OF MULTIPLE REGRESSION ANALYSIS 484 The Multiple Regression Equation 484 The Least Squares Criterion for Selection of Regression Coef cients 484 Nonlinear Relationships and OLS Regression 485 Measures of Contribution to Prediction Accuracy 485 NUMERICAL EXAMPLE: PREDICTING INTENTION TO OBTAIN A MAMMOGRAM 486 NUMERICAL EXAMPLE: MULTIPLE REGRESSION ANALYSES 487 Overview 487 One-Predictor Regression: Continuous Predictor Variable Hypothesis 1 487 Single Categorical Predictor Variable: Regression with Code Predictors Hypotheses 2 and 3 491 Two Continuous Predictors: High Interpredictor Correlation Hypothesis 4 493

NUMERICAL EXAMPLE: PREDICTING INTENTION TO OBTAIN A MAMMOGRAM To provide a concrete basis for illustrating the use of multiple regression in the behavioral sciences, we introduce an arti cial data set from the health psychology area Health psychologists have conducted considerable empirical research and have developed theory to explain the extent to which individuals take actions to protect their health (see Aiken, Gerend, & Jackson, 2001) Our example focuses on women s intentions to obtain a screening mammogram, a test that detects breast cancer in its very early, treatable stages (Aiken, West, Woodward, & Reno, 1994) The criterion Y is a scale score on a 6-point multi-item scale of intention to obtain a mammogram, measured on a group of N = 240 women who are not in compliance with screening guidelines of the National Cancer Institute at the time of data collection.