This research examines an application of the statistical procedures of correlation and regression analysis. The initial part of the examination describes correlation and regression procedures, and illustrates the use of the procedures in an application. Following the description and illustration, the accuracy and appropriateness of the application is discussed.
Description of the Procedure, and An Illustration of the Use of the Procedure in An Application
Correlation and regression procedures are described in this section. This description is followed by an illustration of the use of the procedures in an application.
In least-squares regression analysis, the emphasis is placed on an analysis of the joint interaction of the variations in dependent and independent variables. Because of this analytical emphasis in least squares regression analysis, both the independent and the dependent variables must be measured on an interval scale.
Simple regression analysis involves the analysis of the relationship between one dependent variable and one explanatory, or independent, variable. Multiple regression, on the other hand, is a general statistical technique through which one can analyze the relationship between a dependent or criterion variable and a set of independent or predictor variables. Multiple regression may be viewed either as a descriptive tool by which the linear dependence of one variable on others is summarized and decomposed, or as an inferential tool by which the relationships in the population are evaluated from the examination of sample data.
It is held that before a regression equation can be used for causal analysis, a theoretical basis for a causal relationship between the variables concerned must be established (Casti, 1993, pp. 26-37). It also has been pointed out that the more causal variables that are included in a regression equation to be used in causal analysis, the simpler one's assumptions mu...