Multiple regression

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Related to Multiple regression: Multiple linear regression

Multiple regression

The estimated relationship between a dependent variable and more than one explanatory variable.

Multiple Regression

In statistics, an equation showing the value of a dependent variable as a function of two or more independent variables. As with regression analysis, multiple regression analysis is important for determining certain economic phenomena.
References in periodicals archive ?
51 for the sample (297) used to develop the multiple regression model (D'Souza and Maheshwari, 2009) which is utilized as the grade prediction equation in this study.
Results from multiple regressions are presented in Table 5 and they indicate that different variables had significant explanatory powers in each of the four years in our testing periods.
The multiple regression analysis used in this study was successful in offering estimates of heating times that are reasonably close to those calculated by heat conduction equations developed by MacLean for wood heat sterilization in saturated steam.
Multiple regression analysis is an increasingly used technique in the real estate appraisal field.
Table 3 provides pooled multiple regression results of step two.
A multiple regression model is estimated with appraised value as the dependent variable.
Multiple regression analysis revealed that findings of EF <30% and EF 30-39% were strong predictors of increased plasma concentrations of N-terminal proBNP (t = 7.
Factor analysis and multiple regression models were employed to reveal the crime-causation relationships.
We included data concerning road lengths, stream lengths, human population size, forested land, agricultural land, and urban land in a multiple regression model to determine which factors were associated with higher occurrences of deer-vehicle collisions.
Because the majority of the published regression equations contained unstable predictors, the authors developed modified multiple regression equations using the more stable predictors.
To move beyond traditional research methods and into effect analysis, use statistical tools such as analysis of variance, correlation analysis, multiple regression and structural equation modeling (these are described below).

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