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.
Copyright © 2012, Campbell R. Harvey. All Rights Reserved.

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.
Farlex Financial Dictionary. © 2012 Farlex, Inc. All Rights Reserved
References in periodicals archive ?
Interaction effects in multiple regression. Newbury Park, CA: Sage.
A Multiple Regression Model Y = a + |b.sub.1~|x.sub.1~ - |b.sub.2~|x.sub.2~ + |b.sub.3~|x.sub.3~ - |b.sub.4~|x.sub.4~ where (for example): Y = estimated market share |x.sub.1~ = proportion of socioeconomic groups A and B |x.sub.2~ = a measure of competition |x.sub.3~ = number of people living in the area |x.sub.4~ = distance of the area from the site Note: The alpha (|alpha~) and beta (|beta~) coefficients are developed in the model-building process.
With the purpose of obtaining the best functional model, statistical performances of Stepwise Regression Analysis, use of factor analysis scores with multiple regression model analysis, and Ridge Regression analysis were compared each other.
They noted that multiple regression had weaknesses.
In literature, there have been several studies reported for various sheep breeds on the estimation of live weight from several morphological traits by means of factor scores in multiple regression analysis but the present paper was the initial record on body weight prediction of Savak Akkaraman Lambs.
The multiple regression equation for weight traits (Y) was calculated as follows: Y = a + [b.sub.1][X.sub.1] + [b.sub.2][X.sub.2] + [b.sub.3][X.sub.3] +......+ [b.sub.i][X.sub.i], where Y is the dependent variable, a is the intercept, [b.sub.i] is the partial regression coefficient, and [X.sub.i] is the independent variable.
In my valuation practice, I typically apply multiple regression analysis as a stand-alone method to develop an opinion of a defined standard of value.
Multiple Regression Analysis Was Effective to Enhance the Correlation Coefficients Determined above.
First is a multiple regression analysis as a linear regression analysis, and the second is a logistic-regression analysis as a nonlinear regression analysis.
Running a multiple regression in Excel using the Regression analysis tool follows the same procedure as required for a simple, one-variable regression.
Key words: Forward selection, backward elimination, univariate regression; multiple regression
infant mortality rate by using Multiple Regression model and Conditional Autoregressive Model to quantify spatial risk of infant death.

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