Multiple Regression Analysis in Business
As noted in archive 1 (earlier), multiple regression analysis is a dependence technique. Thus, to use it we must be able to divide the variables into dependent and independent variables. Multiple regression analysis is a statistical technique that can be used to analyze the relationship between a single dependent (criterion) variable and several independent (predictor) variables. The objective of multiple regression analysis is to use the independent (predictor) variables whose values are known to predict the single dependent value selected by the researcher.
Regression analysis is also a statistical tool that should be used only when both the dependent and independent variables are metric. However, under certain circumstances it is possible to include nonmetric data either as independent variables (by transforming either ordinal or nominal data with dummy variable coding) or dependent variable (by the use of a binary measure in the specialized technique of logistic regression). In summary, to apply multiple regression analysis: (1) the data must be metric or appropriately transformed, and (2) before deriving the regression equation, the researcher must decide which variable is to be dependent and which remaining variables will be independent.
In selecting suitable applications of multiple regression, the researcher must consider three primary issues:
1) The appropriateness of the research problem (the ever-widening applications of multiple regression fall in two broad classes of research problems: prediction and explanation)
2) Specification of a statistical relationship (multiple regression is appropriate when the researcher is interested in a statistical, not a functional, relationship)
3) Selection of the dependent and independent variables
Multiple regression model can be modeled with the equation
Y = b0 + b1 X1 + b2 X2 + … + bn Xn
Where
b0 = intercept
b1 X1 = linear effect of X1
b2 X2 = linear effect of X2
bn Xn = linear effect of Xn
As noted in archive 1 (earlier), multiple regression analysis is a dependence technique. Thus, to use it we must be able to divide the variables into dependent and independent variables. Multiple regression analysis is a statistical technique that can be used to analyze the relationship between a single dependent (criterion) variable and several independent (predictor) variables. The objective of multiple regression analysis is to use the independent (predictor) variables whose values are known to predict the single dependent value selected by the researcher.
Regression analysis is also a statistical tool that should be used only when both the dependent and independent variables are metric. However, under certain circumstances it is possible to include nonmetric data either as independent variables (by transforming either ordinal or nominal data with dummy variable coding) or dependent variable (by the use of a binary measure in the specialized technique of logistic regression). In summary, to apply multiple regression analysis: (1) the data must be metric or appropriately transformed, and (2) before deriving the regression equation, the researcher must decide which variable is to be dependent and which remaining variables will be independent.
In selecting suitable applications of multiple regression, the researcher must consider three primary issues:
1) The appropriateness of the research problem (the ever-widening applications of multiple regression fall in two broad classes of research problems: prediction and explanation)
2) Specification of a statistical relationship (multiple regression is appropriate when the researcher is interested in a statistical, not a functional, relationship)
3) Selection of the dependent and independent variables
Multiple regression model can be modeled with the equation
Y = b0 + b1 X1 + b2 X2 + … + bn Xn
Where
b0 = intercept
b1 X1 = linear effect of X1
b2 X2 = linear effect of X2
bn Xn = linear effect of Xn
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