Multiple regression generally explains the relationship between multiple independent or predictor variables and one dependent or criterion variable. A dependent variable is modeled as a function of several independent variables with corresponding coefficients, along with the constant term. Multiple regression requires two or more predictor variables, and this is why it is called multiple regression.
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The multiple regression equation explained above takes the following form:
y = b1x1 + b2x2 + … + bnxn + c.
Here, bi’s (i=1,2…n) are the regression coefficients, which represent the value at which the criterion variable changes when the predictor variable changes.
As an example, let’s say that the test score of a student in an exam will be dependent on various factors like his focus while attending the class, his intake of food before the exam and the amount of sleep he gets before the exam. Using this test one can estimate the appropriate relationship among these factors.
Multiple regression in SPSS is done by selecting “analyze” from the menu. Then, from analyze, select “regression,” and from regression select “linear.”
There are certain terminologies that help in understanding multiple regression. These terminologies are as follows:
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