Microsoft Office Tutorials and References
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Multiple Correlation and Multiple
There are many times in science when you want to predict a criterion, Y, but you
want to ﬁnd out if you can develop a better prediction model by using several
predictors in combination e : g : X 1 ; X 2 ; X 3 ; etc ð Þ instead of a single predictor, X.
The resulting statistical procedure is called ‘‘multiple correlation’’ because it
uses two or more predictors in combination to predict Y, instead of a single
predictor, X. Each predictor is ‘‘weighted’’ differently based on its separate
correlation with Y and its correlation with the other predictors. The job of multiple
correlation is to produce a regression equation that will weight each predictor
differently and in such a way that the combination of predictors does a better job of
predicting Y than any single predictor by itself. We will call the multiple
correlation: R xy .
when only one predictor, X, is used is:
Y ¼ a þ bX
ð 7 : 1 Þ
7.1 Multiple Regression Equation
The multiple regression equation follows a similar format and is:
Y ¼ a þ b 1 X 1 þ b 2 X 2 þ b 3 X 3
þ etc : depending on the number of predictors used
ð 7 : 2 Þ
The ‘‘weight’’ given to each predictor in the equation is represented by the letter
‘‘b’’ with a subscript to correspond to the same subscript on the predictors.
Important note: In order to do multiple regression, you need to have installed
did not install this, you need to do so now.
T. J. Quirk et al., Excel 2010 for Biological and Life Sciences Statistics,
Springer Science+Business Media New York 2013
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