Section 12 Operation Management Statistics

Section 12 Operation Management Statistics - Dr. Harvey A....

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© 2008 by Harvey A. Singer 1 OM 210 Statistical Analysis for  Management Multiple Linear Regression Key Concepts Dr. Harvey A. Singer School of Management George Mason University
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© 2008 by Harvey A. Singer 2 Input Data Sample of n data “points” listed as the ordered pairs ( x 1 , x 2 , …, x k , y ) for the k independent “predictor” variables x k and the single dependent “response” variable y .
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© 2008 by Harvey A. Singer 3 Predictor Variables “Predictors” predict. Any prediction of the value of the response variable should be based on the values of the predictor variables. The future value of the response variable can be forecast from the values of the predictor variables. The predictors are related to the response variable. Choose the predictor variables wisely with good business or economic sense. Look for the big drivers of or the big contributors to the response variable.
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© 2008 by Harvey A. Singer 4 Predictor Variables Predictors are independent variables. Chosen predictors should be independent of each other, or very nearly so. Chosen predictors should not be well correlated with each other. If dependent and correlated, then have the problem of multicollinearity ” or “ collinearity .”
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© 2008 by Harvey A. Singer 5 The General Multiple Regression Model The Estimated Multiple Regression Equation. Let Y = E ( y | all x i ) so that Y = b 0 + b 1 x 1 + b 2 x 2 + … + b k x k The regression coefficients b 0 , b 1 , b 2 , … , b k are constants to be calculated from all the available sample data.
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© 2008 by Harvey A. Singer 6 Regression Coefficients The formulas for the regression coefficients b 0 , b 1 , b 2 , … , b k involve the use of matrix algebra. Computation of coefficients values is very
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This note was uploaded on 01/26/2011 for the course OM 210 taught by Professor Singer during the Fall '08 term at George Mason.

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Section 12 Operation Management Statistics - Dr. Harvey A....

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