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MULTIPLE REGRESSION
The least square regression equation discussed above was based on the assumption that total cost was determined by only one activity based variable. However, other variables are likely to affect labour costs such as labour hours, material costs, machine hours, etc. These may have an effect on labour costs.
The equation for the simple regression can be expanded to include more than one independent variable as shown below:
Y = a + b1X1 + b2 X2 + b3 X3 +………+bn Xn
For two independent variables, the function will be of the form:
Y = a + b1X1 + b2 X2
The normal equations can be given by:∑y = na + b1 ∑X1 + b2∑x2∑X1Y = a∑x1 + b1∑X12 + b2 ∑X1 ∑X2∑X2 Y = a∑x2 + b1∑ X 1∑X2 +b2 ∑X2 Normally computers are used for the solution of multiple regression.
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costs/per unit labor ... $ 4 materials ...5 fixed cost... $ 12 determine the break even point in units if the seeling price is $ 19 determine the break even point in sales at
Constraints 1) A constraint of the type ≤ (≥) can be converted to an equation by adding a slack variable to (subtracting a surplus variable form) the left side of the constrain
The F–test The significance of the regression results can be tested by using the F- statistics. The F-statistics is a ratio which compares the explained sum of squares and t
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