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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.
Multi-collinearity Multiple regression analysis is based on the assumption that the independent variables are not correlated with each other, whenever the independent variables
Service time-probability distribution curve A common example is that service times follow an exponential probability distribution i.e. y=e -x Service channels - t
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A company manufactures a one product. Estimated cost data regarding this product and other information for the product and the company are as follows: Sales price per unit Rs.2000
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I need an example on how to solve a single and two product bundle
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