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Flexible Budget, Multiple Regression The controller for Muir Company's Salem plant is analyzing overhead in order to determine appropriate drivers for use in flexible budgeting.

Flexible Budget, Multiple Regression

The controller for Muir Company's Salem plant is analyzing overhead in order to determine appropriate drivers for use in flexible budgeting. She decided to concentrate on the past 12 months since that time period was one in which there was little important change in technology, product lines, and so on. Data on overhead costs, number of machine hours, number of setups, and number of purchase orders are in the following table.

Month Overhead Costs Machine Hours Number of Setups Number of Purchase Orders
January $ 32,296 1,000 20 216
February 31,550 930 18 250
March 36,280 1,100 21 300
April 36,867 1,050 23 270
May 36,790 1,170 22 285
June 37,800 1,200 25 240
July 40,024 1,235 27 237
August 39,256 1,190 24 303
September 33,800 1,070 20 255
October 33,779 1,210 22 195
November 37,225 1,207 23 270
December 27,500 1,084 15 150
Totals $423,167 13,446 260 2,971

Required:

1. Run a multiple regression equation using machine hours, number of setups, and number of purchase orders as independent variables. Prepare a flexible budget for overhead for the 12 months using the results of this regression equation. (Round the regression coefficients to the nearest cent and predicted overhead to the nearest dollar.)

If there is no variance, enter "0" for the amount and select "NA" in the last column. Enter all your answers as positive amounts.

2. Now, suppose that the controller remembers that the factory throws two big parties each year, one for the 4th of July and the other for Christmas. Rerun the multiple regression with machine hours, number of setups, and number of purchase orders, and add a dummy variable called Party. (This variable takes the value one for months with a factory-sponsored party, and zero otherwise.) Prepare a flexible budget for the 12 months using the results of this regression. (Round the regression coefficients to the nearest cent and each predicted overhead amount to the nearest dollar.)

If there is no variance, enter "0" for the amount and select "NA" in the last column. Enter all your answers as positive amounts.

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