Fundamentals of Cost Accounting 3rd Edition by William N. Lanen, Shannon W. Anderson, Michael Maher

Exercise 47

Interpretation of Regression Results

Lerner, Inc., is accumulating data to prepare its annual profit plan for the coming year. The behavior pattern of the maintenance costs must be determined. The accounting staff has suggested using regression to derive an equation in the form of y = a + bx for maintenance costs. Monthly data regarding maintenance hours and costs for the preceding year have been entered into the regression analysis.

Total hours of maintenance for the year	4,800
Total costs for the year	$43,200
Regression results:
Intercept	$ 684.65
b coefficient	$7.29
R2	0.79724

Required

a. In a regression equation expressed as y = a + bx, how is the letter b best described?

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b. How is the letter y in the regression equation best described?

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c. How is the letter x in the regression equation best described?

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d. Based on the data derived from the regression analysis, what are the estimated costs for 360 maintenance hours in a month?

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e. What is the percent of the total variance that can be explained by the regression equation?

Step 1 of 6

Cost estimation

Cost estimation is an important exercise because it helps managers in decision making. Correct cost estimates result in cost saving and making business successful. Cost estimates helps managers to evaluate and choose the best alternative. It is important for managers to capture the correct cost for each alternative.

There different methods used for cost estimation like engineering, accounting and statistical analysis.

Statistical method

Statistical is more accurate method of cost estimation as compared to engineering and accounting analysis method as they have certain limitations. Under this method, random events are separated from while analyzing relationship between cost and activity. While using statistical method for cost estimation it is important to confirm that past activity levels are related to current estimation.

Simple regression analysis – In simple regression analysis there is only one predictor for the activity and cost is estimated on the basis on one predictor only. For example, overhead cost is estimated on the basis of relationship between total cost and parts used.

Multiple regression analysis – In multiple regression analysis more than one type of predictor is used for the activity and cost is estimated on the basis on multiple predictors. Multiple predictors are used for better and more accurate cost estimation. For example, overhead cost is estimated on the basis of parts used and labor hours used for production purpose. Only parts cost may not provide correct overhead cost estimation, hence labor hours used, must also be included as predictor to calculate overhead cost.