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

Exercise 35

Interpretation of Regression Results: Simple Regression

A local restaurant, Fred’s Fish Fry, is estimating nonfood kitchen costs (labor, supervision, utilities, etc.) based on food cost. Data were gathered for the past 24 months and analyzed using a spreadsheet program. The following output was generated:

Equation
Intercept	$14,000
Coefficient on food cost	225%
Statistical data
Correlation coefficient	0.483
R2	0.233

The company is planning to operate at a level of $15,000 of food costs per month for the coming year. Required

a. Use the regression output to write the nonfood cost equation.

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b. Based on the cost equation, compute the estimated nonfood kitchen costs (labor, supervision, utilities, etc.) per month for the coming year.

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c. Fred has asked you for advice on whether he should rely on the estimate. What will you say?

Step 1 of 4

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.

Correlation coefficient

R is generally referred as correlation coefficient which is used to measure the immediacy of data point to regression line. Higher correlation coefficient i.e. close to 1.0 is always better and it shows data points are close to regression line.