An Important application of regression analysis in accounting is in the estimati

Question

An Important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. a. Compute b_1 and b_2 (to 2 decimals if necessary). b_1 b_0 Complete the estimated regression equation (to 2 decimals if necessary). y = + times b. What is the variable cost per unit produced (to 1 decimal)? c. Compute the coefficient of determination (to 4 decimals). r^2 = What percentage of the variation in total cost can be explained by the production volume (to 2 decimals)? % d. The company's production schedule shows 500 units must be produced next month. What is the estimated total cost for this operation (to 2 decimals)? s

Explanation / Answer

Let,

X = Production Volume

Y = Total Cost

x

y

400

4000

450

5000

550

5400

600

5900

700

6400

750

7000

We copy the data in excel and then we go to Data, there we select Data Analysis. In Data Analysis we can find the list of statistical test, there we select Regression. We select y and x values then we run the analysis and we get the following outcome:

Regression Statistics

Multiple R

0.9791

R Square

0.9587

Adjusted R Square

0.9484

Standard Error

241.5229

Observations

6

ANOVA

df

SS

MS

F

Significance F

Regression

1

5415000

5415000

92.82857

0.00064897

Residual

4

233333.3333

58333.33

Total

5

5648333.333

Coefficients

Standard Error

t Stat

P-value

Lower 95%

Upper 95%

Intercept

1246.67

464.1599341

2.685856

0.054894

-42.04791038

2535.381244

X Variable 1

7.60

0.788810638

9.634759

0.000649

5.409910566

9.790089434

Part a)

Answer:

b1 = 7.60

b0 = 1246.67

y^ = 1246.67 + 7.60x

Part b)

Answer: 7.6

Part c)

· r^2 = 0.9587

· 95.87

Part d)

y^ = 1246.67 + (7.60*500)

     = 5046.67

Answer: 5046.67

x

y

400

4000

450

5000

550

5400

600

5900

700

6400

750

7000

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