An important application of regression analysis in accounting is in the estimati
ID: 3234825 • Letter: A
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. Excel File: data14-21.xls a. Compute b_1 and b_0 (to 2 decimals if necessary). b_1 b_0 Complete the estimated regression equation (to 2 decimals if necessary). y = + x 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)? $Explanation / Answer
Result:
a).
b1=7.60
b0==1246.67
y=1246.67+7.60x
b).
7.6
c).0.9587
95.87%
d).5046.67
Regression Analysis
r²
0.9587
n
6
r
0.9791
k
1
Std. Error
241.5229
Dep. Var.
y
ANOVA table
Source
SS
df
MS
F
p-value
Regression
5,415,000.0000
1
5,415,000.0000
92.83
.0006
Residual
233,333.3333
4
58,333.3333
Total
5,648,333.3333
5
Regression output
confidence interval
variables
coefficients
std. error
t (df=4)
p-value
95% lower
95% upper
Intercept
1,246.6667
464.1599
2.686
.0549
-42.0479
2,535.3812
x
7.6000
0.7888
9.635
.0006
5.4099
9.7901
Predicted values for: y
95% Confidence Interval
95% Prediction Interval
x
Predicted
lower
upper
lower
upper
Leverage
500
5,046.667
4,727.409
5,365.924
4,303.971
5,789.362
0.227
Regression Analysis
r²
0.9587
n
6
r
0.9791
k
1
Std. Error
241.5229
Dep. Var.
y
ANOVA table
Source
SS
df
MS
F
p-value
Regression
5,415,000.0000
1
5,415,000.0000
92.83
.0006
Residual
233,333.3333
4
58,333.3333
Total
5,648,333.3333
5
Regression output
confidence interval
variables
coefficients
std. error
t (df=4)
p-value
95% lower
95% upper
Intercept
1,246.6667
464.1599
2.686
.0549
-42.0479
2,535.3812
x
7.6000
0.7888
9.635
.0006
5.4099
9.7901
Predicted values for: y
95% Confidence Interval
95% Prediction Interval
x
Predicted
lower
upper
lower
upper
Leverage
500
5,046.667
4,727.409
5,365.924
4,303.971
5,789.362
0.227
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