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.

Production Volume (units)	Total Cost ($)
400	3,900
450	4,900
550	5,300
600	5,800
700	6,300
750	6,900

1. Compute b₁ and b₀ (to 1 decimal).
   b₁
   b₀
   
   Complete the estimated regression equation (to 1 decimal).
   =  +  x
   
2. What is the variable cost per unit produced (to 1 decimal)?
   $
   
3. Compute the coefficient of determination (to 3 decimals). Note: report r² between 0 and 1.
   r² =
   
   What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)?
   %
   
4. The company's production schedule shows 500 units must be produced next month. What is the estimated total cost for this operation (to the nearest whole number)?
   $

Answer 1

I am Using Excel

Output

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.979127101
R Square	0.958689879
Adjusted R Square	0.948362349
Standard Error	241.5229458
Observations	6
ANOVA
	df	SS	MS	F	Significance F
Regression	1	5415000	5415000	92.82857143	0.00064897
Residual	4	233333.3333	58333.33333
Total	5	5648333.333
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	1146.6667	464.1599341	2.470412852	0.068916206	-142.0479105	2435.381244
Production Volume (units)(x)	7.6	0.788810638	9.634758504	0.00064897	5.409910566	9.790089434

====================================

====================================

b)  7.6

===================================

c) r² = 0.959

______________________________

r² = 95.9 %

===================================

d)