Question

Kerri Tran​, owner of Flower Hour​, operates a local chain of floral shops. Each shop has its own delivery van. Instead of charging a flat delivery​ fee, Tran wants to set the delivery fee based on the distance driven to deliver the flowers. Tran wants to separate the fixed and variable portions of her van operating costs so that she has a better idea how delivery distance affects these costs. Flower Hour does a regression analysis on the next​ year's data using Excel. The output generated by Excel is as​ follows: LOADING...​(Click the icon to view the regression​ analysis.) Requirements 1. Determine the​ firm's cost equation​ (use the output from the Excel​ regression). 2. Determine the​ R-square (use the output from the Excel​ regression). What does Flower Hour​'s ​R-square indicate? 3. Predict van operating costs at a volume of 16 comma 500 miles assuming the company would use the cost equation from the Excel regression regardless of its​ R-square. Should the company rely on this cost​ estimate? Why or why​ not? Requirement 1. Determine the​ firm's cost equation​ (use the output from the Excel​ regression).​ (Enter amounts to two decimal​ places.) y = $ x + $

SUMMARY OUTPUT
Regression Statistics
Multiple R		0.87
R Square		0.75
Adjusted R Square		0.71
Standard Error		153.46
Observations		7

ANOVA
	df	SS	MS	F	Significance F
Regression	1	362,391.24	362,391.24	15.39	0.0112
Residual	5	117,751.61	23,550.32
Total	6	480,142.86

		Standard			Lower	Upper
	Coefficients	Error	t Stat	P-value	95%	95%
Intercept	1784.73	884.55	2.02	0.10	-489.09	4,058.55
X Variable 1	0.22	0.06	3.92	0.01	0.07	0.36

Answer 1

Part 1

Y = $0.22 X + $1784.73

Variable cost = coefficients – X variable 1

Fixed cost = coefficients – intercept

Part 2

R – square = 0.75

It indicates that the cost equation has 75% of the variability in the data. It means that the data should be used cautiously. Kerri Tran may not use this equation confidently to predict total costs at other volumes within the same relevant range.

Part 3

Y = $0.22 X + $1784.73

For 16500 miles

Y = (0.22*16500)+1784.73 = $5414.73