Kathy Jungemann, owner of Rose Ready, operates a local chain of floral shops. Each shop has its own delivery van. Instead of charging a flat delivery fee, Jungemann wants to set the delivery fee based on the distance driven to deliver the flowers. Jungemann 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. Rose Ready does a regression analysis on the next year's data using Excel. The output generated by Excel is as follows:

SUMMARY OUTPUT

Regression Statistics

Multiple R	0.86
R Square	0.75
Adjusted R Square	0.70
Standard Error	171.55
Observations	7

ANOVA

	df	SS	MS	F	Significance F
Regression	1	435,336.22	435,336.22	14.79	0.0120
Residual	5	147,149.49	29,429.90
Total	6	582,485.71

		Standard			Lower	Upper
	Coefficients	Error	t Stat	P-value	95%	95%
Intercept	709.81	1,150.73	0.62	0.56	-2,248.24	3,667.85
X Variable 1	0.29	0.07	3.85	0.01	0.09	0.48

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 Rose Ready's R-square indicate?

3. Predict van operating costs at a volume of 15,500 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 + $____________________