Coffee vendors have been getting popular in the city of Auckland. A vendor is interested in knowing how temperature (in degrees Celsius) impacts daily hot coffee sales revenue (in $00’s). A random sample of 6 days was taken, with the daily hot coffee sales revenue and the corresponding temperature of that day noted. The regression is run and output from excel is given

SUMMARY OUTPUT
Regression Statistics
Multiple R	0.864422274
R Square	0.747225868
Adjusted R Square	0.684032336
Standard Error	5.202681673
Observations	6
ANOVA
	df	SS	MS	F	Significance F
Regression	1	320.061747	320.0617	11.82440409	0.026325931
Residual	4	108.2715863	27.0679
Total	5	428.3333333
	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%	Lower 95.0%	Upper 95.0%
Intercept	27.71787149	5.662896038	4.894646	0.008075011	11.9951515	43.44059147	11.9951515	43.44059147
Tempreature (X)	-0.694277108	0.201903206	-3.43866	0.026325931	-1.254850276	-0.133703941	-1.25485028	-0.133703941

a. Interpret the coefficient of X-variable. [Hint: note sales revenue (in $00’s).
b. Estimate daily hot beverage sales revenue on a day of 25 degrees Celsius using estimated Model.
c. Calculate error value for the first observation with 25 degrees Celsius.
d. State value of Sum Square Regression (SSR).
e. State coefficient of determination and interpret. Conduct a test of the slope (coefficient of temperature) to determine at the 10% significance level whether there is existence of negative linear relationship between temperature and beverage sales revenue. [Hint: show six step procedure and utilize excel output wherever you can).