You have purchased a laundry and dry-cleaning shop, and are interested in forecasting demand for your services. After some analysis using past data, you generate the following, where sales (Q) is the dependent variable:

Regression Statistics
Multiple R	0.945298
R Square	0.893588
Adjusted R Square	0.88131
Standard Error	19.92731
Observations	50
ANOVA
	Df	SS
Regression	4	86699.63
Residual	45	10324.54
Total	49	97024.17
	Coefficients	Standard Error
Intercept	5.55	0.57
P	-10.69	3.15
Y	0.92	0.39
Pc	5.25	2.08

where Q=number of shirts laundered per week in hundreds, P=price in dollars per laundered shirt, Y=per capita income (in thousands of dollars), and P_c = price charged by another launderer two blocks away.

1. Interpret the price coefficient. Show the original form of the equation implied by the regression. Predict sales when P=$2, Y=40, and P_C=$1.50.
2. Suppose you raise the price by $1. Predict the change in sales. Will total revenue rise or fall? Calculate.
3. At the 95% confidence level, are the variables significant? Use a critical t-stat of 2.0, and calculate your t-statistics.