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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

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 Pc = 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 PC=$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.

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