He manufacturer of a light fixture believes that the dollars spent on advertising, the price of the fixture, and the number of retail stores selling the fixture in a particular month influence the light fixture sales. the manufacturer randomly selects 10 months and collects the following data:

sales advertising price # of stores

41 20 40 1

42 40 60 3

59 40 20 4

60 50 80 5

81 50 10 6

80 60 40 6

100 70 20 7

82 70 60 8

101 80 30 9

110 90 40 10

the sales are in thousands of units per month, the advertising is given in hundreds of dollars per month, and the price is the unit retail price for the particular month. using minitab, the following computer output is obtained.

the regression equation is
sales = 31.0 + 0.820 advertising - 0.325 price + 1.84 stores

predictor coef stdev t p

constant 30.992 7.728 4.01 .007

advertising .8202 .5023 1.63 .154

price -.32502 .08935 -3.64 .011

stores 1.841 3.855 .48 .650

s= 5.465 r-sq=96.7% r-sq(adj)=95.0%

analysis of variance

source df ss ms f p

regression 3 5179.2 1726.4 57.81 .000

residual error 6 179.2 29.9

total 9 5358.4

interpret the regression coefficients for the variables advertising, price, and store.