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The 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 82 101 EModel Summary Adjusted R Sod, Error of Model R R Square ovents the Estimate 1 .983" 967 950 5.46455 a. Predictors: (Constant, stores, Price, Advertising ANOVA Sum of Model Squares df ovenbs ueon F 1 Regression 5179.232 1726.411 57.814 -000" Residual 179.168 29.861 Total 5358.400 a. Dependent Variable: Sales b. Predictors: (Constant), stores, Price, Advertising Coefficients Standardized 95.0% Confidence Interval for Unstandardized Coefficients Coefficients B Model H Std. Error Beti Sig. Lower Bound Upper Sound 1 (Constant 30.992 7.728 4.010 -007 12.081 49.902 Advertising .820 .502 .709 1.633 .154 -.409 2.049 Price -.325 .089 -.288 -3.638 .011 -.544 -.106 stores 1.841 3.855 .209 -478 -650 -7.592 11.273 a. Dependent Variable: Sales