Model 1 Regression Sum of ANOVA Residual Total Squares df Mean Square F Sig. 546491.123 2...

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Model 1 Regression Sum of ANOVA Residual Total Squares df Mean Square F Sig. 546491.123 2 273245.561 71.080 .000b 757308.377 197 3844.205 1303799.500 199 a. Dependent Variable: laptop sales b. Predictors: (Constant), product attractiveness, internet ads Coefficients Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant) -2.461 22.096 -.111 .911 internet ads 3.640 .365 .552 9.963 .000 product attractiveness 14.020 3.141 .247 4.464 .000 a. Dependent Variable: laptop sales These tables provide the output from a multiple regression analysis looking at the impact of number of internet acs and product attractiveness on online laptop sales. Does this model result in significantly better predictions than those based solely on the mean? How do you know? No, because the p-value associated with the F statistic is not less than .05. Yes, because the p-value associated with the t statistic is not less than .05. Yes, because the p-value associated with the F statistic is less than .05. No, because the R-square value is not greater than .50. Model 1 Regression Sum of ANOVA Residual Total Squares df Mean Square F Sig. 546491.123 2 273245.561 71.080 .000b 757308.377 197 3844.205 1303799.500 199 a. Dependent Variable: laptop sales b. Predictors: (Constant), product attractiveness, internet ads Coefficients Unstandardized Coefficients Standardized Coefficients Model B Std. Error Beta t Sig. 1 (Constant) -2.461 22.096 -.111 .911 internet ads 3.640 .365 .552 9.963 .000 product attractiveness 14.020 3.141 .247 4.464 .000 a. Dependent Variable: laptop sales These tables provide the output from a multiple regression analysis looking at the impact of number of internet acs and product attractiveness on online laptop sales. Does this model result in significantly better predictions than those based solely on the mean? How do you know? No, because the p-value associated with the F statistic is not less than .05. Yes, because the p-value associated with the t statistic is not less than .05. Yes, because the p-value associated with the F statistic is less than .05. No, because the R-square value is not greater than .50.