R2 = .909 For population only Coefficientsa Standardized Unstandardized Coefficients Coefficients Model B Std. Error Beta t Sig. 1 (Constant) -12.183 16.726 -.728 .487 Population 3.864 .509 .937 7.589 .000 a. Dependent Variable: Sales = ..878 R2 The regression equation with the highest R2 is: Sales = -4.152 + 4.759P 5.574S 1.427Y – – Where = population and size of store and = years’ experience. P S= Y However neither size of store or years’ experience are significant, so the most reliable equation is Sales = -12.183 + 3.864P (d) Using the full equation Sales = -4.152 + 4.759×28 5.574×6– 1.427×12 – = 112.78 or £112,780 Using the shorter equation Sales = -12.183 + 3.864×28 = 96.01 or £96,010 15. H0 = 0 β0 H1 0 β0 ≠ From question 13 we know that = 375 and = 20377 and the slope is .0184 ∑x ∑x2 We obtain Se from the table below Advertising Sales y Predicted Residual Residual sq x
R2 = .909 For population only Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) -12.183 16.726 -.728 .487 Population 3.864 .509 .937 7.589 .000 a. Dependent Variable: Sales R2 = ..878 The regression equation with the highest R2 is: Sales = -4.152 + 4.759P – 5.574S – 1.427Y Where P = population and S= size of store and Y = years’ experience. However neither size of store or years’ experience are significant, so the most reliable equation is Sales = -12.183 + 3.864P (d) Using the full equation Sales = -4.152 + 4.759×28 – 5.574×6– 1.427×12 = 112.78 or £112,780 Using the shorter equation Sales = -12.183 + 3.864×28 = 96.01 or £96,010 15. H0 β0 = 0 H1 β0 ≠ 0 From question 13 we know that ∑x = 375 and ∑x2 = 20377 and the slope is .0184 We obtain Se from the table below Advertising x Sales y Predicted Residual Residual sq