The table below shows quarterly sales and advertising expenditures for a multinational corporation over the last 8 quarters. Using these data, estimate the least squares regression line that shows the relationship between sales and advertising (or how sales is expected to change for a given unit change in advertising). Determine the slope and intercept of the regression line, the standard error of the regression, the absolute value of the of the critical t statistic from a 99% hypothesis test (testing to see if the slope is different than O, and calculated t statistic. Note that both sales and advertising figures are in millions. Sales | Advertising . 1,828 | 576 1,799 | 505 1,434 | 496 1,775 | 554 1,395 | 493 1,371 | 440 1,694 | 599 1,623 | 382 y-hat = 0.1967 + 187.9773X, standard error of regression = 65.9676, t critical = 1.496, t calculated = 3.707 y-hat = 187.9773 +0.1967X, standard error of regression = 65.9676, t critical = 3.707, t calculated = 1.496 y-hat = 0.1967 + 187.9773X, standard error of regression = 0.2718, t critical = 3.707, t calculated = 0.272 y-hat = 1.3816 + 916.3189X, standard error of regression = 0.2718, t critical = 3.707, t calculated = 0.272 y hat = 916.3189 + 13816X, standard error of regression = 174.8291, t critical = 3.707. t calculated = 1496 y-hat = 187.9773 + 0.1967X, standard error of regression = 0.5213, t critical = 3.707, t calculated = 0.272 y-hat = 916.3189 + 1.3816X, standard error of regression = 0.5213, t critical = 3.707, t calculated = 0.272 y-hat = 1.3816 + 916.3189X, standard error of regression = 174,8291, t critical - 1.496, t calculated = 3.707