A multiple regression analysis was performed using model: Analysis of variance: Model regression df 2 sum of squares 0.744384; Error df 2 sum of squares 2.53933 Parameter estimates: variable intercept df 1 parameter estimate -27.91180 standard error 12.8900; variable year df 1 parameter estimate 0.01498 standard error 0.00650; variable manufacturer df 1 -0.10782 standard error 0.11573 Researchers are interested in deciding if the change in manufacturer had any effect on the average number of home runs hit per game per team. To answer this question, they decide to test the null hypotheses H0: 2 = 0 v. H1: 2 0. (Where 2 is the coefficient on the variable "Manufacturer.") what should they conclude concerning the effect of the change in the manufacturer on the number of home runs per game? Group of answer choices Since the t value equals -0.93 they should not reject the null. Since the t value equals +0.93 they should not reject the null. Since the t value equals -0.93 they should reject the null. Since the t value equals +0.93 they should reject the null