The data frame GPAGender contains data for 343 students, including their Grade Point Average (GPA) and heart rate (Pulse). Suppose you run the following code: supernova ( Im (GPA-Pulse, data = GPAGender) ) Im (GPA-Pulse, data = GPAGender) Analysis of Variance Table (Type III SS) Model: GPA ~ Pulse ss df MS F PRE P Model (error reduced) 0. 704 1 0. 704 4. 497 0. 0130 .0347 Error ( from model) 53. 368 341 0. 157 - . . . Total (empty model) 54. 072 342 0. 158 Call: Lm( formula = GPA - Pulse, data = GPAGender) Coefficients: (Intercept) Pulse 2 . 895880 0. 003754 At a 5% level of confidence, can you conclude that, in the DGP, some variation in GPA is explained by Pulse? O No, because 0.003754 is small. Yes, because 0.0347 is less that 0.05. No, because 0.013 is small. O No, because 0.0347 is less that 0.05