A weight-loss clinic wants to use regression analysis to build a model for weight loss of a client (measured in pounds). Two variables thought to affect weight loss are client's length of time on the weight-loss program and time of session. These variables are described below: Y = Weight loss (in pounds) X1 = Length of time in weight-loss program (in months) X2 = 1 if morning session, 0 if not Data for 25 clients on a weight-loss program at the clinic were collected and used to fit the model with interaction term: Y = Bo + B1X1 + 32X2 + B3X1X2 + E Output from Microsoft Excel follows: Coefficients Standard Error t Stat P-value Lower 99% Upper 99% Intercept -20.7298 22.3710 -0.9266 0.3646 -84.0702 42.6106 Length 7.2472 1.4992 4.8340 0.0001 3.0024 11.4919 Morn 90.1981 40.2336 2.2419 0.0359 -23.7176 204.1138 Length x Morn -5.1024 3.3511 -1.5226 0.1428 -14.5905 4.3857 O There is sufficient evidence (at a = 0.05) of curvature in the relationship between weight loss (Y) and months on program (X1). There is insufficient evidence (at a = 0.05) of curvature in the relationship between weight loss (Y) and months on program(X1). There is sufficient evidence (at a = 0.05) to indicate that the relationship between weight loss (Y) and months on program (X1) varies with session time. O There is insufficient evidence (at a = 0.05) to indicate that the relationship between weight loss (Y) and months on program(X1) varies with session time