SCENARIO 14-11 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) Length of time in weight-loss program (in months) 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 interaction model: Y Output from Microsoft Excel follows:
-Referring to Scenario 14-11, in terms of the in the model, give the mean change in weight loss (Y)for every 1 month increase in time on the program when not attending the morning session.

Question

SCENARIO 14-11 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)  Length of time in weight-loss program (in months)  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 interaction model: Y  Output from Microsoft Excel follows:  
-Referring to Scenario 14-11, in terms of the  in the model, give the mean change in weight loss (Y)for every 1 month increase in time on the program  when not attending the morning session.

Quizplus · Accepted Answer

From the output table, we can see that the coefficient estimate for the interaction term "Time x Morning" is -2.5. This means that for clients who do not attend morning sessions, for every 1 month increase in time on the program, they can expect to lose an average of 2.5 pounds less. Therefore, the mean change in weight loss for every 1 month increase in time on the program when not attending the morning session is -2.5 pounds.

SCENARIO 14-11 a Weight-Loss Clinic Wants to Use Regression Analysis