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=po+p1X1 + B2X2 + B3X1X2 + E
Output from Microsoft Excel follows:
Standard Error
P-value
Lower 99%
t Stat
Coefficients
Upper 99%
42.6106
22.3710
-84.0702
-20.7298
-0.9266
0.3646
Intercept
0.0001
3.0024
4.8340
11.4919
1.4992
7.2472
Length
2.2419
204.1138
0.0359
40.2336
-23.7176
Morn
90.1981
0.1428
-5.1024
3.3511
4.3857
-1.5226
-14.5905
Length X Morn
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) of curvature in the relationship between weight loss (Y) and months on program (X1).
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.
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.
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