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TABLE 14-11 A Weight-Loss Clinic Wants to Use Regression Analysis to Build

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TABLE 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)
X₁ = Length of time in weight-loss program (in months)
X₂ = 1 if morning session, 0 if not
X₃ = 1 if afternoon session, 0 if not (Base level = evening session)
Data for 12 clients on a weight-loss program at the clinic were collected and used to fit the interaction model:
Y = β₀ + β₁X₁ + β₂X₂ + β₃X₃ + β₄X₁X₂ + β₅X₁X₂ + ε
Partial output from Microsoft Excel follows:
TABLE 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) X₁ = Length of time in weight-loss program (in months) X₂ = 1 if morning session, 0 if not X₃ = 1 if afternoon session, 0 if not (Base level = evening session) Data for 12 clients on a weight-loss program at the clinic were collected and used to fit the interaction model: Y = β₀ + β₁X₁ + β₂X₂ + β₃X₃ + β₄X₁X₂ + β₅X₁X₂ + ε Partial output from Microsoft Excel follows:    -Referring to Table 14-11, the overall model for predicting weight-loss (Y) is statistically significant at the 0.05 level.
-Referring to Table 14-11, the overall model for predicting weight-loss (Y) is statistically significant at the 0.05 level.


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