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TABLE 14-12
a Weight-Loss Clinic Wants to Use Y=β0+β1X1+β2X2+β3X3+β4X1X2+β5X1X3+ε Y=\beta_{0}+\beta_{1} X_{1}+\beta_{2} X_{2}+\beta_{3} X_{3}+\beta_{4} X_{1} X_{2}+\beta_{5} X_{1} X_{3}+\varepsilon

question 51

True/False

TABLE 14-12
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)
X
2 = 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+β5X1X3+ε Y=\beta_{0}+\beta_{1} X_{1}+\beta_{2} X_{2}+\beta_{3} X_{3}+\beta_{4} X_{1} X_{2}+\beta_{5} X_{1} X_{3}+\varepsilon
Partial output from Microsoft Excel follows:
 Regression Statistics  Multiple R 0.73514 R Square 0.540438 Adjusted R Square 0.157469 Standard Error 12.4147 Observations 12\begin{array}{l}\text { Regression Statistics }\\\begin{array} { l c } \hline \text { Multiple R } & 0.73514 \\\text { R Square } & 0.540438 \\\text { Adjusted R Square } & 0.157469 \\\text { Standard Error } & 12.4147 \\\text { Observations } & 12 \\\hline\end{array}\end{array}

ANOVA
F=5.41118 Significance F=0.040201F = 5.41118 \quad\text { Significance } F = 0.040201

Coefficients  Standard Error  t Stat  p -valueIntercept 0.08974414.1270.00600.9951Length (X1)6.225382.434732.549560.0479Morn Ses (X2)2.21727222.14160.1001410.9235Aft Ses (X3)11.82333.15453.5589010.0165Length*Morn Ses0.770583.5620.2163340.8359Length * Aft Ses0.541473.359880.1611580.8773\begin{array}{lcccr}\hline & \text {Coefficients }& \text { Standard Error }& \text { t Stat }& \text { p -value} \\\hline \text {Intercept }& 0.089744 & 14.127 & 0.0060 & 0.9951 \\ \text {Length (X1)}& 6.22538 & 2.43473 & 2.54956 & 0.0479 \\ \text {Morn Ses (X2)}& 2.217272 & 22.1416 & 0.100141 & 0.9235 \\ \text {Aft Ses (X3)} & 11.8233 & 3.1545 & 3.558901 & 0.0165 \\ \text {Length*Morn Ses} & 0.77058 & 3.562 & 0.216334 & 0.8359 \\ \text {Length * Aft Ses} & -0.54147 & 3.35988 & -0.161158 & 0.8773 \\\hline\end{array}
-Referring to Table 14-12, the overall model for predicting weight-loss (Y) is statistically significant at the 0.05 level.


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