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

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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= Y= Weight loss (in pounds)
X1= X_{1}= Length of time in weight-loss program (in months)
X2=1 X_{2}=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=β0+β1X1+β2X2+β3X1X2+ε Y=\beta_{0}+\beta_{1} X_{1}+\beta_{2} X_{2}+\beta_{3} X_{1} X_{2}+\varepsilon

 Output from Microsoft Excel follows: \text { Output from Microsoft Excel follows: }

 Regression Statistics  Multiple R 0.7308 R Square 0.5341 Adjusted R Square 0.4675 Standard Error 43.3275 Observations 25\begin{array}{lr}{\text { Regression Statistics }} \\\hline \text { Multiple R } & 0.7308 \\\text { R Square } & 0.5341 \\\text { Adjusted R Square } & 0.4675 \\\text { Standard Error } & 43.3275 \\\text { Observations } & 25 \\\hline\end{array}

 ANOVA \text { ANOVA }
 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)   X_{1}=   Length of time in weight-loss program (in months)   X_{2}=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=\beta_{0}+\beta_{1} X_{1}+\beta_{2} X_{2}+\beta_{3} X_{1} X_{2}+\varepsilon     \text { Output from Microsoft Excel follows: }    \begin{array}{lr} {\text { Regression Statistics }} \\ \hline \text { Multiple R } & 0.7308 \\ \text { R Square } & 0.5341 \\ \text { Adjusted R Square } & 0.4675 \\ \text { Standard Error } & 43.3275 \\ \text { Observations } & 25 \\ \hline \end{array}    \text { ANOVA }       \begin{array}{lrrrrrrr} \hline & \text { Coefficients } & \text { Standard Error } & {t \text { Stot }} & \rho_{\text {-value }} & \text { Lower 99\% } & \text { Upper 99\% } \\ \hline \text { Intercept } & -20.7298 & 22.3710 & -0.9266 & 0.3646 & -84.0702 & 42.6106 \\ \text { Length } & 7.2472 & 1.4992 & 4.8340 & 0.0001 & 3.0024 & 11.4919 \\ \text { Morn } & 90.1981 & 40.2336 & 2.2419 & 0.0359 & -23.7176 & 204.1138 \\ \text { Length × Morn } & -5.1024 & 3.3511 & -1.5226 & 0.1428 & -14.5905 & 4.3857 \end{array}    -Referring to Scenario 14-11, which of the following statements is supported by the analysis shown? a) There is sufficient evidence (at  \alpha = 0.05  ) of curvature in the relationship between weight loss  ( Y )  and months on program  \left( X _ { 1 } \right) . b) There is sufficient evidence (at  \alpha = 0.05  ) to indicate that the relationship between weight loss  ( Y )  and months on program  \left( X _ { 1 } \right)  varies with session time. c) There is insufficient evidence (at  \alpha = 0.05  ) of curvature in the relationship between weight loss  ( Y )  and months on program  \left( X _ { 1 } \right) . d) There is insufficient evidence (at  \alpha = 0.05  ) to indicate that the relationship between weight loss  ( Y )  and months on program  \left( X _ { 1 } \right)  varies with session time.

 Coefficients  Standard Error t Stot ρ-value  Lower 99%  Upper 99%  Intercept 20.729822.37100.92660.364684.070242.6106 Length 7.24721.49924.83400.00013.002411.4919 Morn 90.198140.23362.24190.035923.7176204.1138 Length × Morn 5.10243.35111.52260.142814.59054.3857\begin{array}{lrrrrrrr}\hline & \text { Coefficients } & \text { Standard Error } & {t \text { Stot }} & \rho_{\text {-value }} & \text { Lower 99\% } & \text { Upper 99\% } \\\hline \text { Intercept } & -20.7298 & 22.3710 & -0.9266 & 0.3646 & -84.0702 & 42.6106 \\\text { Length } & 7.2472 & 1.4992 & 4.8340 & 0.0001 & 3.0024 & 11.4919 \\\text { Morn } & 90.1981 & 40.2336 & 2.2419 & 0.0359 & -23.7176 & 204.1138 \\\text { Length × Morn } & -5.1024 & 3.3511 & -1.5226 & 0.1428 & -14.5905 & 4.3857\end{array}


-Referring to Scenario 14-11, which of the following statements is supported by the analysis shown? a) There is sufficient evidence (at α=0.05\alpha = 0.05 ) of curvature in the relationship between weight loss (Y)( Y ) and months on program (X1)\left( X _ { 1 } \right) .
b) There is sufficient evidence (at α=0.05\alpha = 0.05 ) to indicate that the relationship between weight loss (Y)( Y ) and months on program (X1)\left( X _ { 1 } \right) varies with session time.
c) There is insufficient evidence (at α=0.05\alpha = 0.05 ) of curvature in the relationship between weight loss (Y)( Y ) and months on program (X1)\left( X _ { 1 } \right) .
d) There is insufficient evidence (at α=0.05\alpha = 0.05 ) to indicate that the relationship between weight loss (Y)( Y ) and months on program (X1)\left( X _ { 1 } \right) varies with session time.

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