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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, what null hypothesis would you test to determine whether the slope of the linear relationship between weight loss (Y) and time on the program (X1) varies According to time of session? a)  H _ { 0 } : \beta _ { 1 } = 0  b)  H _ { 0 } : \beta _ { 2 } = 0  c)  H _ { 0 } : \beta _ { 3 } = 0  d)  H _ { 0 } : \beta _ { 1 } = \beta _ { 2 } = 0

 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, what null hypothesis would you test to determine whether the slope of the linear relationship between weight loss (Y) and time on the program (X1) varies
According to time of session? a) H0:β1=0H _ { 0 } : \beta _ { 1 } = 0
b) H0:β2=0H _ { 0 } : \beta _ { 2 } = 0
c) H0:β3=0H _ { 0 } : \beta _ { 3 } = 0
d) H0:β1=β2=0H _ { 0 } : \beta _ { 1 } = \beta _ { 2 } = 0

Recognize the influence of cultural values on leadership and follower roles.
Identify strategies leaders use to manage performance deficiencies.
Explain how leaders’ actions and decisions impact follower perceptions and attributions.
Understand the different roles of leaders and followers within organizational settings.

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CLX Controller

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