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

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) $H _ { 0 } : \beta _ { 1 } = \beta _ { 2 } = 0$