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SCENARIO 13-11
a Weight-Loss Clinic Wants to Use Regression Analysis Y=β0+β1X1+β2X2+β3X1X2+εY = \beta _ { 0 } + \beta _ { 1 } X _ { 1 } + \beta _ { 2 } X _ { 2 } + \beta _ { 3 } X _ { 1 } X _ { 2 } + \varepsilon

question 36

Short Answer

SCENARIO 13-11
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
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+εY = \beta _ { 0 } + \beta _ { 1 } X _ { 1 } + \beta _ { 2 } X _ { 2 } + \beta _ { 3 } X _ { 1 } X _ { 2 } + \varepsilon Output from Microsoft Excel follows:  Multiple R 0.7308 R Square 0.5341 Adjusted R Square 0.4675 Standard Error 43.3275 Observations 25 ANOVA df SS  MS  Significance F Regression 345194.066115064.68878.02480.0009 Residual 2139422.65421877.2692 Total 2484616.7203 Coefficients  Standard Error t Stat  P-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 x Morn 5.10243.35111.52260.142814.59054.3857\begin{array}{l}\begin{array} { l r } \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} { l r r l l r } \hline & d f & & { \text { SS } } & { \text { MS } } & \text { Significance } F \\\hline \text { Regression } & 3 & 45194.0661 & 15064.6887 & 8.0248 & 0.0009 \\\text { Residual } & 21 & 39422.6542 & 1877.2692 & & \\\text { Total } & 24 & 84616.7203 & &\\\hline\end{array}\\\\\begin{array} { l r r r r r r } \hline & \text { Coefficients } & \text { Standard Error } & { t \text { Stat } } & \text { P-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 x Morn } & -5.1024 & 3.3511 & -1.5226 & 0.1428 & -14.5905&4.3857\\\hline\end{array}\end{array}
-Referring to SCENARIO 13-11, 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


Definitions:

Operant Conditioning

A method of learning that occurs through rewards and punishments for behavior.

Deviant People

Individuals who significantly deviate from societal norms in behavior, beliefs, or attitudes, often leading to social disapproval.

Modeling

A behavior modification technique that involves observing the behavior of others (the models) and participating with them in performing the desired behavior.

Vicariously

Experiencing something indirectly or through another person's experience, often used in the context of learning or emotional experiences.

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