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SCENARIO 17-6
a Weight-Loss Clinic Wants to Use Regression Analysis

question 197

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SCENARIO 17-6
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 X3=1 if afternoon session, 0 if not  (Base level = evening session)  \begin{aligned} Y & = \text { Weight-loss (in pounds) } \\ X _ { 1 } & = \text { Length of time in weight-loss program (in months) } \\ X _ { 2 } & = 1 \text { if morning session, } 0 \text { if not } \\ X _ { 3 } & = 1 \text { if afternoon session, } 0 \text { if not } \quad \text { (Base level = evening session) } \end{aligned}
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\quad 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}{ll}{\text { Regression Statistics }} \\\text { Multiple R } & 0.73514 \\\text { R Square } & 0.540438 \\\text { Adjusted R Square } & 0.157469 \\\text { Standard Error } & 12.4147 \\\text { Observations } & 12\end{array}

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

 Coeff  StdError t Stat P-value  Intercept 0.08974414.1270.00600.9951 Length (X1) 6.225382.434732.549560.0479 Morn Ses (X2) 2.21727222.14160.1001410.9235 Aft Ses (X3) 11.82333.15453.5589010.0165 Length*Morn Ses 0.770583.5620.2163340.8359 Length"Aft Ses 0.541473.359880.1611580.8773\begin{array}{ccccc} & \text { Coeff } & \text { StdError } & t \text { Stat } & P \text {-value } \\\text { Intercept } & 0.089744 & 14.127 & 0.0060 & 0.9951 \\\text { Length }\left(X_{1}\right) & 6.22538 & 2.43473 & 2.54956 & 0.0479 \\\text { Morn Ses }\left(X_{2}\right) & 2.217272 & 22.1416 & 0.100141 & 0.9235 \\\text { Aft Ses }\left(X_{3}\right) & 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\end{array}


-Referring to Scenario 17-6, in terms of the ?s in the model, give the mean change in weight- loss (Y) for every 1 month increase in time in the program (X1) when attending the afternoon
Session.


Definitions:

Segment Data

This refers to the financial information that is reported separately for different divisions or segments of a company, providing insight into the performance of each segment.

Recent Years

A term referring to the latest or last few years, without a precise timeframe but generally considered to encompass the immediate past.

Cash Receipts Journal

An accounting ledger that tracks cash transactions coming into the business, documenting all cash sales and receivables.

Chart of Accounts

A structured list of all the financial accounts in the general ledger of a company, used for organizing transactions and financial data.

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