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An Actuary Wanted to Develop a Model to Predict How x1x _ { 1 }

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An actuary wanted to develop a model to predict how long individuals will live. After consulting a number of physicians, she collected the age at death (y), the average number of hours of exercise per week ( x1x _ { 1 } ), the cholesterol level ( x2x _ { 2 } ), and the number of points by which the individual's blood pressure exceeded the recommended value ( X3X _ { 3 } ). A random sample of 40 individuals was selected. The computer output of the multiple regression model is shown below:
THE REGRESSION EQUATION IS y=y = 55.8+1.79x10.021x20.016x355.8 + 1.79 x _ { 1 } - 0.021 x _ { 2 } - 0.016 x _ { 3 }  Predictor  Coef  StDev  T  Constant 55.811.84.729x11.790.444.068x20.0210.0111.909x30.0160.0141.143\begin{array} { | c | c c c | } \hline \text { Predictor } & \text { Coef } & \text { StDev } & \text { T } \\\hline \text { Constant } & 55.8 & 11.8 & 4.729 \\x _ { 1 } & 1.79 & 0.44 & 4.068 \\x _ { 2 } & - 0.021 & 0.011 & - 1.909 \\x _ { 3 } & - 0.016 & 0.014 & - 1.143 \\\hline\end{array} S = 9.47 R-Sq = 22.5%.  ANALYSIS OF VARIANCE  Source of Variation  df  SS  MS  F  Regression 39363123.477 Error 36323089.722 Total 394166\begin{array}{l}\text { ANALYSIS OF VARIANCE }\\\begin{array} { | l | c c c c | } \hline \text { Source of Variation } & \text { df } & \text { SS } & \text { MS } & \text { F } \\\hline \text { Regression } & 3 & 936 & 312 & 3.477 \\\text { Error } & 36 & 3230 & 89.722 & \\\hline \text { Total } & 39 & 4166 & & \\\hline\end{array}\end{array} Is there enough evidence at the 10% significance level to infer that the model is useful in predicting length of life?


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