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A Statistician Wanted to Determine Whether the Demographic Variables of Age

question 78

Essay

A statistician wanted to determine whether the demographic variables of age, education and income influence the number of hours of television watched per week. A random sample of 25 adults was selected to estimate the multiple regression model y=β0+β1x1+β2x2+β3x3+εy = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 } + \beta _ { 3 } x _ { 3 } + \varepsilon .
Where:
y = number of hours of television watched last week. x1x _ { 1 } = age. x2x _ { 2 } = number of years of education. x3x _ { 3 } = income (in $1000s).
The computer output is shown below.
THE REGRESSION EQUATION IS y=y = 22.3+0.41x10.29x20.12x322.3 + 0.41 x _ { 1 } - 0.29 x _ { 2 } - 0.12 x _ { 3 }  Predictor  Coef  StDev T Constant 22.310.72.084x10.410.192.158x20.290.132.231x30.120.034.00\begin{array} { | c | c c c | } \hline \text { Predictor } & \text { Coef } & \text { StDev } & \mathrm { T } \\\hline \text { Constant } & 22.3 & 10.7 & 2.084 \\x _ { 1 } & 0.41 & 0.19 & 2.158 \\x _ { 2 } & - 0.29 & 0.13 & - 2.231 \\x _ { 3 } & - 0.12 & 0.03 & - 4.00 \\\hline\end{array} S = 4.51 R-Sq = 34.8%.  ANALYSIS OF VARIANCE  Source of Variation  df  SS  MS  F  Regression 322775.6673.730 Error 2142620.286 Total 24653\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 & 227 & 75.667 & 3.730 \\\text { Error } & 21 & 426 & 20.286 & \\\hline \text { Total } & 24 & 653 & & \\\hline\end{array}\end{array} Is there sufficient evidence at the 1% significance level to indicate that hours of television watched and education are negatively linearly related?


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