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An Economist Is Analysing the Incomes of Professionals (Physicians, Dentists y=β0+β1x1+β2x2+β3x3+εy = \beta _ { 0 } + \beta _ { 1 } x _ { 1 } + \beta _ { 2 } x _ { 2 } + \beta _ { 3 } x _ { 3 } + \varepsilon

question 90

Essay

An economist is analysing the incomes of professionals (physicians, dentists and lawyers). He realises that an important factor is the number of years of experience. However, he wants to know if there are differences among the three professional groups. He takes a random sample of 125 professionals and estimates 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
= annual income (in $1000). x1x _ { 1 } = years of experience. x2x _ { 2 } = 1 if physician.
= 0 if not. x3x _ { 3 } = 1 if dentist.
= 0 if not.
The computer output is shown below.
THE REGRESSION EQUATION IS y=y = 71.65+2.07x1+10.16x27.44x371.65 + 2.07 x _ { 1 } + 10.16 x _ { 2 } - 7.44 x _ { 3 } .  Predictor  Coef  StDev T Constant 71.6518.563.860x12.070.812.556x210.163.163.215x37.442.852.611\begin{array} { | c | r r c | } \hline \text { Predictor } & \text { Coef } & \text { StDev } & T \\\hline \text { Constant } & 71.65 & 18.56 & 3.860 \\x _ { 1 } & 2.07 & 0.81 & 2.556 \\x _ { 2 } & 10.16 & 3.16 & 3.215 \\x _ { 3 } & - 7.44 & 2.85 & - 2.611 \\\hline\end{array} S = 42.6 R-Sq = 30.9%.  ANALYSIS OF VARIANCE  Source of Variation  df  SS  MS F Regression 39800832669.33318.008 Error 1212195081814.116 Total 124317516\begin{array}{l}\text { ANALYSIS OF VARIANCE }\\\begin{array} { | l | r c c c | } \hline \text { Source of Variation } & \text { df } & \text { SS } & \text { MS } & F \\\hline \text { Regression } & 3 & 98008 & 32669.333 & 18.008 \\\text { Error } & 121 & 219508 & 1814.116 & \\\hline \text { Total } & 124 & 317516 & & \\\hline\end{array}\end{array} Do these results allow us to conclude at the 1% significance level that the model is useful in predicting the income of professionals?


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