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An Economist Wanted to Develop a Multiple Regression Model to Enable

question 84

Essay

An economist wanted to develop a multiple regression model to enable him to predict the annual family expenditure on clothes. After some consideration, he developed 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 family clothes expenditure (in $1000s) x1x _ { 1 } = annual household income (in $1000s) x2x _ { 2 } = number of family members X3X _ { 3 } = number of children under 10 years of age
The computer output is shown below.
THE REGRESSION EQUATION IS y=y = 1.74+0.091x1+0.93x2+0.26x31.74 + 0.091 x _ { 1 } + 0.93 x _ { 2 } + 0.26 x _ { 3 }  Predictor  Coef  StDev T Constant 1.740.6302.762x10.0910.0253.640x20.930.2903.207x30.260.1801.444\begin{array} { | c | c c c | } \hline \text { Predictor } & \text { Coef } & \text { StDev } & \mathrm { T } \\\hline \text { Constant } & 1.74 & 0.630 & 2.762 \\x _ { 1 } & 0.091 & 0.025 & 3.640 \\x _ { 2 } & 0.93 & 0.290 & 3.207 \\x _ { 3 } & 0.26 & 0.180 & 1.444 \\\hline\end{array} S = 2.06 R-Sq = 59.6%.  ANALYSIS OF VARIANCE  Source of Variation  df  SS  MS  F  Regression 32889622.647 Error 461954.239 Total 49483\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 & 288 & 96 & 22.647 \\\text { Error } & 46 & 195 & 4.239 & \\\hline \text { Total } & 49 & 483 & & \\\hline\end{array}\end{array} Test the overall model's validity at the 5% significance level.


Definitions:

Basal Ganglia

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A genetic disorder that causes the progressive breakdown of nerve cells in the brain, leading to physical, cognitive, and psychological symptoms.

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The outer layer of the cerebrum in the brain, involved in complex functions such as thought, perception, and memory.

Classical Conditioning

A teaching approach whereby, through consistent pairing, a response initially elicited by a secondary stimulus is in time elicited by the primary stimulus.

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