Stats: Modeling the World Nasta Edition Grades 9-12
Stats: Modeling the World Nasta Edition Grades 9-12
3rd Edition
ISBN: 9780131359581
Author: David E. Bock, Paul F. Velleman, Richard D. De Veaux
Publisher: PEARSON
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Chapter 8, Problem 54E
To determine

To explain what “least squares” means, using these data as a specific example.

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Explanation of Solution

It is given in the question that, we should consider the four points:

  (200,1950),(400,1650),(600,1800),(800,1600)

And the least square line for this is as:

  y^=19750.45x

As the line is given and the points are given, so we used the formula of the line to calculate the predicted values and then find the residual by the formula that,

  Residual=ActualPredicted

Thus, by using excel we have that,

    xyPredicted yResidualSquare of residual
    2001950=1975-(0.45*AQ83)=AR83-AS83=AT83*AT83
    4001650=1975-(0.45*AQ84)=AR84-AS84=AT84*AT84
    6001800=1975-(0.45*AQ85)=AR85-AS85=AT85*AT85
    8001600=1975-(0.45*AQ86)=AR86-AS86=AT86*AT86
    Total==SUM(AU83:AU86)

Thus, the result is as follows:

    xyPredicted yResidualSquare of residual
    20019501885654225
    40016501795-14521025
    60018001705959025
    80016001615-15225
    Total=34500

Therefore, we can say that in this context “least-square” means that this is the best fit line for the equation because the sum of the squared residual is 34500 and the sum of the residuals on any other line would be greater than 34500 .

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