Difference between revisions of "SumSquaredErrors Command"

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<noinclude>{{Manual Page|version=5.0}}</noinclude>{{command|statistics}}
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;SumSquaredErrors( &lt;List of Points>, <Function> )
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:Calculates the sum of squared errors, SSE, between the y-values of the points in the list and the function values of the x-values in the list.
{{command|statistics}}
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:{{example|1= If we have a list of points <code><nowiki>L={(1, 2), (3, 5),(2, 2), (5, 2), (5, 5)}</nowiki></code>  and have calculated for example: <code>f(x)=FitPoly(L,1)</code> and <code>g(x)=FitPoly(L,2)</code>. <code>SumSquaredErrors(L,f)</code> yields ''9'' and <code>SumSquaredErrors(L,g)</code> yields ''6.99'', and therefore we can see, that ''g(x)'' offers the best fit, in the sense of the least sum of squared errors (Gauss).}}
;SumSquaredErrors[ <List of Points>, <Function> ]
 
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Latest revision as of 09:24, 12 October 2017


SumSquaredErrors( <List of Points>, <Function> )
Calculates the sum of squared errors, SSE, between the y-values of the points in the list and the function values of the x-values in the list.
Example: If we have a list of points L={(1, 2), (3, 5),(2, 2), (5, 2), (5, 5)} and have calculated for example: f(x)=FitPoly(L,1) and g(x)=FitPoly(L,2). SumSquaredErrors(L,f) yields 9 and SumSquaredErrors(L,g) yields 6.99, and therefore we can see, that g(x) offers the best fit, in the sense of the least sum of squared errors (Gauss).
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