Difference between revisions of "SumSquaredErrors Command"
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{{command|statistics}} | {{command|statistics}} | ||
| − | ; | + | ;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. | :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|1= If we have a list of points: L={A,B,C,D,E} and have calculated for example: <code>f(x)=FitPoly[L,1]</code> and <code>g(x)=FitPoly[L,2]</code>, then it is possible to decide which of the two functions offers the best fit, in the sense of the least sum of squared errors (Gauss), by comparing: <code>sse_f=SumSquaredErrors[L,f]</code> and <code>sse_g=SumSquaredErrors[L,g]</code>.}} | :{{example|1= If we have a list of points: L={A,B,C,D,E} and have calculated for example: <code>f(x)=FitPoly[L,1]</code> and <code>g(x)=FitPoly[L,2]</code>, then it is possible to decide which of the two functions offers the best fit, in the sense of the least sum of squared errors (Gauss), by comparing: <code>sse_f=SumSquaredErrors[L,f]</code> and <code>sse_g=SumSquaredErrors[L,g]</code>.}} | ||
Revision as of 19:40, 27 October 2014
- 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={A,B,C,D,E} and have calculated for example:
f(x)=FitPoly[L,1]andg(x)=FitPoly[L,2], then it is possible to decide which of the two functions offers the best fit, in the sense of the least sum of squared errors (Gauss), by comparing:sse_f=SumSquaredErrors[L,f]andsse_g=SumSquaredErrors[L,g].
