Important Request

[Han]

(04-03-2017 11:30 PM)math7 Wrote:  Yes, something like that is what i mean Tim, I copied your code into the statistical application and it works fine, that's what I mean by doing the HP Prime, that she calculates and chooses the best approach, I think it should not be so difficult if you already did it for a Model as the complete program would be for the 13 models, a little more code, and the calculator would choose the one that has a better fit to the data, it is simply something like that, if the HP 50g exists I do not see why not in the Prime.

Note: I suppose for a lot of points as it does take more processing and amount of memory in the calculator, but the option would be viable for practical life problems for about 20 or 30 points I think the calculator is quite capable and speedy to calculate something so.

You did not read Tim's response very carefully. The program he posted only works on regression models whose unknown parameters are linear. Even then, given any n distinct points, a polynomial fit (of degree n-1) will always be exact (i.e. "best fit") since it will pass through every single one of the n points. Thus, the "answer" to the "best fit" question is always a polynomial fit. Of course, this is also nonsense because polynomials are not always the best models of data (consider any set of data corresponding to periodic phenomenon).

It would NOT be easy to generalize Tim's program to all the other fits because there is not "standard" for comparison. It would be like comparing apples to oranges (because only some of the regression models are linear; others are not). You cannot define "best" when there is no standard for comparison. You cannot even use a "least amount of error" type of comparison because a polynomial fit will always have 0 error unless your points have repeated input values.