Hello,

Can you be more specific?

How do you measure the error between a function F and a line L in an interval I around a point c? At each point x in I, i can measure the error as just the absolute value of the difference: |F(x) - L(x)|. But that's just the error at the point x. What's the error on the whole interval? The integral of |F - L| over the interval I?

Thanks.

Last edited by LRKM (2013-02-01 20:05:24)

I have never heard of doing that.

Me neither. I was just shooting ideas of what came to mind.

In Numerical Analysis we try to come up with the greatest error in some interval

But that's just one way of looking at the error, I guess.

What does that mean the "error of the whole interval?"

First of all I didnt say "the error OF the whole interval", I said "the error ON the whole interval". My idea was: I know what the error between F and L is at a single point. Then I wondered: How do I measure the error between F and L over the whole interval I? A plausible answer (although of course it may be wrong, hence why I'm asking here) was to take the error between F and L at every single point of the interval and add them up. (this is exactly the integral over I of |F - L|). This is then what I will call the error between F and L over the interval I. Then by looking at this integral, im assigning an "error" over the whole interval I to the line L.

I suspect we may have a misunderstanding. I'm not completely sure I have been able to explain conceptually what I'm trying to understand.

Last edited by LRKM (2013-02-01 20:18:36)

Yes. This is what I meant when I said "But that's just one way of looking at the error". You can "define" your error to be whatever you want (as long as it's logical). You can define the error to be the sum of the absolute values (like I first said) of the differences but then you run into non-differentiability problems because of the absolute value and hence the minimization-maximization tools of calculus cannot be applied, which is why one uses least squares so that it is differentiable.

But I see a problem in this, if i remember correctly you use least squares to do a best-fit to a data set of finite points. How do you overcome the fact that an interval has infinite points?

Last edited by LRKM (2013-02-01 20:40:49)

Good to learn, thanks!

Now, this looks like we are now getting into some heavy machinery for what seems to be a pretty basic and fundamental problem of showing that the derivative is the best linear approximation. There must be a simpler way of proving this fundamental fact. Anyways Taylor series etc depend on the derivative and its properties, namely that it is THE best linear approximation. We would be using circular logic if one were to prove this simple fact using such machinery.

Do you have any idea how to prove this using basic tools? Geometry or something? Im at a loss.

Thanks again for this conversation

Last edited by LRKM (2013-02-01 20:56:52)

We are talking about this

y - f(a) = f '(a)(x - a)?

Yes.

It is the best linear approximation around a

Why?

Obviously at an intuitive level this is clear, I'm interested in the proof of WHY it is the best linear approximation. Besides the fact that all Calculus books hammer this into your head a million times, there has to be a WHY; an explanation of why any other line provides a worst approximation.

I'm being meticulous on purpose.

Last edited by LRKM (2013-02-01 21:11:02)