Solve 3rd Degree Taylor Polynomial for \sqrt[ ]{9.03}

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Homework Help Overview

The original poster attempts to find an approximated value for \(\sqrt{9.03}\) using a third-degree Taylor polynomial centered at \(x_0=9\). The discussion involves evaluating the polynomial and estimating the error associated with the approximation.

Discussion Character

  • Exploratory, Assumption checking, Mathematical reasoning

Approaches and Questions Raised

  • Participants discuss the evaluation of the Taylor polynomial and the correct value of \(9.03 - 9\). There are inquiries about how to estimate the error in the approximation, with references to formulas for the remainder in Taylor series.

Discussion Status

Participants have provided guidance on the error estimation and discussed the nature of the remainder in Taylor series. There is an ongoing exploration of the correct interpretation of the remainder formula, with some participants questioning whether it should be treated as an inequality.

Contextual Notes

There are mentions of specific values and calculations that may influence the understanding of the error estimation, as well as the complexity of determining the exact point \(c\) in the remainder formula.

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Homework Statement


Find an approximated value for [tex]\sqrt[ ]{9.03}[/tex] using a Taylors polynomial of third degree and estimate the error.

Homework Equations




The Attempt at a Solution


I thought of solving it by using

[tex]f(x)=\sqrt[]{x}[/tex] centered at [tex]x_0=9[/tex]

So

[tex]P_n(x)=3+\dysplaystyle\frac{(x-9)}{6}-\dysplaystyle\frac{(x-9)^2}{216}+\dysplaystyle\frac{3(x-9)^3}{3888}[/tex]

Then I evaluated it at x=9.03, so I get:

[tex]P_n(x)=3+\dysplaystyle\frac{(0.3)}{6}-\dysplaystyle\frac{(0.3)^2}{216}+\dysplaystyle\frac{3(0.3)^3}{3888}\approx{3.049604167}[/tex]

I don't know if this is right, I've tried with the calculator and it gives 3.00500... Now, how do I estimate the error? just by resting to the value the calculator gives the one I get?
 
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9.03-9 = 0.03, not 0.3. :)
 
****, thanks haha
 
For the error estimate, there's a formula for the error in approximating a Taylor series by the first n terms (terms up through degree n). If my memory is correct, it looks something like this.

[tex]R_n(x)= \frac{f^{n + 1}(c)}{(n + 1)!}(x - a)^{n + 1}[/tex]

Since it is usually impossible to determine c (which is between x and a), in practice, one finds an upper bound on |f(n+1)(x)|.
 
Thank you Mark. I think I get it.

Here it is:

[tex]R_{n+1}(x)=\displaystyle\frac{-15(x-9)^4}{4!16\sqrt[ ]{\alpha}}[/tex]

[tex]\alpha=9+0.03\theta[/tex], [tex]\theta\in{(0,1)}[/tex]
 
Last edited:
Mark44 said:
For the error estimate, there's a formula for the error in approximating a Taylor series by the first n terms (terms up through degree n). If my memory is correct, it looks something like this.

[tex]R_n(x)= \frac{f^{n + 1}(c)}{(n + 1)!}(x - a)^{n + 1}[/tex]

Since it is usually impossible to determine c (which is between x and a), in practice, one finds an upper bound on |f(n+1)(x)|.

I could be wrong, but shouldn't this be an inequality? That's how I remember it.
 
No, it shouldn't be an inequality. Checking with a calculus text, the formula for the remainder is correct.
 
It can be thought as inequality like this:

[tex]|R_n(x)|= |\frac{f^{n + 1}(c)}{(n + 1)!}(x - a)^{n + 1}|\leq{}\displaystyle\frac{(x-a)^{n+1}}{(n+1)!} k[/tex]
Where [tex]k=max{f(b) \textsf{ such that b} \in{[x,a]}[/tex]
 
Last edited:
The remainder is an equality. Usually it suffices to get an upper bound on that derivative - then you have an inequality.
 
  • #10
jegues said:
I could be wrong, but shouldn't this be an inequality? That's how I remember it.

The various forms of the Taylor remainder are generalizations of the mean value theorem to higher derivatives and higher degree polynomials -- the remainder is exactly equal to the given expression for some point c in a known interval.

Typical practical applications find the maximum over the entire interval, and then use that as an upper bound on the true remainder.
 
  • #11
Hurkyl said:
The various forms of the Taylor remainder are generalizations of the mean value theorem to higher derivatives and higher degree polynomials -- the remainder is exactly equal to the given expression for some point c in a known interval.

Typical practical applications find the maximum over the entire interval, and then use that as an upper bound on the true remainder.

Thank you both, Mark and Hurkyl for clearing things up!
 

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