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Algorithm For these numbers..


Michdd

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As often as you make these posts, you should find a regression calculator....

 

lol

 

 

Looks like quartic regression is the best fit.  (By about .01 on r^2 ;p)

 

f(x) = 25.19668159x^4 - 1982.251758x^3 + 51051.54035x^2 - 410889.2313x + 740630.6288

 

R^2 = .9994005246

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As often as you make these posts, you should find a regression calculator....

 

lol

 

 

Looks like quartic regression is the best fit.  (By about .01 on r^2 ;p)

 

f(x) = 25.19668159x^4 - 1982.251758x^3 + 51051.54035x^2 - 410889.2313x + 740630.6288

 

R^2 = .9994005246

I've actually been trying to figure out the same thing throughout all these posts. The other formula I got turns out it only worked for the first few. So I decided to get more numbers to make this more accurate..

 

How do I render the formula that works from that function you gave me?

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function points($level) {
  return 740630.6288 + $level * (410889.2313 + $level * (51051.54035 + $level *(1982.251758 + $level * 25.19668159)));
}

That doesn't work.. if I input 1 for example it outputs: 1204578.84889 It should be outputting 150..

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Where did this set come from Michdd?

 

 

If it's like an experience table from a game or something, there there is a good chance the original creator used an equation of some kind.  Obviously quartic doesn't fit your data when x is low, but it was the closest fit that my calculator could figure.  It could be some kind of weird function for all we know.

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I tried everything a TI-83 calculator can do....  (Yeah, I actually got out a calculator.  Messed up, eh?)

 

 

None of them fit better than quartic, and I would expect most of them to be inaccurate when x is small.

 

 

Just wondering...  What exactly is this data from?  A game?

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Well my PHP best fit routines give:

 

Linear:

    Y = -3356182.0813 + 527670.027346 * X

    Goodness of fit (R2) = 0.770107920425

Exponential

    Y = 988.824767691 * 1.22215715614^X

    Goodness of fit (R2) = 0.0133880717857

Logarithmic

    Y = -7352389.96876 + 6404324.35463 * log(X)

    Goodness of fit (R2) = 0.494025408661

Power

    Y = 55.7661392554 * X^3.08867545849

    Goodness of fit (R2) = 0.556663601401

2nd Order Polynomial

    Y = 2462965.17606 + -735312.020309 * X + 22975.8285934 * X^2

    Goodness of fit (R2) = 0.956647178838

3rd Order Polynomial

    Y = -1314023.33251 + 546488.096084 * X + -40019.3995475 * X^2 + 753.074489922 * X^3

    Goodness of fit (R2) = 0.990972453353

4th Order Polynomial

    Y = 740630.628802 + -410889.231328 * X + 51051.5403512 * X^2 + -1982.25175809 * X^3 + 25.1966815902 * X^4

    Goodness of fit (R2) = 0.999400524594

5th Order Polynomial

    Y = -54987.569198 + 54314.9019047 * X + -13486.4094351 * X^2 + 1156.42543951 * X^3 + -36.7871254933 * X^4 + 0.427917621793 * X^5

    Goodness of fit (R2) = 0.999999462817

6th Order Polynomial

    Y = 1949.25791698 + -2884.08436671 * X + 1417.54916073 * X^2 + -213.282939974 * X^3 + 16.3830775025 * X^4 + -0.479307843025 * X^5 + 0.00563166434849 * X^6

    Goodness of fit (R2) = 0.999999999856

 

Even the 6th Order polynomial isn't accurate for the low values though

 

1 => 285.349172354

2 => 392.17412227

3 => 1510.97083927

4 => 3169.92277

5 => 5136.77878991

6 => 7375.52805619

15 => 87373.7131515

33 => 1729985.46426

51 => 19807601.1832

56 => 37665272.9156

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