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i have a non linear equation that depends on three variables e, theta and z.

i have done calculations to calculate e while varying theta and z. theta varied among the vector [0, Pi/4, Pi/3, Pi/2] and z was varying between 1 and 20

when plotting my data it gives the following plot where z is represented on the x-axis and each curve correspond to one theta

 

i am currently able of fitting one plot to one equation i would like to fit the data points using the nonlinearfit function and to only get one equation for all the plots. is that possible in maple or not

 

Ellipsoid Surface Fitting...

April 30 2014 Chia 95

Hi everyone,

I have a question about surface fitting. I tried to follow the step of application "Fitting an Ellipse to Data" to fit the ellipsoid surface but got the incorrect result:

 

 

It seems that the convergence condition can be modified but I have no idea. 

The related Maple file is attached:

Ellipsoid_Surface_Fitting.mw

I'd appreciate any help on this topic. Thank a lot.

Hello everyone!

I have a question that I can't seem to find a straight answer to. I need to fit a circle to a collection of points that a circular in nature. I was trying to use the following elliptical least squares fit, but I can't determine what I should be minimizing.

Here's the page:

http://www.maplesoft.com/applications/view.aspx?SID=1395&view=html

 

For an ellipse, I used the general conic:

F:=a*x^2+b*x*y+c*y^2+d*x+e*y+f

I minimize using:

V:=Minimize(E,{4*a*c-b^2=1});

 

What would I use for a circle? Or is there a better way for a circle?

Hi

 

I have some data:

Matrix(10, 2, {(1, 1) = 0, (1, 2) = 0, (2, 1) = .5, (2, 2) = 3.25, (3, 1) = 1.0, (3, 2) = 5.82, (4, 1) = 1.5, (4, 2) = 7.50, (5, 1) = 2.0, (5, 2) = 8.79, (6, 1) = 2.5, (6, 2) = 9.83, (7, 1) = 3.0, (7, 2) = 10.66, (8, 1) = 3.5, (8, 2) = 11.35, (9, 1) = 4.0, (9, 2) = 11.94, (10, 1) = 4.5, (10, 2) = 12.46})

 

I want Maple to make a trendline fitting a Logarithmic function. I can make it output some function with this:

LeastSquares(`<,>`(.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5), `<,>`(3.25, 5.82, 7.50, 8.79, 9.83, 10.66, 11.35, 11.94, 12.46), x, curve = a+b*ln(x))

It outputs:

5.96497783539274+4.25309474196387*ln(x)

 

But please notice, the dataset in the function does not have the first 0 and 0. If i do that:

LeastSquares(`<,>`(0, .5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5), `<,>`(0, 3.25, 5.82, 7.50, 8.79, 9.83, 10.66, 11.35, 11.94, 12.46), x, curve = a+b*ln(x))

It outputs: 

Error, (in Matrix) numeric exception: division by zero

 

Besides that, i need the R-squard value for determinating how well it fits.

 

If i do the same thing i Excel the data set will give a formular: 5.5464ln(x)-0.2175 with a R-sward value of 0.9985.

 

How can i do this i maple?

 

Thanks in Advance!

 

----

Emil Kristensen

Hi there!

I wrote a piece of code which spits out the numerical datapoints (x,y(x)) corresponding to a function y(x). So that the result is accurate, I need quite a lot of data points - currently I am working with 5k.

In order to work with this function later, I interpolated it with a Spline. For instance, I would like to sample the function values on a fifferent grid, etc.. However the evaluation of this function really takes up hell of a lot of time, and the reason seems to be, that it, being a spline on 5k nodes, is simply a huge expression.

Is there a better way to do this? Are other fitting functions than a spline maybe better suited?

Thanks for help!

 

Hi everyone,

I have a very complicated function y with only one independent variable x, and want to fit or approximate it by a simpler function, say polynomial. Many books or maple reference seem to tell how to fit a set of data instead of a given function. But the argument x in the function is assumed to be continuous other than discrete, so I don't know whether it is possible to express datax in form of x's range such as 0..1, and express datay in form of the function. After that , maybe I can fit the two created data sets by a polynomial function.

Or, does anyone have a better or more direct way to do the fitting linking two fucntions?

I am appreciated for your help.

Best,

GOODLUCK

Hello every one,

restart;with(stats):

with(stats[statplots]):
with(plots):

x1_values:=[0.1, 0.20, 0.30, 0.40, 0.50, 0.60, 0.70, 0.80];

x2_values:=[1, 2, 3, 4, 5, 6, 7, 8];

x3_values:=[11, 12, 13, 14, 15, 16, 17, 18];

x4_values:=[10, 20, 30, 40, 50, 60, 70, 80];

y_values:=[30, 40, 60, 70, 90, 120, 150, 200];

How to fit the above data into the following equation

y=a+b*x1+c*x2+d*x3+e*x4+f*x1^2+g*x2^2+h*x3^2+i*x4^2+j*x1*x2+k*x1*x3+l*x1*x4

+m*x2*x3+n*x2*x4+p*x3*x4;

Thanks

 

 

 

I have to make a plot for experimental data where x corresponds to time and y is the concentration. So I loaded the values in two lists, ran fit to least squares and got the best fitting curve (it's linear). However, the line has to start at (0,0). How can I Maple to do that for me?

Hey 

I'm trying to fit a spacecurve onto some points I have. 

I know, I can use Spline to do this in 2D, but it doesn't work in 3D.

What command(s) can I use to fit a spacecurve onto my points? 

Thanks in advance

How does one find a formula that  fits a given set of data  to the best advantage in Maple? 

 

e.g

a := [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10];

b := [ 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.001, 0.011, 0.011, 0.011, 0.011, 0.011, 0.011, 0.011, 0.011, 0.011, 0.011];

c := [ -0.88, -8.87, -0.86, -0.82, -0.77, -0.71, -0.66, -0.62, -0.57, -0.54, -0.89, -0.88, -0.85, -0.81,...

Hello,

 

I have two sets of data representing two function that depends on x with a parameter A.

I need to do a fit on both data series at the same time so to fit with the best parameter A.

 

Here is how I do a fit on one function

 

> f(x):=A*cos(x-B)^(2);               
> g(x):=A*(cos(x-C)^(2)+ sin(x-C)^(2))^2; 
> fit1 := Fit(f(x), r, x, parameternames = [A, B, C...

MAPLE Users,

Suppose I have a set of points in some N-dimensional space.  I would like to obtain a simple polynomial that

best fits the data.  I do not know in advance the form of the function, but a simple function that does not overfit

the data would probably be OK.  By "simple" I mean the smallest degree with or without cross-terms that gives a

decent fit.  My data set will typically be an external comma or tab separated text file.

Too long evaluation...

December 03 2012 Combs 0

Hi, I'm working on fitting model. For some reason is taking way too long to evaluate it actually hasn't finished it at all. Im running on i7. Thanks for any help. this is my code: 

> X := Vector([0, 170, 0]);
              
> Y := Vector([0, 11, 56]);
               

Q(t) := -(36788*76)*(.28*K*p/(-p-exp(-r*t...

 What justifies the t-1; to achieve this result?

restart;
interface(warnlevel = 0, imaginaryunit = I, rtablesize = 12);
with(plots); with(plottools);
alias(FFT = DiscreteTransforms[FourierTransform], IFFT = DiscreteTransforms[InverseFourierTransform]);

Temp := [24.2, 28.4, 32.7, 39.7, 47.0, 53.0, 56.0, 55.0, 49.4, 42.2, 32.0, 27.1];
POI := seq([n, Temp[n]], n = 1 .. 12);

 p1 := pointplot([POI], labels = ["x~month", "y = Temperature~ºF"...

I am trying to fit some data to a model.

The model is given by this equation.

A*(C/B)^(2*B)*(B-1)^(2*(B-1))/(205*10^9*(2*B+1)*(sigma^2-(650*10^6)^2)*sigma^(2*(B-1)))+(C-sigma)^2*A/(205*10^9*(2*B+1)*(sigma^2-(650*10^6)^2))

> with(Statistics);
> X := Vector([819.4, 795.6, 788.8, 782.0, 776.56, 763.64, 748.28, 724.42, 717.40, 711.28, 707.20, 680], datatype = float);
> Y := Vector([5.3*10^4, 7.8*10^4, 9*10^4, 9.5*10^4, 10^5, 1.2*10^5, 1.37*10^5,...

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