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Hi Everyone

Is there a built-in way to fit a regression using generalized least squares in Maple ?


> with(Statistics);
> X := Vector([1, 2, 3, 4, 5, 6, 7], datatype = float);
> Y := Vector([155625, 172472, 179589, 186579, 205421, 214989, 237937], datatype = float);
> ExponentialFit(X, Y, x, output = residualsumofsquares);
Result by Maple: 0.00216641200893470318
But a real result is 8.159611742*10^7

Please, can anybody help me? Thank you.


If there is a regression in an update it should be fixed within the same version and not left as an open bug in current versions.

Since 16.01 update is no longer available and 16.02 is the only option.  It would be very much appreciated by the mapleprimes community and maple users to see a 16.03 update.


I know all the necessary computational steps to create a Linear Regression line, but I am having trouble making it into a succient procedure. I have to make a 3 procedures for three methods, minimizing Vertical distance, Horizontal, and lastly, Diagonal. 

I uploaded an example of my work to compute a linear regression line, minizming vertical distance. I have all the necessary steps for horizontal and diaganol as well. 




I'm trying to fit some of my research data to a polynomial.   I can do this, but what I need to know is how Maple is calculating the standard errors.  In other words. I need to know the underlying forumla Maple is using for these standard errors.

Is there any documentation on this anywhere?  


For reference, the Maple command I am using is:

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

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 was introduced to the geometric interpretation of correlation and linear regression recently.

Orignially due to the famous statistician R.A.Fisher, the idea is that the correlation between 
two variables is the cosine of the angle between the 2 vectors in n-dimensional space.
This can be demonstrated in Maple as follows:

First, we represent each variable as a vector and transform it so that it is centred at its
mean and has a length equal...

I have a huge .txt file in it is a lot of difirent numbers. At first I am doing this:

>data:=readdata(C:/text.txt, 1, integer)

Maple reads the file.
I  have a function p(x)=e^(x^4+0.5*a*x^2+b*x)
I tried to draw a histogram and then to do something with fitting, I know that then I have to do logarithm the function then I get ln(p(x))=x^4+0.5*a*x^2+b*x, the histogram then should be up side down. The point is that...

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