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STR#_135.txt
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1995-04-19
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f =
e =
d =
Two Gaussian fit: y = a exp(-((x-b)/c)^2) + d exp(-((x-e)/f)^2)
Gaussian fit: y = a exp(-((x-b)/c)^2) + d + e x + f x^2
Gaussian fit: y = a exp(-((x-b)/c)^2) + d + e x
Gaussian fit: y = a exp(-((x-b)/c)^2) + d
Gaussian fit: y = a exp(-((x-b)/c)^2)
(x , y , 竏匝竏y , 竏惣竏x) list:
(x,<y>) list:
(x , y) list:
(x , y , 竏x , 竏y) list:
(x,<y>,竏<y>) list:
Fit Coefficients: (x0,a,b,c,d [y=a+b竏x+c竏x^2+d竏x^3],竏x= x-x0)
Unweighted spline fit:
Fit Coefficients:
Point by Point Quadratic Bezier Interpolation:
Fit Coefficients: (x0,a,b[y=a+(b 竏x),竏x= x-x0])
Point by Point Linear Interpolation:
Reduced Chi^2:
) =
a(
Harmonic Series + Log: y = 竏(0,n-1)a(i)/x^i +a(n)ln(x)
Harmonic Series: y = 竏疎(i)/x^i
Normalised Chebychev Polynomial fit: y = 竏疎(i)T(x,i) -1<x<1
Legendre Polynomial fit: y = 竏疎(i)P(x,i)
Polynomial fit: y = 竏疎(i)x^i
c =
b =
a =
Quadratic fit: y = a + bx + cx^2
Random correlation probability :
Linear correlation coeff :
b =
a =
Linear fit: y = a + bx
Poissonian weighted
Gaussian weighted
Experimental weighted
Unweighted