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 , ∑x∆y , ∑y∆x)  list:

(x,<y>) list:

(x , y)  list:

(x , y , ∆x , ∆y)  list:

(x,<y>,∆<y>)  list:

Fit Coefficients: (x0,a,b,c,d [y=a+b∆x+c∆x^2+d∆x^3],∆x= x-x0)

Unweighted spline fit: 

Fit Coefficients: 

Point by Point Quadratic Bezier Interpolation: 

Fit Coefficients: (x0,a,b[y=a+(b ∆x),∆x= x-x0])

Point by Point Linear Interpolation: 

Reduced Chi^2: 

) = 

a(

Harmonic Series + Log: y = ∑(0,n-1)a(i)/x^i +a(n)ln(x)

Harmonic Series: y = ∑a(i)/x^i

Normalised Chebychev Polynomial fit: y = ∑a(i)T(x,i) -1<x<1

Legendre Polynomial fit: y =  ∑a(i)P(x,i)

Polynomial fit: y = ∑a(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