Normal Distribution
Author: John P. Burke

A normális eloszlás sűrűségfüggvénye

x_0

F(x_0)=P(x<x_0)

m = -0.5 (slider from -5 to 5, step 0.1)
σ = 2 (slider from 0.5 to 5, step 0.1)
szöveg2: "f(x)= \frac{ 1 }{ σ \sqrt{ 2π } } \cdot e^{- \frac{(x-m)²  }{2σ² }}= \frac{ 1 }{ " + σ + " \sqrt{ 2π } } \cdot e^{- \frac{(x-" + m + ")²  }{2" + σ + "² }}"
f(x) = exp(-1.0 * (x - m)^(2.0) / (2.0 * σ^(2.0))) / (sqrt(2.0 * 3.14) * σ)
P = Point(xAxis)
Integrál = Integral(f, -100.0, x(P))
Fx_0 = Normal(m, σ, x(P))
szöveg4: "F(" + (x(P)) + ")=P(x<" + (x(P)) + ")=" + Fx_0