Vielfache

$\mu$

\mu

\text{Binomialverteilung:}

r = 1 (slider from 1 to 3, step 1)
p = 0.5 (slider from 0 to 1, step 0.01)
n = 10 (slider from 2 to 500, step 1)
EX: (n * p)
SX: sqrt(((n * p) * (1 - p)))
d: EX + (r * SX)
d1: floor(d)
c: EX - (r * SX)
c1: ceil(c)
Verteilung = BinomialDist(n, p)
O = 0.5 (slider from -5 to 5)
U = -0.05 (slider from -5 to 5)
xPunkte = Sequence((i, 0), i, 0, n)
a: EX - SX
bink = BinomialDist(n, p, {a})
b: EX + SX
xHistogramm = Sequence(i, i, a, b)
yHistogramm = Sequence(((nCr[n, i] * p^(i)) * (1 - p)^(n - i)), i, a, b)
HistogrammIntervall = BarChart(xHistogramm, yHistogramm)
xPunkteIntervall = Sequence((i, 0), i, a, b)
VerteilungIntervall = Zip(Polygon[{P + (0.5, 0), P + (0.5, h), P + (-0.5, h), P + (-0.5, 0)}], P, xPunkteIntervall, h, yHistogramm)
v: (-0.2, -0.03)
k = 1 (slider from -5 to 5)
binab = BinomialDist(n, p, Sequence[i, i, c1, d1])
xWerte = Sequence(i, i, 0, n)
yWerte = Sequence(((nCr[n, i] * p^(i)) * (1 - p)^(n - i)), i, 0, n)
P = 1 (slider from -5 to 5)
h = 1 (slider from -5 to 5)
werte = 1 (slider from -5 to 5)
VerteilungX = Zip(Polygon[{P + (0.5, 0), P + (0.5, h), P + (-0.5, h), P + (-0.5, 0)}], P, xPunkte, h, yWerte)
m: x = EX
Text5: "\text{Standardabweichung:} \\
\underbrace{\sigma(X)}_{\sigma} = \sqrt{n \cdot p \cdot (1-p)} = " + (LaTeX[SX]) + ""
Text4: "\text{Erwartungswert:} \\
\underbrace{E(X)}_{\mu} = n \cdot p = " + (LaTeX[EX]) + ""
ga: x = c
gb: x = d
yV = 0.45 (slider from -5 to 5)
A: (c, yV)
M: (EX, yV)
B: (d, yV)
u = Vector(A, M)
w = Vector(M, A)
e = Vector(M, B)
f = Vector(B, M)
kSX: (0, -0.01)
vB: (0, 0.04)
vA: (0, -0.04)
Text3: "\mu+" + (LaTeX[r]) + "\cdot\sigma"
Text1: "\mu-" + (LaTeX[r]) + "\cdot\sigma"
Text2: "
\text{Wahrscheinlichkeit:}\\P(\mu-" + (LaTeX[r]) + "\sigma\leq X  \leq \mu+" + (LaTeX[r]) + "\sigma) = " + (LaTeX[binab]) + ""
L: ((-n)) / 10
R: n + n / 10