Casio FX-CG10 Software User Guide - Page 263
Distribution Continuous, Distribution, Probability Density, Cumulative Distribution
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k Distribution (Continuous) Distribution Normal Distribution Probability Density p(x) = 1 2πσ e- (x - μμ)2 2σ 2 (σ > 0) Cumulative Distribution Student-t Distribution p(x) = Γ df + 1 2 Γ df 2 × 1 + x2 df - df+1 2 π × df χ2 Distribution df p(x) = 1 Γ df 2 × 1 2 2 × x df 2 -1 × - e x 2 (x м 0) F Distribution p(x) = Γ ndf + ddf 2 Γ ndf 2 × Γ ddf 2 ndf ndf ndf 2x2 -1 1 + ndf × - ndf + ddf x2 ddf ddf (x м 0) ∫ Upper p = p(x)dx Lower Distribution Inverse Cumulative Distribution Normal Distribution ∫ Upper p = p(x)dx -∞ tail = Left ∫ ∞ p = p(x)dx Lower tail = Right ∫ Upper p = p(x)dx Lower tail = Central Student-t Distribution χ2 Distribution ∫ ∞ p = p(x)dx Lower F Distribution 6-70
6-70
k
Distribution (Continuous)
Distribution
Probability Density
Cumulative Distribution
Normal
Distribution
πσ
2
p
(
x
) =
1
e
–
2
2
σ
(
x
–
μ
)
2
μ
(
> 0)
σ
p
=
p
(
x
)
dx
Upper
Lower
∫
Student-
t
Distribution
p
(
x
) =
×
Γ
Γ
×
df
π
–
df
+
1
2
2
df
2
df
+ 1
df
x
2
1 +
χ
2
Distribution
p
(
x
) =
×
(
x
²
0)
Γ
1
2
df
df
2
×
x
2
1
df
2
–
1
x
2
–
×
e
F
Distribution
ndf
2
x
ddf
ndf
ndf
2
–
1
ddf
ndf
×
x
1 +
ndf + ddf
2
p
(
x
) =
–
Γ
2
ndf
+
ddf
Γ
2
ndf
× Γ
2
ddf
(
x
²
0)
Distribution
Inverse Cumulative Distribution
Normal
Distribution
p
=
p
(
x
)
dx
Upper
–
∞
∫
p
=
p
(
x
)
dx
Lower
∞
∫
p
=
p
(
x
)
dx
Upper
Lower
∫
tail = Left
tail = Right
tail = Central
Student-
t
Distribution
p
=
p
(
x
)
dx
Lower
∞
∫
χ
2
Distribution
F
Distribution