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Chapter 7: Statistics Application
152
Inverse Student’s
t
Cumulative Distribution
.... [Inv. Distribution] - [Inverse
t
CD]
Calculates the lower bound value of a Student’s
t
cumulative probability distribution for specified
values.
χχ
2
Probability Density
.... [Distribution] - [
χ
2
PD]
Calculates the
χ
2
probability density for a specified value.
χ
2
Cumulative Distribution
.... [Distribution] - [
χ
2
CD]
Calculates the cumulative probability of a
χ
2
distribution between a lower
bound and an upper bound.
Inverse
χ
2
Cumulative Distribution
.... [Inv. Distribution] - [Inverse
χ
2
CD]
Calculates the lower bound value of a
χ
2
cumulative probability distribution for specified values.
F
Probability Density
.... [Distribution] - [F PD]
Calculates the
F
probability density for a specified value.
F
Cumulative Distribution
.... [Distribution] - [F CD]
Calculates the cumulative probability of an
F
distribution
between a lower bound and an upper bound.
Inverse
F
Cumulative Distribution
.... [Inv. Distribution] - [Inverse F CD]
Calculates the lower bound value of an
F
cumulative probability distribution for specified values.
Binomial Distribution Probability
.... [Distribution] - [Binomial PD]
Calculates the probability in a binomial distribution that success will
occur on a specified trial.
p
: probability of success (0
s
p
s
1)
n
: number of trials
Binomial Cumulative Distribution
.... [Distribution] - [Binomial CD]
Calculates the cumulative probability in a binomial distribution that success will occur on or before a specified
trial.
Inverse Binomial Cumulative Distribution
.... [Inv. Distribution] - [Inverse Binomial CD]
Calculates the minimum number of trials of a binomial cumulative probability distribution for
specified values.
Poisson Distribution Probability
.... [Distribution] - [Poisson PD]
Calculates the probability in a Poisson distribution that success will occur on a specified trial.
0712
To calculate Poisson probability for the data below and graph the result
Specified trial: 10 Mean: 6
(
x
= 0, 1, 2, ...)
*
: mean (0 <
*
)
∞
f
(
x
) =
Γ
1
2
df
df
2
x e
2
1
df
2
–1
x
2
–
p
=
Γ
1
2
df
df
2
x e dx
2
1
df
2
–1
x
2
–
a
b
∞
Γ
2
n
Γ
2
n
+
d
Γ
2
d
f
(
x
) =
n
2
x
d
n
n
2
–
1
d
n
.
x
1 +
n + d
2
–
-
×
×
∞
/
t
0
1