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Poisson
Cumulative
Normal
T
2
F
Functions and commands
Poisson probability mass function. Computes the probability
of k occurrences of an event during a future interval given
the mean of the occurrences of that event during that interval
in the past. For this function, k is a non-negative integer and
is a real number.
POISSON(m,k)
Example: Suppose that on average you get 20 emails a day.
What is the probability that tomorrow you will get 15?
POISSON(20,15)
Cumulative normal distribution function. Returns the lower-tail
probability of the normal probability density function for the
value x, given the mean, and standard deviation, of a
normal distribution. If only one argument is supplied, it is
taken as x, and the assumption is that =0 and =1.
(
NORMAL_CDF
Example:
NORMAL_CDF(0,1,2)
Cumulative student's t distribution function. Returns the lower-
tail probability of the student's t-probability density function at
x, given n degrees of freedom.
(n,x)
STUDENT_CDF
Example:
student_cdf(
2
Cumulative
distribution function. Returns the lower-tail
probability of the
x, given n degrees of freedom.
CHISQUARE_CDF(n,k)
Example:
CHISQUARE_CDF
Cumulative Fisher distribution function. Returns the lower-tail
probability of the Fisher probability density function for the
value x, given numerator n and denominator d degrees of
freedom.
FISHER_CDF(n,d,x)
Example:
returns
0.0516488535318.
,
,]x)
returns
0.97724986805.
returns
3,–3.2)
2
probability density function for the value
) returns
(
2,6.1
0.0246659214814.
0.952641075609.
,
295
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