Returns the probability of X <= z for a normal distribution.
PROBN(z, mean, std)
|
z |
- |
A real or series. The z value. |
|
mean |
- |
Optional. A real, the mean of the distribution. Defaults to 0.0. |
|
std |
- |
Optional. A real, the standard deviation of the distribution. Defaults to 1.0. |
A real or series, the probability that a value is less than or equal to the input z value for a normal distribution with the given mean and standard deviation.
For the input value z, returns the probability p where P(X <= z) = p.
probn(0)
returns 0.5, the probability that a value is less than or equal to 0.0 for a normal distribution with a mean of 0.0 and a standard deviation of 1.0.
In probabilistic terms, given the normal distribution N(0, 1), i.e. mean of 0, variance of 1:
P(X <= 0.0) = 0.5
probn(2, 1, 2) - probn(0, 1, 2)
returns 0.382925, the probability that a value is less than or equal to 2 and greater than or equal to 0.0 for a normal distribution with a mean of 1.0 and a standard deviation of 2.0
In probabilistic terms, given the normal distribution N(1, 4), i.e. mean of 1, variance of 4:
P(0.0 <= X <= 2.0) = 0.382925
1 – probn(.5)
returns 0.30853754, the probability that a value is greater than 0.5 for a normal distribution with a mean of 0.0 and a standard deviation of 1.0
probn(invprobn(.3))
returns 0.3, indicating that PROBN and INVPROBN are inverse functions.
probn(-3..0.01..3)
displays the normal cumulative distribution function from –3 to 3.
PROBN uses ERF to evaluate the area
under the normal distribution curve. Note that
See INVPROBN to calculate the inverse normal cumulative distribution function. PROBN and INVPROBN are inverse functions.
See PDFNORM to generate the normal density function.