41. Poisson Distribution.
Poisson distribution is called the distribution have the experiment interval, for example minutes, hours, days, years etc.
The probability distribution of Poisson random variable m representing the number of outcomes occurring in a given time interval or specified region denoted by t is equal:
where
λ is the average number of outcomes,
m=1,2,3…,
np→λ,
for n→
The sum of Poisson distribution is equal:
The mean of the Poisson distribution:
-
μ = λ
the variance:
-
σ2 = λ
And the standart deviation:
-
σ =
42. Continuous Uniform Distribution. Normal Distribution.
In probability theory and statistics, the continuous uniform distribution or rectangular distribution is a family of probability distributions such that for each member of the family, all intervals of the same length on the distribution's support are equally probable. The support is defined by the two parameters, a and b, which are its minimum and maximum values. The distribution is often abbreviated U(a,b). It is the maximum entropy probability distribution for a random variate X under no constraint other than that it is contained in the distribution's support.
The probability density function of the continuous uniform distribution is:
The values of f(x) at the two boundaries a and b are usually unimportant because they do not alter the values of the integrals of f(x) dx over any interval, nor of x f(x) dx or any higher moment.
The cumulative distribution function is:
The mean of the continuous uniform distribution:
the median:
the mode:
any value
in 
and the variance:
Normal Distribution.
In probability theory, the normal (or Gaussian) distribution is a continuous probability distribution, defined on the entire real line, that has a bell-shaped probability density function, known as the Gaussian function or informally as the bell curve:
The parameter μ is the mean or expectation (location of the peak) and σ 2 is the variance. σ is known as the standard deviation. The distribution with μ = 0 and σ 2 = 1 is called the standard normal distribution or the unit normal distribution. A normal distribution is often used as a first approximation to describe real-valued random variables that cluster around a single mean value.
μ,σ is depending on random variable x