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ProbabilityDistributionType
Lvl Type, Domain name and/or Mnemonic code Concept ID Mnemonic Print Name Definition/Description
1 L:  (B) 10756 B beta The beta-distribution is used for data that is bounded on both sides and may or may not be skewed (e.g., occurs when probabilities are estimated.) Two parameters ? and ? are available to adjust the curve. The mean ? and variance ?2 relate as follows: ?
1 L:  (E) 10752 E exponential Used for data that describes extinction. The exponential distribution is a special form of ?-distribution where ? = 1, hence, the relationship to mean ? and variance ?2 are ? = ? and ?2 = ?2.
1 L:  (F) 10755 F F Used to describe the quotient of two ?2 random variables. The F-distribution has two parameters ?1 and ?2, which are the numbers of degrees of freedom of the numerator and denominator variable respectively. The relationship to mean ? and variance ?2 are
1 L:  (G) 10751 G (gamma) The gamma-distribution used for data that is skewed and bounded to the right, i.e. where the maximum of the distribution curve is located near the origin. The ?-distribution has a two parameters ? and ?. The relationship to mean ? and variance ?2 is ? =
1 L:  (LN) 10750 LN log-normal The logarithmic normal distribution is used to transform skewed random variable X into a normally distributed random variable U = log X. The log-normal distribution can be specified with the properties mean ? and standard deviation ?. Note however that m
1 L:  (N) 10749 N normal (Gaussian) This is the well-known bell-shaped normal distribution. Because of the central limit theorem, the normal distribution is the distribution of choice for an unbounded random variable that is an outcome of a combination of many stochastic processes. Even f
1 L:  (T) 10754 T T Used to describe the quotient of a normal random variable and the square root of a ?2 random variable. The t-distribution has one parameter ?, the number of degrees of freedom. The relationship to mean ? and variance ?2 are: ? = 0 and ?2 = ? / (? ? 2)
1 L:  (U) 10748 U uniform The uniform distribution assigns a constant probability over the entire interval of possible outcomes, while all outcomes outside this interval are assumed to have zero probability. The width of this interval is 2 ? ?3. Thus, the uniform distribution as
1 L:  (X2) 10753 X2 chi square Used to describe the sum of squares of random variables which occurs when a variance is estimated (rather than presumed) from the sample. The only parameter of the ?2-distribution is ?, so called the number of degrees of freedom (which is the number of i

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