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| R2012a Documentation → Statistics Toolbox | |
Learn more about Statistics Toolbox |
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Y = cdf('name',X,A)
Y = cdf('name',X,A,B)
Y = cdf('name',X,A,B,C)
Y = cdf('name',X,A) computes the cumulative distribution function for the one-parameter family of distributions specified by name. A contains parameter values for the distribution. The cumulative distribution function is evaluated at the values in X and its values are returned in Y.
If X and A are arrays, they must be the same size. If X is a scalar, it is expanded to a constant matrix the same size as A. If A is a scalar, it is expanded to a constant matrix the same size as X.
Y is the common size of X and A after any necessary scalar expansion.
Y = cdf('name',X,A,B) computes the cumulative distribution function for two-parameter families of distributions, where parameter values are given in A and B.
If X, A, and B are arrays, they must be the same size. If X is a scalar, it is expanded to a constant matrix the same size as A and B. If either A or B are scalars, they are expanded to constant matrices the same size as X.
Y is the common size of X, A, and B after any necessary scalar expansion.
Y = cdf('name',X,A,B,C) computes the cumulative distribution function for three-parameter families of distributions, where parameter values are given in A, B, and C.
If X, A, B, and C are arrays, they must be the same size. If X is a scalar, it is expanded to a constant matrix the same size as A, B, and C. If any of A, B or C are scalars, they are expanded to constant matrices the same size as X.
Y is the common size of X, A, B, and C after any necessary scalar expansion.
Acceptable strings for name (specified in single quotes) are:
| name | Distribution | Input Parameter A | Input Parameter B | Input Parameter C |
|---|---|---|---|---|
| beta or Beta | Beta Distribution | a | b | — |
| bino or Binomial | Binomial Distribution | n: number of trials | p: probability of success for each trial | — |
| chi2 or Chisquare | Chi-Square Distribution | ν: degrees of freedom | — | — |
| exp or Exponential | Exponential Distribution | μ: mean | — | — |
| ev or Extreme Value | Extreme Value Distribution | μ: location parameter | σ: scale parameter | — |
| f or F | F Distribution | ν1: numerator degrees of freedom | ν2: denominator degrees of freedom | — |
| gam or Gamma | Gamma Distribution | a: shape parameter | b: scale parameter | — |
| gev or Generalized Extreme Value | Generalized Extreme Value Distribution | k: shape parameter | σ: scale parameter | μ: location parameter |
| gp or Generalized Pareto | Generalized Pareto Distribution | k: tail index (shape) parameter | σ: scale parameter | μ: threshold (location) parameter |
| geo or Geometric | Geometric Distribution | p: probability parameter | — | — |
| hyge or Hypergeometric | Hypergeometric Distribution | M: size of the population | K: number of items with the desired characteristic in the population | n: number of samples drawn |
| logn or Lognormal | Lognormal Distribution | μ | σ | — |
| nbin or Negative Binomial | Negative Binomial Distribution | r: number of successes | p: probability of success in a single trial | — |
| ncf or Noncentral F | Noncentral F Distribution | ν1: numerator degrees of freedom | ν2: denominator degrees of freedom | δ: noncentrality parameter |
| nct or Noncentral t | Noncentral t Distribution | ν: degrees of freedom | δ: noncentrality parameter | — |
| ncx2 or Noncentral Chi-square | Noncentral Chi-Square Distribution | ν: degrees of freedom | δ: noncentrality parameter | — |
| norm or Normal | Normal Distribution | μ: mean | σ: standard deviation | — |
| poiss or Poisson | Poisson Distribution | λ: mean | — | — |
| rayl or Rayleigh | Rayleigh Distribution | b: scale parameter | — | — |
| t or T | Student's t Distribution | ν: degrees of freedom | — | — |
| unif or Uniform | Uniform Distribution (Continuous) | a: lower endpoint (minimum) | b: upper endpoint (maximum) | — |
| unid or Discrete Uniform | Uniform Distribution (Discrete) | N: maximum observable value | — | — |
| wbl or Weibull | Weibull Distribution | a: scale parameter | b: shape parameter | — |
Compute the cdf of the normal distribution with mean 0 and standard deviation 1 at inputs –2, –1, 0, 1, 2:
p1 = cdf('Normal',-2:2,0,1)
p1 =
0.0228 0.1587 0.5000 0.8413 0.9772
The order of the parameters is the same as for normcdf.
Compute the cdfs of Poisson distributions with rate parameters 0, 1, ..., 4 at inputs 1, 2, ..., 5, respectively:
p2 = cdf('Poisson',0:4,1:5)
p2 =
0.3679 0.4060 0.4232 0.4335 0.4405The order of the parameters is the same as for poisscdf.
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