Statistics: wblcdf

### Function Reference: `wblcdf`

statistics: p = wblcdf (x)
statistics: p = wblcdf (x, lambda)
statistics: p = wblcdf (x, lambda, k)
statistics: p = wblcdf (…, `"upper"`)
statistics: [p, plo, pup] = wblcdf (x, lambda, k, pcov)
statistics: [p, plo, pup] = wblcdf (x, lambda, k, pcov, alpha)
statistics: [p, plo, pup] = wblcdf (…, `"upper"`)

Weibull cumulative distribution function (CDF).

For each element of x, compute the cumulative distribution function (CDF) of the Weibull distribution with scale parameter lambda and shape parameter k. The size of p is the common size of x, lambda and k. A scalar input functions as a constant matrix of the same size as the other inputs.

Default values are lambda = 1, k = 1.

When called with three output arguments, `[p, plo, pup]` it computes the confidence bounds for p when the input parameters lambda and k are estimates. In such case, pcov, a 2-by-2 matrix containing the covariance matrix of the estimated parameters, is necessary. Optionally, alpha has a default value of 0.05, and specifies 100 * (1 - alpha)% confidence bounds. plo and pup are arrays of the same size as p containing the lower and upper confidence bounds.

`[…] = wblcdf (…, "upper")` computes the upper tail probability of the lognormal distribution.

Further information about the Weibull distribution can be found at https://en.wikipedia.org/wiki/Weibull_distribution

See also: wblinv, wblpdf, wblrnd, wblstat, wblplot

Source Code: wblcdf

### Example: 1

     ## Plot various CDFs from the Weibull distribution
     x = 0:0.001:2.5;
     p1 = wblcdf (x, 1, 0.5);
     p2 = wblcdf (x, 1, 1);
     p3 = wblcdf (x, 1, 1.5);
     p4 = wblcdf (x, 1, 5);
     plot (x, p1, "-b", x, p2, "-r", x, p3, "-m", x, p4, "-g")
     grid on
     legend ({"λ = 1, k = 0.5", "λ = 1, k = 1", ...
              "λ = 1, k = 1.5", "λ = 1, k = 5"}, "location", "southeast")
     title ("Weibull CDF")
     xlabel ("values in x")
     ylabel ("probability")