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Octave can create Quantile Plots (QQ-Plots), and Probability Plots (PP-Plots). These are simple graphical tests for determining if a data set comes from a certain distribution.
Note that Octave can also show histograms of data
using the hist
function as described in
Two-Dimensional Plots.
Perform a QQ-plot (quantile plot).
If F is the CDF of the distribution dist with parameters params and G its inverse, and x a sample vector of length n, the QQ-plot graphs ordinate s(i) = i-th largest element of x versus abscissa q(if) = G((i - 0.5)/n).
If the sample comes from F, except for a transformation of location and scale, the pairs will approximately follow a straight line.
If the second argument is a vector y the empirical CDF of y is used as dist.
The default for dist is the standard normal distribution. The optional argument params contains a list of parameters of dist. For example, for a quantile plot of the uniform distribution on [2,4] and x, use
qqplot (x, "unif", 2, 4)
dist can be any string for which a function distinv or dist_inv exists that calculates the inverse CDF of distribution dist.
If no output arguments are given, the data are plotted directly.
Perform a PP-plot (probability plot).
If F is the CDF of the distribution dist with parameters params and x a sample vector of length n, the PP-plot graphs ordinate y(i) = F (i-th largest element of x) versus abscissa p(i) = (i - 0.5)/n. If the sample comes from F, the pairs will approximately follow a straight line.
The default for dist is the standard normal distribution.
The optional argument params contains a list of parameters of dist.
For example, for a probability plot of the uniform distribution on [2,4] and x, use
ppplot (x, "uniform", 2, 4)
dist can be any string for which a function dist_cdf that calculates the CDF of distribution dist exists.
If no output is requested then the data are plotted immediately.
Next: Correlation and Regression Analysis, Previous: Basic Statistical Functions, Up: Statistics [Contents][Index]