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  2. P–P plot - Wikipedia

    en.wikipedia.org/wiki/P–P_plot

    As an example, if the two distributions do not overlap, say F is below G, then the P–P plot will move from left to right along the bottom of the square – as z moves through the support of F, the cdf of F goes from 0 to 1, while the cdf of G stays at 0 – and then moves up the right side of the square – the cdf of F is now 1, as all points of F lie below all points of G, and now the cdf ...

  3. x̅ and R chart - Wikipedia

    en.wikipedia.org/wiki/X̅_and_R_chart

    As with the ¯ and s and individuals control charts, the ¯ chart is only valid if the within-sample variability is constant. [4] Thus, the R chart is examined before the x ¯ {\displaystyle {\bar {x}}} chart; if the R chart indicates the sample variability is in statistical control, then the x ¯ {\displaystyle {\bar {x}}} chart is examined to ...

  4. Normal probability plot - Wikipedia

    en.wikipedia.org/wiki/Normal_probability_plot

    Normal probability plots are made of raw data, residuals from model fits, and estimated parameters. A normal probability plot. In a normal probability plot (also called a "normal plot"), the sorted data are plotted vs. values selected to make the resulting image look close to a straight line if the data are approximately normally distributed.

  5. Probability plot correlation coefficient plot - Wikipedia

    en.wikipedia.org/wiki/Probability_plot...

    Vertical axis: Probability plot correlation coefficient; Horizontal axis: Value of shape parameter. That is, for a series of values of the shape parameter, the correlation coefficient is computed for the probability plot associated with a given value of the shape parameter. These correlation coefficients are plotted against their corresponding ...

  6. L-moment - Wikipedia

    en.wikipedia.org/wiki/L-moment

    Another advantage L-moments have over conventional moments is that their existence only requires the random variable to have finite mean, so the L-moments exist even if the higher conventional moments do not exist (for example, for Student's t distribution with low degrees of freedom). A finite variance is required in addition in order for the ...

  7. Weibull distribution - Wikipedia

    en.wikipedia.org/wiki/Weibull_distribution

    In probability theory and statistics, the Weibull distribution / ˈ w aɪ b ʊ l / is a continuous probability distribution.It models a broad range of random variables, largely in the nature of a time to failure or time between events.

  8. Pearson distribution - Wikipedia

    en.wikipedia.org/wiki/Pearson_distribution

    A Pearson density p is defined to be any valid solution to the differential equation (cf. Pearson 1895, p. 381) ′ () + + + + = ()with: =, = = +, =. According to Ord, [3] Pearson devised the underlying form of Equation (1) on the basis of, firstly, the formula for the derivative of the logarithm of the density function of the normal distribution (which gives a linear function) and, secondly ...

  9. Shape of a probability distribution - Wikipedia

    en.wikipedia.org/wiki/Shape_of_a_probability...

    In statistics, the concept of the shape of a probability distribution arises in questions of finding an appropriate distribution to use to model the statistical properties of a population, given a sample from that population.

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