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  2. Cumulative frequency analysis - Wikipedia

    en.wikipedia.org/wiki/Cumulative_frequency_analysis

    Histogram derived from the adapted cumulative probability distribution Histogram and probability density function, derived from the cumulative probability distribution, for a logistic distribution. The observed data can be arranged in classes or groups with serial number k. Each group has a lower limit (L k) and an upper limit (U k).

  3. Scale parameter - Wikipedia

    en.wikipedia.org/wiki/Scale_parameter

    Animation showing the effects of a scale parameter on a probability distribution supported on the positive real line. Effect of a scale parameter over a mixture of two normal probability distributions. If the probability density exists for all values of the complete parameter set, then the density (as a function of the scale parameter only ...

  4. 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 ...

  5. Generalized additive model for location, scale and shape

    en.wikipedia.org/wiki/Generalized_additive_model...

    The first two population distribution parameters and are usually characterized as location and scale parameters, while the remaining parameter(s), if any, are characterized as shape parameters, e.g. skewness and kurtosis parameters, although the model may be applied more generally to the parameters of any population distribution with up to four ...

  6. Kernel density estimation - Wikipedia

    en.wikipedia.org/wiki/Kernel_density_estimation

    Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.

  7. Stochastic simulation - Wikipedia

    en.wikipedia.org/wiki/Stochastic_simulation

    A stochastic simulation is a simulation of a system that has variables that can change stochastically (randomly) with individual probabilities. [1]Realizations of these random variables are generated and inserted into a model of the system.

  8. Raised cosine distribution - Wikipedia

    en.wikipedia.org/wiki/Raised_cosine_distribution

    In probability theory and statistics, the raised cosine distribution is a continuous probability distribution supported on the interval [, +].The probability density function (PDF) is

  9. Tukey lambda distribution - Wikipedia

    en.wikipedia.org/wiki/Tukey_lambda_distribution

    The Tukey lambda distribution has a single shape parameter, λ, and as with other probability distributions, it can be transformed with a location parameter, μ, and a scale parameter, σ. Since the general form of probability distribution can be expressed in terms of the standard distribution, the subsequent formulas are given for the standard ...