This example computes three core statistics from an array (a), namely the average (AV), standard deviation (SD), and coefficient of variation (CV). Results are derived step by step and returned in array (r). Implemented in Python, MATLAB, and JavaScript.

Calculate the variance of an array using a two-pass algorithm.

Calculate the standard deviation of an array using Welford's algorithm.

Calculate the mean and variance of a double-precision floating-point strided array.

Calculate the mean and variance of a double-precision floating-point strided array using a two-pass algorithm.

Calculate the variance of a double-precision floating-point strided array using a one-pass algorithm proposed by Youngs and Cramer.

Calculate the standard deviation of a strided array.

Calculate the variance of a strided array ignoring NaN values and using a two-pass algorithm.

Calculate the variance of an array ignoring `NaN` values and using a one-pass textbook algorithm.

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Calculate the variance of a single-precision floating-point strided array using a two-pass algorithm.

Calculate the standard deviation of a double-precision floating-point strided array using Welford's algorithm.

Calculate the standard deviation of a double-precision floating-point strided array using a two-pass algorithm.

Calculate the variance of a single-precision floating-point strided array using a one-pass textbook algorithm.

Calculate the variance of a double-precision floating-point strided array ignoring NaN values and using a two-pass algorithm.

Calculate the standard deviation of a strided array ignoring NaN values and using Welford's algorithm.

Calculate the standard deviation of an array ignoring `NaN` values and using a two-pass algorithm.

Calculate the variance of an array using Welford's algorithm.

Calculate the standard deviation of an array ignoring `NaN` values and using a one-pass textbook algorithm.

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