About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 68. Chapters: Standard deviation, Variance, Algorithms for calculating variance, Kurtosis, Root mean square, Skewness, Central moment, Range, Semivariance, Estimation of covariance matrices, Margin of error, Mean squared error, Heteroscedasticity, Engineering tolerance, Standard error, Bollinger Bands, Negentropy, Bessel's correction, Mean difference, Variogram, Errors and residuals in statistics, Goodness of fit, Studentized residual, Goldfeld-Quandt test, Minimum mean square error, Bhattacharyya distance, Squared deviations, Absolute deviation, Coefficient of variation, Index of dispersion, Cosmic variance, Statistical dispersion, Root mean square deviation, Median absolute deviation, Qualitative variation, Robust measures of scale, Law of total variance, Rescaled range, Tracking signal, Mean square weighted deviation, Sampling variogram, Homoscedasticity, Computational formula for the variance, Mean squared prediction error, Mean absolute scaled error, Deviance, Symmetric mean absolute percentage error, Standardized moment, Mean absolute error, Precision, Real aggregated percentage error, Fano factor, Variation ratio, Conditional variance, Antenna tracking system, Relative standard deviation, Quartile coefficient of dispersion, Berkson error model, Root mean square fluctuation, Deviation analysis, Statistical noise, Mean square quantization error. Excerpt: Standard deviation is a widely used measurement of variability or diversity used in statistics and probability theory. It shows how much variation or "dispersion" there is from the average (mean, or expected value). A low standard deviation indicates that the data points tend to be very close to the mean, whereas high standard deviation indicates that the data are spread out over a large range of values. Technically, the standard deviation of a statistical population, data s...
