Search results
Results from the WOW.Com Content Network
Software versioning is the process of assigning either unique version names or unique version numbers to unique states of computer software. Within a given version number category (e.g., major or minor), these numbers are generally assigned in increasing order and correspond to new developments in the software.
Algorithms for calculating variance play a major role in computational statistics.A key difficulty in the design of good algorithms for this problem is that formulas for the variance may involve sums of squares, which can lead to numerical instability as well as to arithmetic overflow when dealing with large values.
In spatial statistics the theoretical variogram, denoted (,), is a function describing the degree of spatial dependence of a spatial random field or stochastic process (). ...
In applied statistics, a variance-stabilizing transformation is a data transformation that is specifically chosen either to simplify considerations in graphical exploratory data analysis or to allow the application of simple regression-based or analysis of variance techniques. [1]
Principal component analysis (PCA) is a linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing.. The data is linearly transformed onto a new coordinate system such that the directions (principal components) capturing the largest variation in the data can be easily identified.
In statistics, the variance function is a smooth function that depicts the variance of a random quantity as a function of its mean.The variance function is a measure of heteroscedasticity and plays a large role in many settings of statistical modelling.
The semivariance is calculated in the same manner as the variance but only those observations that fall below the mean are included in the calculation: =: < It is also described as a specific measure in different fields of application. For skewed distributions, the semivariance can provide additional information that a variance does not.
Mathematically, ANCOVA decomposes the variance in the DV into variance explained by the CV(s), variance explained by the categorical IV, and residual variance. Intuitively, ANCOVA can be thought of as 'adjusting' the DV by the group means of the CV(s). [1] The ANCOVA model assumes a linear relationship between the response (DV) and covariate (CV):