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Excel graph of the difference between two evaluations of the smallest root of a quadratic: direct evaluation using the quadratic formula (accurate at smaller b) and an approximation for widely spaced roots (accurate for larger b). The difference reaches a minimum at the large dots, and round-off causes squiggles in the curves beyond this minimum.
A scale factor of 1 ⁄ 10 cannot be used here, because scaling 160 by 1 ⁄ 10 gives 16, which is greater than the greatest value that can be stored in this fixed-point format. However, 1 ⁄ 11 will work as a scale factor, because the maximum scaled value, 160 ⁄ 11 = 14. 54 , fits within this range.
A scale factor is usually a decimal which scales, or multiplies, some quantity. In the equation y = Cx, C is the scale factor for x. C is also the coefficient of x, and may be called the constant of proportionality of y to x. For example, doubling distances corresponds to a scale factor of two for distance, while cutting a cake in half results ...
Robust measures of scale can be used as estimators of properties of the population, either for parameter estimation or as estimators of their own expected value.. For example, robust estimators of scale are used to estimate the population standard deviation, generally by multiplying by a scale factor to make it an unbiased consistent estimator; see scale parameter: estimation.
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The scattered points are the input scores of observations and the arrows show the contribution of each feature to the input loading vectors. Spectramap biplot of Anderson's iris data set Discriminant analysis biplot of Fisher's iris data. Biplots are a type of exploratory graph used in statistics, a generalization of the simple two-variable ...
Since each column of the basic design has 50% 0s and 25% each +1s and −1s, multiplying each column, j, by σ(X j)·2 1/2 and adding μ(X j) prior to experimentation, under a general linear model hypothesis, produces a "sample" of output Y with correct first and second moments of Y.
High-leverage points, if any, are outliers with respect to the independent variables. That is, high-leverage points have no neighboring points in R p {\displaystyle \mathbb {R} ^{p}} space, where p {\displaystyle {p}} is the number of independent variables in a regression model.