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  2. Fisher transformation - Wikipedia

    en.wikipedia.org/wiki/Fisher_transformation

    The application of Fisher's transformation can be enhanced using a software calculator as shown in the figure. Assuming that the r-squared value found is 0.80, that there are 30 data [clarification needed], and accepting a 90% confidence interval, the r-squared value in another random sample from the same population may range from 0.656 to 0.888.

  3. Linear trend estimation - Wikipedia

    en.wikipedia.org/wiki/Linear_trend_estimation

    All have the same trend, but more filtering leads to higher r 2 of fitted trend line. The least-squares fitting process produces a value, r-squared (r 2), which is 1 minus the ratio of the variance of the residuals to the variance of the dependent variable. It says what fraction of the variance of the data is explained by the fitted trend line.

  4. Linear least squares - Wikipedia

    en.wikipedia.org/wiki/Linear_least_squares

    The OLS method minimizes the sum of squared residuals, and leads to a closed-form expression for the estimated value of the unknown parameter vector β: ^ = (), where is a vector whose ith element is the ith observation of the dependent variable, and is a matrix whose ij element is the ith observation of the jth independent variable.

  5. List of mathematical constants - Wikipedia

    en.wikipedia.org/wiki/List_of_mathematical_constants

    For a lattice L in Euclidean space R n with unit covolume, i.e. vol(R n /L) = 1, let λ 1 (L) denote the least length of a nonzero element of L. Then √γ n n is the maximum of λ 1 (L) over all such lattices L. 1822 to 1901 Hafner–Sarnak–McCurley constant [118]

  6. Simple linear regression - Wikipedia

    en.wikipedia.org/wiki/Simple_linear_regression

    It is common to make the additional stipulation that the ordinary least squares (OLS) method should be used: the accuracy of each predicted value is measured by its squared residual (vertical distance between the point of the data set and the fitted line), and the goal is to make the sum of these squared deviations as small as possible.

  7. Multidimensional scaling - Wikipedia

    en.wikipedia.org/wiki/Multidimensional_scaling

    An R-square of 0.6 is considered the minimum acceptable level. [citation needed] An R-square of 0.8 is considered good for metric scaling and .9 is considered good for non-metric scaling. Other possible tests are Kruskal’s Stress, split data tests, data stability tests (i.e., eliminating one brand), and test-retest reliability.

  8. R-value - Wikipedia

    en.wikipedia.org/wiki/R-value

    R-value (soils) in geotechnical engineering, the stability of soils and aggregates for pavement construction; R-factor (crystallography), a measure of the agreement between the crystallographic model and the diffraction data; R 0 or R number, the basic reproduction number in epidemiology; In computer science, a pure value which cannot be ...

  9. Estimation theory - Wikipedia

    en.wikipedia.org/wiki/Estimation_theory

    Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component. The parameters describe an underlying physical setting in such a way that their value affects the distribution of the measured data.