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  2. Coefficient of determination - Wikipedia

    en.wikipedia.org/wiki/Coefficient_of_determination

    Ordinary least squares regression of Okun's law.Since the regression line does not miss any of the points by very much, the R 2 of the regression is relatively high.. In statistics, the coefficient of determination, denoted R 2 or r 2 and pronounced "R squared", is the proportion of the variation in the dependent variable that is predictable from the independent variable(s).

  3. Frobenius method - Wikipedia

    en.wikipedia.org/wiki/Frobenius_method

    The general definition of the indicial polynomial is the coefficient of the lowest power of z in the infinite series. In this case it happens to be that this is the rth coefficient but, it is possible for the lowest possible exponent to be r − 2, r − 1 or, something else depending on the given differential equation. This detail is important ...

  4. Bayes error rate - Wikipedia

    en.wikipedia.org/wiki/Bayes_error_rate

    where is the instance, [] the expectation value, is a class into which an instance is classified, (|) is the conditional probability of label for instance , and () is the 0–1 loss function: L ( x , y ) = 1 − δ x , y = { 0 if x = y 1 if x ≠ y {\displaystyle L(x,y)=1-\delta _{x,y}={\begin{cases}0&{\text{if }}x=y\\1&{\text{if }}x\neq y\end ...

  5. Ordinary least squares - Wikipedia

    en.wikipedia.org/wiki/Ordinary_least_squares

    In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model (with fixed level-one [clarification needed] effects of a linear function of a set of explanatory variables) by the principle of least squares: minimizing the sum of the squares of the differences between the observed dependent variable (values ...

  6. Least squares - Wikipedia

    en.wikipedia.org/wiki/Least_squares

    The result of fitting a set of data points with a quadratic function Conic fitting a set of points using least-squares approximation. In regression analysis, least squares is a parameter estimation method based on minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each ...

  7. Variance inflation factor - Wikipedia

    en.wikipedia.org/wiki/Variance_inflation_factor

    Then, calculate the VIF factor for ^ with the following formula : V I F i = 1 1 − R i 2 {\displaystyle \mathrm {VIF} _{i}={\frac {1}{1-R_{i}^{2}}}} where R 2 i is the coefficient of determination of the regression equation in step one, with X i {\displaystyle X_{i}} on the left hand side, and all other predictor variables (all the other X ...

  8. An expanding coalition of health and consumer advocates is campaigning against Robert F. Kennedy Jr.'s nomination to the top U.S. health job over concerns about his activism against vaccines and ...

  9. Root mean square deviation - Wikipedia

    en.wikipedia.org/wiki/Root_mean_square_deviation

    Normalizing the RMSD facilitates the comparison between datasets or models with different scales. Though there is no consistent means of normalization in the literature, common choices are the mean or the range (defined as the maximum value minus the minimum value) of the measured data: [4]

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