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  2. Sum of squares - Wikipedia

    en.wikipedia.org/wiki/Sum_of_squares

    The squared Euclidean distance between two points, equal to the sum of squares of the differences between their coordinates; Heron's formula for the area of a triangle can be re-written as using the sums of squares of a triangle's sides (and the sums of the squares of squares) The British flag theorem for rectangles equates two sums of two ...

  3. Squared deviations from the mean - Wikipedia

    en.wikipedia.org/wiki/Squared_deviations_from...

    The sum of squared deviations needed to calculate sample variance (before deciding whether to divide by n or n − 1) is most easily calculated as = From the two derived expectations above the expected value of this sum is

  4. Residual sum of squares - Wikipedia

    en.wikipedia.org/wiki/Residual_sum_of_squares

    The general regression model with n observations and k explanators, the first of which is a constant unit vector whose coefficient is the regression intercept, is = + where y is an n × 1 vector of dependent variable observations, each column of the n × k matrix X is a vector of observations on one of the k explanators, is a k × 1 vector of true coefficients, and e is an n× 1 vector of the ...

  5. Partition of sums of squares - Wikipedia

    en.wikipedia.org/wiki/Partition_of_sums_of_squares

    If the sum of squares were not normalized, its value would always be larger for the sample of 100 people than for the sample of 20 people. To scale the sum of squares, we divide it by the degrees of freedom, i.e., calculate the sum of squares per degree of freedom, or variance. Standard deviation, in turn, is the square root of the variance.

  6. One-way analysis of variance - Wikipedia

    en.wikipedia.org/wiki/One-way_analysis_of_variance

    Step 3: Calculate the "between-group" sum of squared differences: ... Calculate the "within-group" sum of squares. Begin by centering the data in each group a 1 a 2 a 3;

  7. 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 ...

  8. Difference of two squares - Wikipedia

    en.wikipedia.org/wiki/Difference_of_two_squares

    The difference of two squares is used to find the linear factors of the sum of two squares, using complex number coefficients. For example, the complex roots of z 2 + 4 {\displaystyle z^{2}+4} can be found using difference of two squares:

  9. Mean squared error - Wikipedia

    en.wikipedia.org/wiki/Mean_squared_error

    The MSE either assesses the quality of a predictor (i.e., a function mapping arbitrary inputs to a sample of values of some random variable), or of an estimator (i.e., a mathematical function mapping a sample of data to an estimate of a parameter of the population from which the data is sampled).