chi-square test). In the analysis of variance, one of the components into which the variance is partitioned may be a lack-of-fit sum of squares. In assessing...

a sum of squares due to lack of fit, or more tersely a lack-of-fit sum of squares, is one of the components of a partition of the sum of squares of residuals...

sum of squares (RSS), also known as the sum of squared residuals (SSR) or the sum of squared estimate of errors (SSE), is the sum of the squares of residuals...

In statistics, the explained sum of squares (ESS), alternatively known as the model sum of squares or sum of squares due to regression (SSR – not to be...

a lack-of-fit test is any of many tests of a null hypothesis that a proposed statistical model fits well. See: Goodness of fit Lack-of-fit sum of squares...

between-groups SSP matrices. Squared deviations from the mean Sum of squares (statistics) Lack-of-fit sum of squares Expected mean squares Everitt, B.S. (2002)...

squares due to lack of fit", see Lack-of-fit sum of squares For sums of squares relating to model predictions, see Explained sum of squares For sums of...

Innovation (signal processing) Lack-of-fit sum of squares Margin of error Mean absolute error Observational error Propagation of error Probable error Random...

so that a comparison can be made between the lack-of-fit sum of squares and the pure error sum of squares, under the assumption that model errors are homoscedastic...

partition of sums of squares is a concept that permeates much of inferential statistics and descriptive statistics. More properly, it is the partitioning of sums...

regression model fits the data well. See Lack-of-fit sum of squares. The hypothesis that a data set in a regression analysis follows the simpler of two proposed...

regression Curve fitting Empirical Bayes method Errors and residuals Lack-of-fit sum of squares Line fitting Linear classifier Linear equation Logistic regression...

Coefficient of determination Lack-of-fit sum of squares Reduced chi-squared Willis BH, Riley RD (2017). "Measuring the statistical validity of summary meta-analysis...

Numerical methods for linear least squares F-test t-test Lack-of-fit sum of squares Confidence band Coefficient of determination Multiple correlation...

SStot, and the FVU is 0. Coefficient of determination Correlation Explained sum of squares Lack-of-fit sum of squares Linear regression Regression analysis...

links 1.96 2SLS (two-stage least squares) – redirects to instrumental variable 3SLS – see three-stage least squares 68–95–99.7 rule 100-year flood A priori...

Least trimmed squares (LTS), or least trimmed sum of squares, is a robust statistical method that fits a function to a set of data whilst not being unduly...

general case, "The analysis of variance can also be applied to unbalanced data, but then the sums of squares, mean squares, and F-ratios will depend on...

The partial least squares path modeling or partial least squares structural equation modeling (PLS-PM, PLS-SEM) is a method for structural equation modeling...

of a sum of squares calculations. Deviance is analogous to the sum of squares calculations in linear regression and is a measure of the lack of fit to...

be to note where a few squares together share possible values thereby eliminating the possibility that other squares in that sum could have those values...

expected proportions are significant, indicating model lack of fit. The Pearson chi-squared goodness of fit test provides a method to test if the observed and...

axis. The least-squares fit is a common method to fit a straight line through the data. This method minimizes the sum of the squared errors in the data...

auxiliary regression of the squared residuals on the independent variables. From this auxiliary regression, the explained sum of squares is retained, divided...

regression sum of squares, also called the explained sum of squares, and SS tot {\displaystyle {\text{SS}}_{\text{tot}}} is the total sum of squares (proportional...

the fit of a regression model that has been estimated using ordinary least squares. It is applied in the context of model selection, where a number of predictor...

packing in a square. Packing squares into other shapes can have high computational complexity: testing whether a given number of unit squares can fit into an...

provide the best fit in some sense, often defined as the fit that results in the minimum sum of the squared errors (a least squares criterion). Additive...

{\Lambda } } where Λ is the diagonal matrix of eigenvalues λ(k) of XTX. λ(k) is equal to the sum of the squares over the dataset associated with each component...

least squares and beyond). Springer Vieweg. ISBN 978-3-658-11455-8. Tofallis, Chris (2008). "Least Squares Percentage Regression". Journal of Modern...