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The last value listed, labelled “r2CU” is the pseudo-r-squared by Nagelkerke and is the same as the pseudo-r-squared by Cragg and Uhler. Pseudo-R-squared values are used when the outcome variable is nominal or ordinal such that the coefficient of determination R 2 cannot be applied as a measure for goodness of fit and when a likelihood ...
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).
Nicolaas Jan Dirk "Nico" Nagelkerke (born 1951) is a Dutch biostatistician and epidemiologist. As of 2012, he was a professor of biostatistics at the United Arab Emirates University . He previously taught at the University of Leiden in the Netherlands .
Nagelkerke's pseudo-R^2 is a scaled version of Cox and Snell's R^2 that can be obtained from a generalized linear model when dealing with binary responses. When using binary responses, a better coefficient of determination has been suggested in genetic profile analyses (see below).
Using the change in R-square is more appropriate than mere raw correlations, because the raw correlations do not reflect the overlap of the newly introduced measure and the existing measures. [3] For example, the College Board has used multiple regression models to assess the incremental validity of a revised SAT test. [4]
Regression analysis – use of statistical techniques for learning about the relationship between one or more dependent variables (Y) and one or more independent variables (X). Overview articles [ edit ]
This equation is similar to the equation involving (,) in the introduction (this is the matrix version of that equation). When X and e are uncorrelated , under certain regularity conditions the second term has an expected value conditional on X of zero and converges to zero in the limit, so the estimator is unbiased and consistent.
Here i represents the equation number, r = 1, …, R is the individual observation, and we are taking the transpose of the column vector. The number of observations R is assumed to be large, so that in the analysis we take R → ∞ {\displaystyle \infty } , whereas the number of equations m remains fixed.