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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 .
R squared will be negative if you remove the intercept from the equation. 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.
The following outline is provided as an overview of and topical guide to regression analysis: 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 ).
In particular, Monte Carlo simulations show that one will get a very high R squared, very high individual t-statistic and a low Durbin–Watson statistic. Technically speaking, Phillips (1986) proved that parameter estimates will not converge in probability , the intercept will diverge and the slope will have a non-degenerate distribution as ...
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.
If that sum of squares is divided by n, the number of observations, the result is the mean of the squared residuals. Since this is a biased estimate of the variance of the unobserved errors, the bias is removed by dividing the sum of the squared residuals by df = n − p − 1, instead of n , where df is the number of degrees of freedom ( n ...