EMPIRICAL BAYES TEST OF REGRESSION COEFFICIENT IN A MULTIPLE LINEAR REGRESSION MODEL[J]. 韦来生.Acta Mathematicae Applicatae Sinica(English Series). 1990(03)Wei Laisheng, Empirical Bayes test of regression coefficient in a multiple linear regression model, Acta Math. Appl. Sinica 6 (1990), 251–...
Tests of significance of a single partial regression coefficient in a multiple regression model are often made in situations where the standard assumptions underlying the probability calculation (for example assumption of normally of random error term) do not hold. When the random error term fails to...
V. (1996). Estimating the coefficient of cross-validity in multiple regression: A comparison of analytical and empirical methods. The Journal of Experimental Education, 64, 240-266.Kromrey, J. D., &Hines, C. V.(1996). Estimating the coefficient of cross-validity in multiple regres- sion:...
In short, the addition of independent variables to the regression model does not affect the equations for computing either the predicted values or the errors of prediction.doi:10.1007/978-0-585-25657-3_19Michael Patrick AllenSpringer USAllen, M. P. 1997. The coefficient of determination in ...
The regression coefficient for audience, b1 = 10.73, indicates that, all else being equal, a magazine with an extra 1000 readers (because X1 is given in thousands in the original data set) will charge an extra $10.73 (on average) for a one-page ad. You can also think of it as meanin...
The feasibility and effectiveness of the proposed fuzzy robust-based varying coefficient multiple regression model were examined and compared with some common fuzzy multiple regression models. Moreover, the proposed regression model was also assessed in terms of several common fuzzy multiple regression mode...
The coefficient of determination, r squared, in a multiple regression equation is the: a. Coefficient of the independent variable divided by the standard error of regression coefficient. b. Percentage of variation in the dependent variable explained by the variation in the independent variables. c....
The multiple correlation coefficient, R (or r, in the simple case), is frequently used in evaluating regression models. Statistical significance is the usual criterion for judging R. Various views of its practical significance should also be considered. One practical measure is the percent reduction...
In genetic studies, not only can the number of predictors obtained from microarray measurements be extremely large, there can also be multiple response variables. Motivated by such a situation, we consider semiparametric dimension reduction methods in sparse multivariate regression models. Previous studies...
multiple regressionSimple correlation coefficientThe inequality between the coefficient of determination and the sum of two squared simple correlation coefficients in a two-variable regression model is reexamined through two relative measures. They are the relative coefficient of determination and the relative...