Applied Regression Analysis
John Wiley and Sons, Incorporated, New York, N.Y., 1966 - Analisis de regresion - 407 pages
Fitting a straight lise by squares; The matrix approach to linear regression; Thr examination of residuals; Two independent variables; More complicated models; Selecting the "best" regression equation; A specific problem; Multiple regression and mathematical model building; Multiple regression applied to analysis of variance problems; An introduction to nonlinear estimation; Percentage points of the distribution; Percentage points of the distribution.
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The Matrix Approach to Linear Regression
The Examination of Residuals
20 other sections not shown
analysis of variance ANOVA Source df Biometrika calculations Chapter Coefficients and Confidence column confidence interval Confidence Limits Constant term contours correlation matrix Decoded B Limits degrees of freedom deviation of residuals discussed Due to regression dummy variables estimation space examined example F-value Figure fitted equation follows function given independent variables iterative lack of fit least squares estimate linear regression method multiple regression normal equations Observed Y Predicted obtained orthogonal polynomials Overall F Total parameters Partial Correlation Coefficients Partial F-test plot Predicted Y Residual prediction equation pure error regression analysis regression equation Residual Analysis response mean sample space Section Sequential F-test shown Source df SS Source of Variation Square of Partials SS MS F Standard deviation sum of squares Tableau Technometrics temperature term in prediction Total corrected transformations Variable entering variance table Variation df SS vector zero