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The Matrix Approach to Linear Regression
The Examination of Residuals
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analysis of variance calculations Chapter Coefficients and Confidence column confidence interval Confidence Limits Constant Term contours correlation matrix Decoded B Limits degrees of freedom deviation of residuals distribution estimate of a2 estimation space example F value Figure fitted equation follows function given independent variables lack of fit least squares estimate linear regression method multiple regression nonlinear normal equations Observed Y Predicted obtained orthogonal polynomials parameters Partial Correlation Coefficients Partial F-test plot postulated model Predicted Y Residual prediction equation procedure pure error regression analysis regression equation Residual Analysis Residual Normal Deviate response mean Response variable sample space Section Sequential F-test shown Source df SS Source of Variation Square of Partials squares due SS MS F Standard deviation sum of squares temperature Term in Prediction Total corrected transformations Variable entering variance table Variation df SS vector zero