Applied Regression Analysis, Volume 1This book provides a standard, basic course in multiple linear regression, but it also includes material that either has not previously appeared in a textbook or, if it has appeared, is not generally available. |
Contents
CHAPTER | 1 |
THE EXAMINATION OF RESIDUALS | 92 |
TWO INDEPENDENT VARIABLES | 104 |
Copyright | |
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analysis of variance b₁ calculations Chapter Coefficients and Confidence column confidence interval Confidence Limits Constant Term correlation matrix Decoded degrees of freedom deviation of residuals estimation space example F value Figure fitted equation freedom Determinant value given independent variables lack of fit least squares estimate linear regression mean square method multiple regression nonlinear normal equations Observed Y Predicted obtained orthogonal orthogonal polynomials Overall F Total parameters Partial Correlation Coefficients plot polynomials Predicted Y Residual prediction equation procedure pure error regression analysis regression equation Regression Residual Residual Analysis residuals Mean response mean Degrees response Std Response variable shown Source df SS Source of Variation Square of Partials SS MS F Standard deviation Standard Error Statistics sum of squares Term in Prediction Total corrected transformations Upper/Lower variance table vector X₁ X₂ Y₁ Z₁ zero β₁ βο σ² ΣΧ