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. |
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analysis of variance b₁ B₁X b₂ calculations Chapter Coefficients and Confidence column confidence interval Confidence Limits Constant Term correlation matrix Decoded B Coefficient degrees of freedom deviation of residuals entering Sequential F-test estimation space example F value Figure fitted equation freedom Determinant value given independent variables lack of fit least squares estimate linear regression Mean Decoded method nonlinear normal equations Observed Y Predicted obtained orthogonal orthogonal polynomials Overall F Total parameters Partial Correlation Coefficients plot Predicted Y Residual prediction equation procedure pure error regression analysis regression equation Residual Analysis Residual Normal Deviate response mean Degrees Response variable shown Source df SS Source of Variation Square of Partials SS MS F ẞ₁ ẞo Standard deviation Statistical sum of squares Term in Prediction Total corrected transformations Variable entering Sequential variance table vector X₁ X₂ Y₁ Y₂ Z₁ zero σ²