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LINEAR MODELS I
LINEAR MODELS II
12 other sections not shown
algorithm analysis approximation assumed autocorrelation axes Bayesian beliefs cm cm cm coefficients columns coordinates correlation corresponding covariance matrix credible interval criterion defined degrees of freedom denote derived dimensions distances eigenvalues eigenvectors elements error example expected extended Kalman filter factor follows forecast frequency given gives hypothesis independent Kalman filter least squares likelihood linear model linear predictor mathematical measurement method minimax minimize multidimensional scaling multivariate Normal distribution oİoo observations obtained OnOnOnOnOn OOOOO optimal order statistic ordination orthogonal orthogonal matrix periodogram points population prediction principal components principal components analysis prior probability problem Procrustes Procrustes analysis rainfall random variable regression residual result risk functions rows scaling shown in Figure solution spectrum st st st statistic sum of squares Suppose symmetric symmetric matrix Theorem tM tM tM utility values variance variates vector zero