## Introduction to statistical methods in linear and non-linear estimation |

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6iXi 6kXk ANOVA Table approximation assertion assumption confidence interval Confidence region contribution due controlled variables critical region distance Square distance Distribution Theory Doolittle Method due to X2 estimate of a2 example experimental extra S. S. due follows full rank initial guess value least squares estimation likelihood function Likelihood Ratio method linear form linear functions linear hypothesis linear transformation linearize the model locus LUMV matrix Mean Square error Minimum Variance multivariate normal distribution Non-linear least Squares non-linear model non-singular Note observe 2/i observed response plane positive definite postulated previous result quadratic form quadratic function random variables Regression residual Square distance Square Statistical Sum of Square Suppose the model symmetric symmetric matrix Theorem true value unbiased estimator unknown v'RiV vector Wiley write X[Xi Xi6i y'Ay y'RiV y'Riy y's are uncorrelated Zi0i