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MATH 3P82 REGRESSION ANALYSIS Lecture Notes

MATH 3P82 REGRESSION ANALYSISL ecture Notesc Jan Vrbik23 Contents1 PREVIEW52 USINGMAPLE7 Basics .. 9 Procedures .. 10 MatrixAlgebra .. 10 Otherusefulcommands: .. 113 SIMPLE REGRESSION13 MaximumLikelihoodMethod .. 15 ConfidenceIntervals .. 17 REGRESSION coefficients .. 18 Residual 19 Hypotheses 20 ModelAdequacy(Lack-of-FitTest).. 20 Weighted REGRESSION .. 22 Correlation .. 24 Large -Sample 26 Confidence interval for the correlation 274 multivariate (LINEAR) REGRESSION29 MultivariateNormalDistribution .. 29 Partial correlation 30 Multiple REGRESSION - Main 33 Weighted-case 35 SearchingforOptimalModel .. 37 CoefficientofCorrelation(Determination) .. 384 Polynomial REGRESSION .. 39 Dummy(Indicator)Variables .. 425 NONLINEARREGRESSION436 ROBUSTREGRESSION47 Laplace distribution .. 47 CauchyCase .. 53 YuleModel.

values with, inevitably, some random component). The ’independent’ variable xis ... xk- multiple (multivariate) linear regression, 3. a polynomial function of x- polynomial regression, 4. any other type of function, with one or more parameters (e.g. y= aebx) - nonlinear regression.

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