Transcription of MATH 3P82 REGRESSION ANALYSIS Lecture Notes
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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 .. 555 Chapter 1 PREVIEWR egression is a procedure which selects, from a certain class of functions, the onewhich bestfits a given set of empirical data (usually presented as a table ofxandyvalues with, inevitably, some random component).
that each line of your input has to end with a semicolon: >4∗5−3/(5+2)+2ˆ(−3); 1103 56 The result of any computation can be stored under a name (which you make up, rather arbitrarily), and used in any subsequent expression. Maple then remembers the value, until the end of your session, or till you deliberately replace it with a new value.
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