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Author References.- Tables.- Problems.- Examples.- C.5 Fit.- of Measures and Regions Confidence Bias, C.4 Pitfalls.- C.3 Weighting.- C.2.4 Method.- Simplex The C.2.3 Algorithms.- Quasi-Newton C.2.2 Method.- Descent Steepest C.2.1 Algorithms.- Other Some C.2 Procedure.- Marquardt C.1.4 Derivatives.- and Values Starting C.1.3 Halving.- Step C.1.2 Procedure.- Gauss-Newton The C.1.1 Algorithms.- Type Gauss-Newton C.1 Squares.- Least Nonlinear C Problems.- Correlation.- Multiple B.7 Transformations.- of Jacobian B.6 Distributions.- t and F The B.5 Distributions.- Chi-Square The B.4 Distribution.- Normal Multivariate The B.3 Vectors.- Random B.2 Variables.- Random of Combinations Linear B.1.4 Statistics.- Sample B.1.3 Variables.- Random Correlated B.1.2 Variables.- Random Independent. B.1.1 Variables.- Random B.1 Vectors.- Random and Variables Random B Problems.- Spaces.- Vector A.14 Forms.- Quadratic A.13 Inverse.- Generalized The A.12 Matrices.- Idempotent A.11 Vectors.- and Roots Characteristic A.10 Inverses.- A.9 Determinants.- A.8 Matrices.- Partitioned A.7 Matrix.- a of Trace A.6 Matrix.- a of Rank A.5 Vectors.- A.4 Matrices.- Identity and Null A.3 Matrix.- a of Transpose The A.2 Multiplication.- and Addition A.1 Matrices.- A Problems.- Estimator.- Shrinkage 12.4 Estimates.- of Variance and Bias 12.3.2 Regression.- Ridge of Interpretations Physical 12.3.1 Regression.- Ridge 12.3 Estimates.- of Variance and Bias 12.2.1 Regression.- Component. Principal 12.2 2..- Introduction 12.1 Estimation.- *Biased 12 Problems.- Examples.- 11.4 Procedures.- Stagewise Modified and Stagewise 11.3.3 Procedures.- Stepwise 11.3.2 Subsets.- Possible All Over Search 11.3.1 Procedures.- Selection Variable 11.3 Cp.- Mallows' Values: Predicted on *Effect 11.2.4 Estimates.- of Matrix Covariance on *Effect 11.2.3 Variance.- Error of Estimation on *Effect 11.2.2 ßj.- of Estimates on Effects 11.2.1 Variables.- Dropping of Effects Some 11.2 Introduction.- 11.1 Selection.- Variable 11 Problems.- Examples.- 10.4 Components.- Variance 10.3.3 Numbers.- Condition and Eigenvalues 10.3.2 Factors.- Inflation Variance and Tolerances 10.3.1 Multicollinearity.- Detecting 10.3 Effects.- Its and Multicollinearity 10.2 Introduction.- 10.1 Multicollinearity.- 10 Problems.- 9.- Chapter to Appendix Response.- and Predictors for Transformations Power Simultaneous 9.3.5 Predictors.- the Transforming Methods: Analytic 9.4.4 Response.- the Transforming Methods: Analytic 9.4.3 Variables.- Independent Many Method: Graphical 9.4.2 Variable.- Independent. One Method: Graphical 9.4.1 Transformations.- Choosing 9.4 Neighbors.- Near on Based Method Another 9.3.5 Approach.- Near-Neighbor Wood and Daniel 9.3.4 Measurements.- Repeat of Use 9.3.3 Terms.- Additional of Use 9.3.2 Plots.- Residual Examining 9.3.1 Transformations.- for Need the on Deciding 9.3 Proportions.- for Model Logit The 9.2.4 Models.- Multiplicative 9.2.3 Spline.- 9.2.2 Regression.- Polynomial 9.2.1 Transformations.- Common Some 9.2 Warning.- of Word Important An 9.1.1 Introduction.- 9.1 Transformations.- 9 Problems.- 8.- Chapter to Appendix Examples.- 8.6 Influence.- of Measures Other 8.5.1 Observations.- Influential 8.5 157.- Model the to Belong Not Do That Points and Outliers Detecting 8.4 Residuals.- The 8.3 Remoteness.- of Description as *Leverage 8.2.1 Leverage.- The 8.2 Introduction.- 8.1 Observations.- Influential and Outliers 8 Problems.- Parameters.- of Estimation 7.7.2 Correlation.- Spatial for Testing 1 7.7. Correlation.- Spatial 7.7 Test.- Durbin-Watson The 7.6.1 Correlation.- Serial 7.6 Model.- Curve Growth The 7.5 Errors.- Nested 7.4 Unknown.- and Unequal Variances Error 7.3.1 Squares.- Least Generalized Estimated 7.3 Known.- Is ? When Case Squares: Least Generalized 7.2 Introduction.- 7.1 Errors.- *Correlated 7 Problems.- Weighing.- 6.4 Transformations.- Stabilizing Variance 6.3 Tests.- Formal 6.2.1 Heteroscedasticity.- Detecting 6.2 Introduction.- 6.1 Variances.- Unequal 6 Problems.- Theory.- *Asymptotic 5.5 *Bootstrapping.- 5.4 Theory.- Sample Large Invoking 5.3 Normalitv.- for Tests 5.2.2 Plots.- ProbahilItV 5.2.1 Normality.- for Checking 5.2 Introduction.- 5.1 Assumption.- Normality The 5 Problems.- Variables.- Dependent as Indicators 4.6 Regression.- Line Broken 4.5 Variables.- Indicator and Continuous 4.4 Variables.- Polychotomous 4.3 Application.- Simple A 4.2 Introduction.- 4.1 Variables.- Indicator 4 Problem.- Coefficients.- of Combinations Linear for *C.I's 3.8.4 Parameters.- Regression for Region *Confidence 3.8.3 Observation.- Future a for C.I 3.8.2 Value.- Predicted a of Expectation the for C.I. 3.8.1 Regions.- and Intervals Confidence 3.8 Equations.- Repression of Comparison 3.7 Examples.- 3.6 Cases.- Special Two 3.5 Statistic.- Test of *Distribution 3.4 Test.- Ratio *Likelihood 3.3 Hypothesis.- Linear 12 Introduction.- 3.1 Regions.- Confidence and Tests 3 Problems.- 2.- Chapter to Appendix Squares.- Least *Constrained 2.12 Scaling.- and Centering 2.11 Model.- Centered The 2.10 Theorem.- Gauss-Markov The 2.9 39?2.- Fit of Measures 2.8 ?.- of Estimation 2.7 Conditions.- G-M Under Estimates of Variance and Mean 2.6 Conditions.- Gauss-Markov 2..- 31 Examples 2.4 Estimates.- Squares Least 2.3 Notation.- Matrix in Model Regression 2.2 Introduction.- 2.1 Regression.- Multiple 2 Problems.- 1.- Chapter to Appendix Predictions.- 1.10 Tests.- and Intervals Confidence 1.9 b1.- and b0 of Variance and Mean 1.8 Regression.- Simple for Fit of pleasure A 1.7 Method?.- Good a Squares Least Is When 1.6 Case.- Special a and Example Another 1.5 Squares.- Least 1.4 Model.- The 1.3 Problem.- Specific A Relationships: Determining 1.2 Relationships.- 1.1 Introduction.- 1 auf großen Endgeräten benutzerfreundlich gestaltet sein CPM – Kosten pro 1000 Kontakte (Cost Per Mille) Warum? 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