Normal view

## Applied regression analysis / N. R. Draper, H. Smith.

Editor: New York : Wiley, c1981Edición: 2nd edDescripción: xiv, 709 p. : il. ; 24 cmISBN: 0471029955Tema(s): Regression analysisOtra clasificación: 62-01 (62J05) Recursos en línea: Contributor biographical information | Publisher description | Table of contents only
Contenidos:
```1 Fitting a Straight Line by Least Squares [1]
1.0 Introduction: The Need for Statistical Analysis [1]
1.1 Straight Line Relationships between Two Variables [5]
1.2 Linear Regression: Fitting a Straight Line [8]
1.3 The Precision of the Estimated Regression [17]
1.4 Examining the Regression Equation [22]
1.5 Lack of Fit and Pure Error [33]
1.6 The Correlation between X and Y [43]
1.7 Inverse Regression (Straight Line Case) [47]
1.8 Some Practical Implications of Chapter 1 [51]
Exercises [55]
2 The Matrix Approach to Linear Regression [70]
2.0 Introduction [70]
2.1 Fitting a Straight Line in Matrix Terms: The Estimates of Bo and B1 [70]
2.2 The Analysis of Variance in Matrix Terms [80]
2.3 The Variances and Covariance of b0 and b1 from the Matrix Calculation [82]
2.4 Variance of Ŷ Using the Matrix Development [83]
2.5 Summary of Matrix Approach to Fitting a Straight Line [84]
2.6 The General Regression Situation [85]
2.7 The “Extra Sum of Squares” Principle [97]
2.8 Orthogonal Columns in the X-Matrix [98]
2.9 Partial F-Tests and Sequential F-Tests [101]
2.10 Testing a General Linear Hypothesis in Regression Situations [102]
2.11 Weighted Least Square [108]
2.12 Bias in Regression Estimates [117]
2.13 Restricted Least Squares [122]
2.14 Some Notes on Errors in the Predictors (As Well as in the Response) [122]
2.15 Inverse Regression (Multiple Predictor Case) [125]
Appendix 2A Selected Useful Matrix Results [126]
Appendix 2B Expected Value of Extra Sum of Squares [128]
Appendix 2C How Significant Should My Regression Be? [129]
Appendix 2D Lagrange's Undetermined Multipliers [134]
Exercises [136]
3 The Examination of Residuals [141]
3.0 Introduction [141]
3.1 Overall Plot [142]
3.2 Time Sequence Plot [145]
3.3 Plot Against Ŷi [147]
3.4 Plot Against the Predictor Variables Xji, i = 1, 2,..., n [148]
3.5 Other Residuals Plots [149]
3.6 Statistics for Examination of Residuals [150]
3.7 Correlations among the Residuals [151]
3.8 Outliers [152]
3.9 Serial Correlation in Residuals [153]
3.10. Examining Runs in the Time Sequence Plot of Residuals [157]
3.11 The Durbin-Watson Test for a Certain Type of Serial Correlation [162]
3.12 Detection of Influential Observations [169]
Appendix 3A. Normal and Half-Normal Plots [177]
Exercises [183]
4 Two Predictor Variables [193]
4.0 Introduction [193]
4.1 Multiple Regression with Two Predictor Variables as a Sequence of Straight-Line Regressions [196]
4.2 Examining the Regression Equation [204]
Exercises [212]
5 More Complicated Models [218]
5.0 Introduction [218]
5.1 Polynomial Models of Various Orders in the Xj [219]
5.2 Models Involving Transformations Other Than Integer Powers [221]
5.3 Families of Transformations [225]
5.4 The Use of “Dummy” Variables in Multiple Regression [241]
5.5 Centering and Scaling; Performing the Regression in Correlation Form [257]
5.6 Orthogonal Polynomials [266]
5.7 Transforming X Matrices to Obtain Orthogonal Columns [275]
5.8 Regression Analysis of Summary Data [278]
Exercises [280]
6 Selecting the “Best” Regression Equation [294]
6.0 Introduction [294]
6.1 All Possible Regressions [296]
6.2 “Best Subset” Regression [303]
6.3 The Backward Elimination Procedure [305]
6.4 The Stepwise Regression Procedure [307]
6.5 A Drawback to Understand but not be Overly Concerned About [311]
6.6 Variations on the Previous Methods [312]
6.7 Ridge Regression [313]
6.8 PRESS [325]
6.9 Principal Component Regression [327]
6.10 Latent Root Regression [332]
6.11 The Stagewise Regression Procedure [337]
6.12 Summary [341]
6.13 Computational Method for Stepwise Regression [342]
6.14 Robust Regression [342]
6.15 Some Comments on Statistical Computer Packages [344]
Appendix 6A Canonical Form of Ridge Regression [349]
Exercises [352]
7 Two Specific Problems [380]
7.0 Introduction [380]
7.1 The First Problem [380]
7.2 Examination of the Data [381]
7.3 Choosing the First Variable to Enter Regression [383]
7.4 Construction of New Variables [386]
7.5 The Addition of a Cross-Product Term to the Model [386]
7.6 Enlarging the Model [388]
7.7 The Second Problem. Worked Examples of Second-Order Surface Fitting for k = 3 and k = 2 Variables [390]
Exercises [404]
8 Multiple Regression and Mathematical Model Building [412]
8.0 Introduction [412]
8.1 Planning the Model Building Process [414]
8.2 Development of the Mathematical Model [418]
8.3 Validation and Maintenance of the Mathematical Model [419]
9 Multiple Regression Applied to Analysis of Variance Problems [423]
9.0 Introduction [423]
9.1 The One-Way Classification : An Example [424]
9.2 Regression Treatment of the One-Way Classification Example [427]
9.3 The One-Way Classification [431]
9.4 Regression Treatment of the One-Way Classification Using the Original Model [432]
9.5 Regression Treatment of the One-Way Classification: Independent Normal Equations [437]
9.6 The Two-Way Classification with Equal Numbers of Observations in the Cells: An Example [439]
9.7 Regression Treatment of the Two-Way Classification Example [441]
9.8 The Two-Way Classification with Equal Numbers of Observations in the Cells [445]
9.9 Regression Treatment of the Two-Way Classification with Equal Numbers of Observations in the Cells [447]
9.10 Example: The Two-Way Classification [451]
Exercises [454]
10 An Introduction to Nonlinear Estimation [458]
10.0 Introduction [458]
10.1 Least Squares in the Nonlinear Case [459]
10.2 Estimating the Parameters of a Nonlinear System [462]
10.3 An Example [475]
10.4 A Note on Reparameterization of the Model [488]
10.5 The Geometry of Linear Least Squares [489]
10.6 The Geometry of Nonlinear Least Squares [500]
10.7 Nonlinear Growth Models [505]
10.8 Nonlinear Models: Other Work [513]
Exercises [517]
Normal Distribution [530]
Percentage Points of the t-Distribution [532]
Percentage Points of the F-Distribution [533]```
Item type Home library Shelving location Call number Materials specified Status Date due Barcode Course reserves
Libros
Libros ordenados por tema 62 D765-2 (Browse shelf) Available A-5945

Incluye índice.

Bibliografía: p. 675-699.

1 Fitting a Straight Line by Least Squares [1] --
1.0 Introduction: The Need for Statistical Analysis [1] --
1.1 Straight Line Relationships between Two Variables [5] --
1.2 Linear Regression: Fitting a Straight Line [8] --
1.3 The Precision of the Estimated Regression [17] --
1.4 Examining the Regression Equation [22] --
1.5 Lack of Fit and Pure Error [33] --
1.6 The Correlation between X and Y [43] --
1.7 Inverse Regression (Straight Line Case) [47] --
1.8 Some Practical Implications of Chapter 1 [51] --
Exercises [55] --
2 The Matrix Approach to Linear Regression [70] --
2.0 Introduction [70] --
2.1 Fitting a Straight Line in Matrix Terms: The Estimates of Bo and B1 [70] --
2.2 The Analysis of Variance in Matrix Terms [80] --
2.3 The Variances and Covariance of b0 and b1 from the Matrix Calculation [82] --
2.4 Variance of Ŷ Using the Matrix Development [83] --
2.5 Summary of Matrix Approach to Fitting a Straight Line [84] --
2.6 The General Regression Situation [85] --
2.7 The “Extra Sum of Squares” Principle [97] --
2.8 Orthogonal Columns in the X-Matrix [98] --
2.9 Partial F-Tests and Sequential F-Tests [101] --
2.10 Testing a General Linear Hypothesis in Regression Situations [102] --
2.11 Weighted Least Square [108] --
2.12 Bias in Regression Estimates [117] --
2.13 Restricted Least Squares [122] --
2.14 Some Notes on Errors in the Predictors (As Well as in the Response) [122] --
2.15 Inverse Regression (Multiple Predictor Case) [125] --
Appendix 2A Selected Useful Matrix Results [126] --
Appendix 2B Expected Value of Extra Sum of Squares [128] --
Appendix 2C How Significant Should My Regression Be? [129] --
Appendix 2D Lagrange's Undetermined Multipliers [134] --
Exercises [136] --
3 The Examination of Residuals [141] --
3.0 Introduction [141] --
3.1 Overall Plot [142] --
3.2 Time Sequence Plot [145] --
3.3 Plot Against Ŷi [147] --
3.4 Plot Against the Predictor Variables Xji, i = 1, 2,..., n [148] --
3.5 Other Residuals Plots [149] --
3.6 Statistics for Examination of Residuals [150] --
3.7 Correlations among the Residuals [151] --
3.8 Outliers [152] --
3.9 Serial Correlation in Residuals [153] --
3.10. Examining Runs in the Time Sequence Plot of Residuals [157] --
3.11 The Durbin-Watson Test for a Certain Type of Serial Correlation [162] --
3.12 Detection of Influential Observations [169] --
Appendix 3A. Normal and Half-Normal Plots [177] --
Exercises [183] --
4 Two Predictor Variables [193] --
4.0 Introduction [193] --
4.1 Multiple Regression with Two Predictor Variables as a Sequence of Straight-Line Regressions [196] --
4.2 Examining the Regression Equation [204] --
Exercises [212] --
5 More Complicated Models [218] --
5.0 Introduction [218] --
5.1 Polynomial Models of Various Orders in the Xj [219] --
5.2 Models Involving Transformations Other Than Integer Powers [221] --
5.3 Families of Transformations [225] --
5.4 The Use of “Dummy” Variables in Multiple Regression [241] --
5.5 Centering and Scaling; Performing the Regression in Correlation Form [257] --
5.6 Orthogonal Polynomials [266] --
5.7 Transforming X Matrices to Obtain Orthogonal Columns [275] --
5.8 Regression Analysis of Summary Data [278] --
Exercises [280] --
6 Selecting the “Best” Regression Equation [294] --
6.0 Introduction [294] --
6.1 All Possible Regressions [296] --
6.2 “Best Subset” Regression [303] --
6.3 The Backward Elimination Procedure [305] --
6.4 The Stepwise Regression Procedure [307] --
6.5 A Drawback to Understand but not be Overly Concerned About [311] --
6.6 Variations on the Previous Methods [312] --
6.7 Ridge Regression [313] --
6.8 PRESS [325] --
6.9 Principal Component Regression [327] --
6.10 Latent Root Regression [332] --
6.11 The Stagewise Regression Procedure [337] --
6.12 Summary [341] --
6.13 Computational Method for Stepwise Regression [342] --
6.14 Robust Regression [342] --
6.15 Some Comments on Statistical Computer Packages [344] --
Appendix 6A Canonical Form of Ridge Regression [349] --
Exercises [352] --
7 Two Specific Problems [380] --
7.0 Introduction [380] --
7.1 The First Problem [380] --
7.2 Examination of the Data [381] --
7.3 Choosing the First Variable to Enter Regression [383] --
7.4 Construction of New Variables [386] --
7.5 The Addition of a Cross-Product Term to the Model [386] --
7.6 Enlarging the Model [388] --
7.7 The Second Problem. Worked Examples of Second-Order Surface Fitting for k = 3 and k = 2 Variables [390] --
Exercises [404] --
8 Multiple Regression and Mathematical Model Building [412] --
8.0 Introduction [412] --
8.1 Planning the Model Building Process [414] --
8.2 Development of the Mathematical Model [418] --
8.3 Validation and Maintenance of the Mathematical Model [419] --
9 Multiple Regression Applied to Analysis of Variance Problems [423] --
9.0 Introduction [423] --
9.1 The One-Way Classification : An Example [424] --
9.2 Regression Treatment of the One-Way Classification Example [427] --
9.3 The One-Way Classification [431] --
9.4 Regression Treatment of the One-Way Classification Using the Original Model [432] --
9.5 Regression Treatment of the One-Way Classification: Independent Normal Equations [437] --
9.6 The Two-Way Classification with Equal Numbers of Observations in the Cells: An Example [439] --
9.7 Regression Treatment of the Two-Way Classification Example [441] --
9.8 The Two-Way Classification with Equal Numbers of Observations in the Cells [445] --
9.9 Regression Treatment of the Two-Way Classification with Equal Numbers of Observations in the Cells [447] --
9.10 Example: The Two-Way Classification [451] --
Exercises [454] --
10 An Introduction to Nonlinear Estimation [458] --
10.0 Introduction [458] --
10.1 Least Squares in the Nonlinear Case [459] --
10.2 Estimating the Parameters of a Nonlinear System [462] --
10.3 An Example [475] --
10.4 A Note on Reparameterization of the Model [488] --
10.5 The Geometry of Linear Least Squares [489] --
10.6 The Geometry of Nonlinear Least Squares [500] --
10.7 Nonlinear Growth Models [505] --
10.8 Nonlinear Models: Other Work [513] --
Exercises [517] --
Normal Distribution [530] --
Percentage Points of the t-Distribution [532] --
Percentage Points of the F-Distribution [533] --

MR, 82f:62002

There are no comments on this title.