Applied linear regression models / John Neter, Michael H. Kutner, Christopher J. Nachtsheim, William Wasserman.
Series The Irwin series in statisticsEditor: Chicago ; Buenos Aires : Irwin, c1996Edición: 3rd edDescripción: xv, 720 p. : il. ; 27 cmISBN: 025608601XOtra clasificación: 62J05Part I
Simple Linear Regression
1 Linear Regression with One Predictor Variable [3]
1.1 Relations between Variables [3]
1.2 Regression Models and Their Uses [6]
1.3 Simple Linear Regression Model with Distribution of Error Terms Unspecified [10]
1.4 Data for Regression Analysis [14]
1.5 Overview of Steps in Regression Analysis [15]
1.6 Estimation of Regression Function [17]
1.7 Estimation of Error Terms Variance σ2 [27]
1.8 Normal Error Regression Model [29]
2 Inferences in Regression Analysis [44]
2.1 Inferences concerning B1 [44]
2.2 Inferences concerning B0 [53]
2.3 Some Considerations on Making Inferences concerning BO and B1 [54]
2.4 Interval Estimation of E{ Yh} [56]
2.5 Prediction of New Observation [61]
2.6 Confidence Band for Regression Line [67]
2.7 Analysis of Variance Approach to Regression Analysis [69]
2.8 General Linear Test Approach [78]
2.9 Descriptive Measures of Association between X and Y in Regression Model [80]
2.10 Considerations in Applying Regression Analysis [84]
2.11 Case when X is Random [85]
3 Diagnostics and Remedial Measures [95]
3.1 Diagnostics for Predictor Variable [95]
3.2 Residuals [97]
3.3 Diagnostics for Residuals [98]
3.4 Overview of Tests Involving Residuals [110]
3.5 Correlation Test for Normality [111]
3.6 Tests for Constancy of Error Variance [112]
3.7 F Test for Lack of Fit [115]
3.8 Overview of Remedial Measures [124]
3.9 Transformations [126]
3.10 Exploration of Shape of Regression Function [135]
3.11 Case Example—-Plutonium Measurement [138]
4 Simultaneous Inferences and Other Topics in Regression Analysis [152]
4.1 Joint Estimation of B0 and [152]
4.2 Simultaneous Estimation of Mean Responses [155]
4.3 Simultaneous Prediction Intervals for New Observations [158]
4.4 Regression through Origin [159]
4.5 Effects of Measurement Errors [164]
4.6 Inverse Predictions [167]
4.7 Choice of X Levels [169]
5 Matrix Approach to Simple Linear Regression Analysis [176]
5.1 Matrices [176]
5.2 Matrix Addition and Subtraction [180]
5.3 Matrix Multiplication [182]
5.4 Special Types of Matrices [185]
5.5 Linear Dependence and Rank of Matrix [188]
5.6 Inverse of a Matrix [189]
5.7 Some Basic Theorems for Matrices [194]
5.8 Random Vectors and Matrices [194]
5.9 Simple Linear Regression Model in Matrix Terms [198]
5.10 Least Squares Estimation of Regression Parameters [200]
5.11 Fitted Values and Residuals [202]
5.12 Analysis of Variance Results [205]
5.13 Inferences in Regression Analysis [208]
Part II
Multiple Linear Regression
6 Multiple Regression—I [217]
6.1 Multiple Regression Models [217]
6.2 General Linear Regression Model in Matrix Terms [225]
6.3 Estimation of Regression Coefficients [227]
6.4 Fitted Values and Residuals [227]
6.5 Analysis of Variance Results [228]
6.6 Inferences about Regression Parameters [231]
6.7 Estimation of Mean Response and Prediction of New Observation [233]
6.8 Diagnostics and Remedial Measures [236]
6.9 An Example—Multiple Regression with Two Predictor Variables [241]
7 Multiple Regression—II [260]
7.1 Extra Sums of Squares [260]
7.2 Uses of Extra Sums of Squares in Tests for Regression Coefficients [268]
7.3 Summary of Tests concerning Regression Coefficients [271]
7.4 Coefficients of Partial Determination [274]
7.5 Standardized Multiple Regression Model [277]
7.6 Multicollinearity and Its Effects [285]
7.7 Polynomial Regression Models [296]
7.8 Interaction Regression Models [308]
7.9 Constrained Regression [315]
8 Building the Regression Model I: Selection of Predictor Variables [327]
8.1 Overview of Model-Building Process [327]
8.2 Surgical Unit Example [334]
8.3 All-Possible-Regressions Procedure for Variables Reduction [336]
8.4 Forward Stepwise Regression and Other Automatic Search Procedures for Variables Reduction [347]
8.5 Some Final Comments on Model Building for Exploratory Observational Studies [353]
9 Building the Regression Model II: Diagnostics [361]
9.1 Model Adequacy for a Predictor Variable—Partial Regression Plots [361]
9.2 Identifying Outlying Y Observations—Studentized Deleted Residuals [368]
9.3 Identifying Outlying X Observations—Hat Matrix Leverage Values [375]
9.4 Identifying Influential Cases—DFFITS, Cook’s Distance, and DFBETAS Measures [378]
9.5 Multicollinearity Diagnostics—Variance Inflation Factor [385]
9.6 Surgical Unit Example—Continued [388]
10 Building the Regression Model III: Remedial Measures and Validation [400]
10.1 Unequal Error Variances Remedial Measures—Weighted Least Squares [400]
10.2 Multicollinearity Remedial Measures—Ridge Regression [410]
10.3 Remedial Measures for Influential Cases—Robust Regression [416]
10.4 Remedial Measures for Unknown Response Function—Nonparametric Regression [425]
10.5 Remedial Measures for Evaluating Precision in Nonstandard Situations—Bootstrapping [429]
10.6 Model Validation [434]
10.7 Case Example—Mathematics Proficiency [439]
11 Qualitative Predictor Variables [455]
11.1 One Qualitative Predictor Variable [455]
11.2 Model Containing Interaction Effects [461]
11.3 More Complex Models [464]
11.4 Comparison of Two or More Regression Functions [468]
11.5 Other Uses of Indicator Variables [474]
11.6 Some Considerations in Using Indicator Variables [480]
11.7 Case Example—MNDOT Traffic Estimation [483]
12 Autocorrelation in Time Series Data [497]
12.1 Problems of Autocorrelation [497]
12.2 First-Order Autoregressive Error Model [501]
12.3 Durbin-Watson Test for Autocorrelation [504]
12.4 Remedial Measures for Autocorrelation [507]
12.5 Forecasting with Autocorrelated Error Terms [517]
Part III
Nonlinear Regression
13 Introduction to Nonlinear Regression [531]
13.1 Linear and Nonlinear Regression Models [531]
13.2 Example [535]
13.3 Least Squares Estimation in Nonlinear Regression [536]
13.4 Model Building and Diagnostics [547]
13.5 Inferences about Nonlinear Regression Parameters [548]
13.6 Learning Curve Example [555]
14 Logistic Regression, Poisson Regression, and Generalized Linear Models [567]
14.1 Regression Models with Binary Response Variable [567]
14.2 Simple Logistic Response Function [570]
14.3 Simple Logistic Regression [573]
14.4 Multiple Logistic Regression [580]
14.5 Model Building: Selection of Predictor Variables [585]
14.6 Diagnostics [590]
14.7 Inferences about Logistic Regression Parameters [599]
14.8 Inferences about Mean Response [602]
14.9 Prediction of a New Observation [605]
14.10 Polytomous Logistic Regression [608]
14.11 Poisson Regression [609]
14.12 Generalized Linear Models [614]
Part IV
Correlation Analysis
15 Normal Correlation Models [631]
15.1 Distinction between Regression and Correlation Models [631]
15.2 Bivariate Normal Distribution [632]
15.3 Conditional Inferences [636]
15.4 Inferences on Correlation Coefficients [640]
15.5 Multivariate Normal Distribution [645]
15.6 Spearman Rank Correlation Coefficient [651]| Item type | Home library | Shelving location | Call number | Materials specified | Status | Date due | Barcode | Course reserves |
|---|---|---|---|---|---|---|---|---|
Libros
|
Instituto de Matemática, CONICET-UNS | Libros ordenados por tema | 62 N469-3 (Browse shelf) | Available | A-7369 |
Incluye referencias bibliográficas (p. 708-714) e índice.
Part I --
Simple Linear Regression --
1 Linear Regression with One Predictor Variable [3] --
1.1 Relations between Variables [3] --
1.2 Regression Models and Their Uses [6] --
1.3 Simple Linear Regression Model with Distribution of Error Terms Unspecified [10] --
1.4 Data for Regression Analysis [14] --
1.5 Overview of Steps in Regression Analysis [15] --
1.6 Estimation of Regression Function [17] --
1.7 Estimation of Error Terms Variance σ2 [27] --
1.8 Normal Error Regression Model [29] --
2 Inferences in Regression Analysis [44] --
2.1 Inferences concerning B1 [44] --
2.2 Inferences concerning B0 [53] --
2.3 Some Considerations on Making Inferences concerning BO and B1 [54] --
2.4 Interval Estimation of E{ Yh} [56] --
2.5 Prediction of New Observation [61] --
2.6 Confidence Band for Regression Line [67] --
2.7 Analysis of Variance Approach to Regression Analysis [69] --
2.8 General Linear Test Approach [78] --
2.9 Descriptive Measures of Association between X and Y in Regression Model [80] --
2.10 Considerations in Applying Regression Analysis [84] --
2.11 Case when X is Random [85] --
3 Diagnostics and Remedial Measures [95] --
3.1 Diagnostics for Predictor Variable [95] --
3.2 Residuals [97] --
3.3 Diagnostics for Residuals [98] --
3.4 Overview of Tests Involving Residuals [110] --
3.5 Correlation Test for Normality [111] --
3.6 Tests for Constancy of Error Variance [112] --
3.7 F Test for Lack of Fit [115] --
3.8 Overview of Remedial Measures [124] --
3.9 Transformations [126] --
3.10 Exploration of Shape of Regression Function [135] --
3.11 Case Example—-Plutonium Measurement [138] --
4 Simultaneous Inferences and Other Topics in Regression Analysis [152] --
4.1 Joint Estimation of B0 and [152] --
4.2 Simultaneous Estimation of Mean Responses [155] --
4.3 Simultaneous Prediction Intervals for New Observations [158] --
4.4 Regression through Origin [159] --
4.5 Effects of Measurement Errors [164] --
4.6 Inverse Predictions [167] --
4.7 Choice of X Levels [169] --
5 Matrix Approach to Simple Linear Regression Analysis [176] --
5.1 Matrices [176] --
5.2 Matrix Addition and Subtraction [180] --
5.3 Matrix Multiplication [182] --
5.4 Special Types of Matrices [185] --
5.5 Linear Dependence and Rank of Matrix [188] --
5.6 Inverse of a Matrix [189] --
5.7 Some Basic Theorems for Matrices [194] --
5.8 Random Vectors and Matrices [194] --
5.9 Simple Linear Regression Model in Matrix Terms [198] --
5.10 Least Squares Estimation of Regression Parameters [200] --
5.11 Fitted Values and Residuals [202] --
5.12 Analysis of Variance Results [205] --
5.13 Inferences in Regression Analysis [208] --
Part II --
Multiple Linear Regression --
6 Multiple Regression—I [217] --
6.1 Multiple Regression Models [217] --
6.2 General Linear Regression Model in Matrix Terms [225] --
6.3 Estimation of Regression Coefficients [227] --
6.4 Fitted Values and Residuals [227] --
6.5 Analysis of Variance Results [228] --
6.6 Inferences about Regression Parameters [231] --
6.7 Estimation of Mean Response and Prediction of New Observation [233] --
6.8 Diagnostics and Remedial Measures [236] --
6.9 An Example—Multiple Regression with Two Predictor Variables [241] --
7 Multiple Regression—II [260] --
7.1 Extra Sums of Squares [260] --
7.2 Uses of Extra Sums of Squares in Tests for Regression Coefficients [268] --
7.3 Summary of Tests concerning Regression Coefficients [271] --
7.4 Coefficients of Partial Determination [274] --
7.5 Standardized Multiple Regression Model [277] --
7.6 Multicollinearity and Its Effects [285] --
7.7 Polynomial Regression Models [296] --
7.8 Interaction Regression Models [308] --
7.9 Constrained Regression [315] --
8 Building the Regression Model I: Selection of Predictor Variables [327] --
8.1 Overview of Model-Building Process [327] --
8.2 Surgical Unit Example [334] --
8.3 All-Possible-Regressions Procedure for Variables Reduction [336] --
8.4 Forward Stepwise Regression and Other Automatic Search Procedures for Variables Reduction [347] --
8.5 Some Final Comments on Model Building for Exploratory Observational Studies [353] --
9 Building the Regression Model II: Diagnostics [361] --
9.1 Model Adequacy for a Predictor Variable—Partial Regression Plots [361] --
9.2 Identifying Outlying Y Observations—Studentized Deleted Residuals [368] --
9.3 Identifying Outlying X Observations—Hat Matrix Leverage Values [375] --
9.4 Identifying Influential Cases—DFFITS, Cook’s Distance, and DFBETAS Measures [378] --
9.5 Multicollinearity Diagnostics—Variance Inflation Factor [385] --
9.6 Surgical Unit Example—Continued [388] --
10 Building the Regression Model III: Remedial Measures and Validation [400] --
10.1 Unequal Error Variances Remedial Measures—Weighted Least Squares [400] --
10.2 Multicollinearity Remedial Measures—Ridge Regression [410] --
10.3 Remedial Measures for Influential Cases—Robust Regression [416] --
10.4 Remedial Measures for Unknown Response Function—Nonparametric Regression [425] --
10.5 Remedial Measures for Evaluating Precision in Nonstandard Situations—Bootstrapping [429] --
10.6 Model Validation [434] --
10.7 Case Example—Mathematics Proficiency [439] --
11 Qualitative Predictor Variables [455] --
11.1 One Qualitative Predictor Variable [455] --
11.2 Model Containing Interaction Effects [461] --
11.3 More Complex Models [464] --
11.4 Comparison of Two or More Regression Functions [468] --
11.5 Other Uses of Indicator Variables [474] --
11.6 Some Considerations in Using Indicator Variables [480] --
11.7 Case Example—MNDOT Traffic Estimation [483] --
12 Autocorrelation in Time Series Data [497] --
12.1 Problems of Autocorrelation [497] --
12.2 First-Order Autoregressive Error Model [501] --
12.3 Durbin-Watson Test for Autocorrelation [504] --
12.4 Remedial Measures for Autocorrelation [507] --
12.5 Forecasting with Autocorrelated Error Terms [517] --
Part III --
Nonlinear Regression --
13 Introduction to Nonlinear Regression [531] --
13.1 Linear and Nonlinear Regression Models [531] --
13.2 Example [535] --
13.3 Least Squares Estimation in Nonlinear Regression [536] --
13.4 Model Building and Diagnostics [547] --
13.5 Inferences about Nonlinear Regression Parameters [548] --
13.6 Learning Curve Example [555] --
14 Logistic Regression, Poisson Regression, and Generalized Linear Models [567] --
14.1 Regression Models with Binary Response Variable [567] --
14.2 Simple Logistic Response Function [570] --
14.3 Simple Logistic Regression [573] --
14.4 Multiple Logistic Regression [580] --
14.5 Model Building: Selection of Predictor Variables [585] --
14.6 Diagnostics [590] --
14.7 Inferences about Logistic Regression Parameters [599] --
14.8 Inferences about Mean Response [602] --
14.9 Prediction of a New Observation [605] --
14.10 Polytomous Logistic Regression [608] --
14.11 Poisson Regression [609] --
14.12 Generalized Linear Models [614] --
Part IV --
Correlation Analysis --
15 Normal Correlation Models [631] --
15.1 Distinction between Regression and Correlation Models [631] --
15.2 Bivariate Normal Distribution [632] --
15.3 Conditional Inferences [636] --
15.4 Inferences on Correlation Coefficients [640] --
15.5 Multivariate Normal Distribution [645] --
15.6 Spearman Rank Correlation Coefficient [651] --
MR, REVIEW #
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