Regression analysis by example (Record no. 21815)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 06857 a2200229 4500 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OSt |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20230927053628.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 230924b |||||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 978-8126545667 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519.536 |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Chatterjee, Samprit |
| 245 ## - TITLE STATEMENT | |
| Title | Regression analysis by example |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 5th |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
| Place of publication, distribution, etc. | New York: |
| Name of publisher, distributor, etc. | John Wiley & Sons, |
| Date of publication, distribution, etc. | 2014. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | xv, 393p., app., ref., ind., 24 cm X 18 cm |
| 490 ## - SERIES STATEMENT | |
| Series statement | Wiley series in probability and statistics |
| 500 ## - GENERAL NOTE | |
| General note | Recommended by: Banikanta Mishra |
| 521 ## - TARGET AUDIENCE NOTE | |
| Target audience note | Preface xiv<br/>1 Introduction 1<br/><br/>1.1 What Is Regression Analysis? 1<br/><br/>1.2 Publicly Available Data Sets 2<br/><br/>1.3 Selected Applications of Regression Analysis 3<br/><br/>1.4 Steps in Regression Analysis 13<br/><br/>1.5 Scope and Organization of the Book 21<br/><br/>Exercises 23<br/><br/>2 Simple Linear Regression 25<br/><br/>2.1 Introduction 25<br/><br/>2.2 Covariance and Correlation Coefficient 25<br/><br/>2.3 Example: Computer Repair Data 30<br/><br/>2.4 The Simple Linear Regression Model 32<br/><br/>2.5 Parameter Estimation 33<br/><br/>2.6 Tests of Hypotheses 36<br/><br/>2.7 Confidence Intervals 41<br/><br/>2.8 Predictions 41<br/><br/>2.9 Measuring the Quality of Fit 43<br/><br/>2.10 Regression Line Through the Origin 46<br/><br/>2.11 Trivial Regression Models 48<br/><br/>2.12 Bibliographic Notes 49<br/><br/>Exercises 49<br/><br/>3 Multiple Linear Regression 57<br/><br/>3.1 Introduction 57<br/><br/>3.2 Description of the Data and Model 57<br/><br/>3.3 Example: Supervisor Performance Data 58<br/><br/>3.4 Parameter Estimation 61<br/><br/>3.5 Interpretations of Regression Coefficients 62<br/><br/>3.6 Centering and Scaling 64<br/><br/>3.7 Properties of the Least Squares Estimators 67<br/><br/>3.8 Multiple Correlation Coefficient 68<br/><br/>3.9 Inference for Individual Regression Coefficients 69<br/><br/>3.10 Tests of Hypotheses in a Linear Model 71<br/><br/>3.11 Predictions 81<br/><br/>3.12 Summary 82<br/><br/>Exercises 82<br/><br/>Appendix: Multiple Regression in Matrix Notation 89<br/><br/>4 Regression Diagnostics: Detection of Model Violations 93<br/><br/>4.1 Introduction 93<br/><br/>4.2 The Standard Regression Assumptions 94<br/><br/>4.3 Various Types of Residuals 96<br/><br/>4.4 Graphical Methods 98<br/><br/>4.5 Graphs Before Fitting a Model 101<br/><br/>4.6 Graphs After Fitting a Model 105<br/><br/>4.7 Checking Linearity and Normality Assumptions 105<br/><br/>4.8 Leverage, Influence, and Outliers 106<br/><br/>4.9 Measures of Influence 111<br/><br/>4.10 The Potential-Residual Plot 115<br/><br/>4.11 What to Do with the Outliers? 116<br/><br/>4.12 Role of Variables in a Regression Equation 117<br/><br/>4.13 Effects of an Additional Predictor 122<br/><br/>4.14 Robust Regression 123<br/><br/>Exercises 123<br/><br/>5 Qualitative Variables as Predictors 129<br/><br/>5.1 Introduction 129<br/><br/>5.2 Salary Survey Data 130<br/><br/>5.3 Interaction Variables 133<br/><br/>5.4 Systems of Regression Equations 136<br/><br/>5.5 Other Applications of Indicator Variables 147<br/><br/>5.6 Seasonality 148<br/><br/>5.7 Stability of Regression Parameters Over Time 149<br/><br/>Exercises 151<br/><br/>6 Transformation of Variables 163<br/><br/>6.1 Introduction 163<br/><br/>6.2 Transformations to Achieve Linearity 165<br/><br/>6.3 Bacteria Deaths Due to XRay Radiation 167<br/><br/>6.4 Transformations to Stabilize Variance 171<br/><br/>6.5 Detection of Heteroscedastic Errors 176<br/><br/>6.6 Removal of Heteroscedasticity 178<br/><br/>6.7 Weighted Least Squares 179<br/><br/>6.8 Logarithmic Transformation of Data 180<br/><br/>6.9 Power Transformation 181<br/><br/>6.10 Summary 185<br/><br/>Exercises 186<br/><br/>7 Weighted Least Squares 191<br/><br/>7.1 Introduction 191<br/><br/>7.2 Heteroscedastic Models 192<br/><br/>7.3 Two-Stage Estimation 195<br/><br/>7.4 Education Expenditure Data 197<br/><br/>7.5 Fitting a Dose-Response Relationship Curve 206<br/><br/>Exercises 208<br/><br/>8 The Problem of Correlated Errors 209<br/><br/>8.1 Introduction: Autocorrelation 209<br/><br/>8.2 Consumer Expenditure and Money Stock 210<br/><br/>8.3 Durbin-Watson Statistic 212<br/><br/>8.4 Removal of Autocorrelation by Transformation 214<br/><br/>8.5 Iterative Estimation With Autocorrelated Errors 216<br/><br/>8.6 Autocorrelation and Missing Variables 217<br/><br/>8.7 Analysis of Housing Starts 218<br/><br/>8.8 Limitations of Durbin-Watson Statistic 222<br/><br/>8.9 Indicator Variables to Remove Seasonality 223<br/><br/>8.10 Regressing Two Time Series 226<br/><br/>Exercises 228<br/><br/>9 Analysis of Collinear Data 233<br/><br/>9.1 Introduction 233<br/><br/>9.2 Effects of Collinearity on Inference 234<br/><br/>9.3 Effects of Collinearity on Forecasting 240<br/><br/>9.4 Detection of Collinearity 245<br/><br/>Exercises 254<br/><br/>10 Working With Collinear Data 259<br/><br/>10.1 Introduction 259<br/><br/>10.2 Principal Components 259<br/><br/>10.3 Computations Using Principal Components 263<br/><br/>10.4 Imposing Constraints 263<br/><br/>10.5 Searching for Linear Functions of the β’s 267<br/><br/>10.6 Biased Estimation of Regression Coefficients 272<br/><br/>10.7 Principal Components Regression 272<br/><br/>10.8 Reduction of Collinearity in the Estimation Data 274<br/><br/>10.9 Constraints on the Regression Coefficients 276<br/><br/>10.10 Principal Components Regression: A Caution 277<br/><br/>10.11 Ridge Regression 280<br/><br/>10.12 Estimation by the Ridge Method 281<br/><br/>10.13 Ridge Regression: Some Remarks 285<br/><br/>10.14 Summary 287<br/><br/>10.15 Bibliographic Notes 288<br/><br/>Exercises 288<br/><br/>Appendix 10.A: Principal Components 291<br/><br/>Appendix 10.B: Ridge Regression 294<br/><br/>Appendix 10.C: Surrogate Ridge Regression 297<br/><br/>11 Variable Selection Procedures 299<br/><br/>11.1 Introduction 299<br/><br/>11.2 Formulation of the Problem 300<br/><br/>11.3 Consequences of Variables Deletion 300<br/><br/>11.4 Uses of Regression Equations 302<br/><br/>11.5 Criteria for Evaluating Equations 303<br/><br/>11.6 Collinearity and Variable Selection 306<br/><br/>11.7 Evaluating All Possible Equations 306<br/><br/>11.8 Variable Selection Procedures 307<br/><br/>11.9 General Remarks on Variable Selection Methods 309<br/><br/>11.10 A Study of Supervisor Performance 310<br/><br/>11.11 Variable Selection With Collinear Data 314<br/><br/>11.12 The Homicide Data 314<br/><br/>11.13 Variable Selection Using Ridge Regression 317<br/><br/>11.14 Selection of Variables in an Air Pollution Study 318<br/><br/>11.15 A Possible Strategy for Fitting Regression Models 326<br/><br/>11.16 Bibliographic Notes 327<br/><br/>Exercises 328<br/><br/>Appendix: Effects of Incorrect Model Specifications 332<br/><br/>12 Logistic Regression 335<br/><br/>12.1 Introduction 335<br/><br/>12.2 Modeling Qualitative Data 336<br/><br/>12.3 The Logit Model 336<br/><br/>12.4 Example: Estimating Probability of Bankruptcies 338<br/><br/>12.5 Logistic Regression Diagnostics 341<br/><br/>12.6 Determination of Variables to Retain 342<br/><br/>12.7 Judging the Fit of a Logistic Regression 345<br/><br/>12.8 The Multinomial Logit Model 347<br/><br/>12.8.1 Multinomial Logistic Regression 347<br/><br/>12.9 Classification Problem: Another Approach 354<br/><br/>Exercises 355<br/><br/>13 Further Topics 359<br/><br/>13.1 Introduction 359<br/><br/>13.2 Generalized Linear Model 359<br/><br/>13.3 Poisson Regression Model 360<br/><br/>13.4 Introduction of New Drugs 361<br/><br/>13.5 Robust Regression 363<br/><br/>13.6 Fitting a Quadratic Model 364<br/><br/>13.7 Distribution of PCB in U.S. Bays 366<br/><br/>Exercises 370<br/><br/>Appendix A: Statistical Tables 371<br/><br/>References 381<br/><br/>Index 389 |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| General subdivision | Regression analysis |
| 700 ## - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Hadi, Ali S. |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Source of classification or shelving scheme | Dewey Decimal Classification |
| Koha item type | Books |
| Withdrawn status | Lost status | Source of classification or shelving scheme | Damaged status | Not for loan | Home library | Current library | Date acquired | Source of acquisition | Cost, normal purchase price | Inventory number | Total Checkouts | Full call number | Barcode | Date last seen | Date last checked out | Cost, replacement price | Price effective from | Koha item type |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Decimal Classification | KEIC | KEIC | 09/16/2023 | Amazon | 4500.00 | IN-65 | 2 | 519.536 CHA | 22577 | 01/22/2025 | 01/10/2025 | 3139.00 | 09/16/2023 | Books |