Best Practices in Logistic RegressionSAGE Publications, 2014 M02 26 - 488 páginas Jason W. Osborne’s Best Practices in Logistic Regression provides students with an accessible, applied approach that communicates logistic regression in clear and concise terms. The book effectively leverages readers’ basic intuitive understanding of simple and multiple regression to guide them into a sophisticated mastery of logistic regression. Osborne’s applied approach offers students and instructors a clear perspective, elucidated through practical and engaging tools that encourage student comprehension. |
Contenido
A CONCEPTUAL INTRODUCTION TO BIVARIATE LOGISTIC REGRESSION | 1 |
HOW DOES LOGISTIC REGRESSION HANDLE A BINARY DEPENDENT VARIABLE? | 19 |
PERFORMING SIMPLE LOGISTIC REGRESSION | 45 |
A PRACTICAL GUIDE TO TESTING ASSUMPTIONS AND CLEANING DATA FOR LOGISTIC REGRESSION | 85 |
CONTINUOUS PREDICTORS WHY SPLITTING CONTINUOUS VARIABLES INTO CATEGORIES IS UNDESIRABLE | 131 |
USING UNORDERED CATEGORICAL INDEPENDENT VARIABLES IN LOGISTIC REGRESSION | 171 |
CURVILINEAR EFFECTS IN LOGISTIC REGRESSION | 201 |
LOGISTIC REGRESSION WITH MULTIPLE INDEPENDENT VARIABLES OPPORTUNITIES AND PITFALLS | 243 |
A BRIEF OVERVIEW OF PROBIT REGRESSION | 297 |
REPLICATION AND GENERALIZABILITY IN LOGISTIC REGRESSION | 313 |
MODERN AND EFFECTIVE METHODS OF DEALING WITH MISSING DATA | 357 |
MULTINOMIAL AND ORDINAL LOGISTIC REGRESSION | 389 |
MULTILEVEL MODELING WITH LOGISTIC REGRESSION | 435 |
| 451 | |
| 455 | |
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