Best Practices in Logistic Regression
eBook - ePub

Best Practices in Logistic Regression

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Best Practices in Logistic Regression

About this book

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.

Best Practices in Logistic Regression explains logistic regression in a concise and simple manner that gives students the clarity they need without the extra weight of longer, high-level texts.

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Yes, you can access Best Practices in Logistic Regression by Jason W. Osborne in PDF and/or ePUB format, as well as other popular books in Social Sciences & Social Science Research & Methodology. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover Page
  2. Half Title
  3. Title
  4. Copyright
  5. Brief Contents
  6. Detailed Contents
  7. Preface
  8. Acknowledgments
  9. About the Author
  10. 1 A CONCEPTUAL INTRODUCTION TO BIVARIATE LOGISTIC REGRESSION
  11. 2 HOW DOES LOGISTIC REGRESSION HANDLE A BINARY DEPENDENT VARIABLE?
  12. 3 PERFORMING SIMPLE LOGISTIC REGRESSION
  13. 4 A PRACTICAL GUIDE TO TESTING ASSUMPTIONS AND CLEANING DATA FOR LOGISTIC REGRESSION
  14. 5 CONTINUOUS PREDICTORS: WHY SPLITTING CONTINUOUS VARIABLES INTO CATEGORIES IS UNDESIRABLE
  15. 6 USING UNORDERED CATEGORICAL INDEPENDENT VARIABLES IN LOGISTIC REGRESSION
  16. 7 CURVILINEAR EFFECTS IN LOGISTIC REGRESSION
  17. 8 LOGISTIC REGRESSION WITH MULTIPLE INDEPENDENT VARIABLES: OPPORTUNITIES AND PITFALLS
  18. 9 A BRIEF OVERVIEW OF PROBIT REGRESSION
  19. 10 REPLICATION AND GENERALIZABILITY IN LOGISTIC REGRESSION
  20. 11 MODERN AND EFFECTIVE METHODS OF DEALING WITH MISSING DATA
  21. 12 MULTINOMIAL AND ORDINAL LOGISTIC REGRESSION
  22. 13 MULTILEVEL MODELING WITH LOGISTIC REGRESSION
  23. Author Index
  24. Subject Index
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