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Logistic regression : a self-learning text / David G. Kleinbaum.

By: Kleinbaum, David G.
Material type: materialTypeLabelBookSeries: Springer series in statisticsStatistics in the health sciences: Publisher: New York : Springer, 1994Description: xiii, 282 p. : ill. ; 25 cm.ISBN: 0387941428 ; 3540941428 .Subject(s): Medicine -- Research -- Statistical methods | Regression analysis | Logistic distributionDDC classification: 610.72
Contents:
Introduction to logistic regression -- Important special cases of the logistic model -- Computing the odds ratio in logistic regression -- Maximum likelihood techniques: an overview -- Statistical inferences using maximum likelihood techniques -- Modeling strategy guidelines -- Modeling strategy for assessing interaction and confounding -- Analysis of matched data using logistic regression.
Holdings
Item type Current library Call number Copy number Status Date due Barcode Item holds
General Lending MTU Bishopstown Library Lending 610.72 (Browse shelf(Opens below)) 1 Available 00074769
Total holds: 0

Enhanced descriptions from Syndetics:

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Her name is Elise Freeman, and her chilling cry for help-to whoever may be listening-comes too late to save her. On a DVD found near her lifeless body, the emotionally and physically battered woman chronicles a year-and-a-half-long ordeal of monstrous abuse at the hands of three sadistic tormentors. But even more shocking than the lurid details is the revelation that the offenders, like their victim, are teachers at one of L.A.'s most prestigious prep schools. With Elise now dead by uncertain means, homicide detective Milo Sturgis is assigned to probe the hallowed halls of Windsor Prep Academy. And if ever he could use Dr. Alex Delaware's psychological prowess, it's now.

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From the Hardcover edition.

Bibliography: p. 275-276. - Includes index.

Introduction to logistic regression -- Important special cases of the logistic model -- Computing the odds ratio in logistic regression -- Maximum likelihood techniques: an overview -- Statistical inferences using maximum likelihood techniques -- Modeling strategy guidelines -- Modeling strategy for assessing interaction and confounding -- Analysis of matched data using logistic regression.

Table of contents provided by Syndetics

  • Preface (p. vii)
  • Acknowledgments (p. ix)
  • Chapter 1 Introduction to Logistic Regression (p. 1)
  • Introduction (p. 2)
  • Abbreviated Outline (p. 2)
  • Objectives (p. 3)
  • Presentation (p. 4)
  • Detailed Outline (p. 29)
  • Key Formulae (p. 31)
  • Practice Exercises (p. 32)
  • Test (p. 34)
  • Answers to Practice Exercises (p. 37)
  • Chapter 2 Important Special Cases of the Logistic Model (p. 39)
  • Introduction (p. 40)
  • Abbreviated Outline (p. 40)
  • Objectives (p. 40)
  • Presentation (p. 42)
  • Detailed Outline (p. 65)
  • Practice Exercises (p. 67)
  • Test (p. 69)
  • Answers to Practice Exercises (p. 71)
  • Chapter 3 Computing the Odds Ratio in Logistic Regression (p. 73)
  • Introduction (p. 74)
  • Abbreviated Outline (p. 74)
  • Objectives (p. 75)
  • Presentation (p. 76)
  • Detailed Outline (p. 92)
  • Practice Exercises (p. 95)
  • Test (p. 97)
  • Answers to Practice Exercises (p. 99)
  • Chapter 4 Maximum Likelihood Techniques: An Overview (p. 101)
  • Introduction (p. 102)
  • Abbreviated Outline (p. 102)
  • Objectives (p. 103)
  • Presentation (p. 104)
  • Detailed Outline (p. 120)
  • Practice Exercises (p. 121)
  • Test (p. 122)
  • Answers to Practice Exercises (p. 124)
  • Chapter 5 Statistical Inferences Using Maximum Likelihood Techniques (p. 125)
  • Introduction (p. 126)
  • Abbreviated Outline (p. 126)
  • Objectives (p. 127)
  • Presentation (p. 128)
  • Detailed Outline (p. 150)
  • Practice Exercises (p. 152)
  • Test (p. 156)
  • Answers to Practice Exercises (p. 158)
  • Chapter 6 Modeling Strategy Guidelines (p. 161)
  • Introduction (p. 162)
  • Abbreviated Outline (p. 162)
  • Objectives (p. 163)
  • Presentation (p. 164)
  • Detailed Outline (p. 183)
  • Practice Exercises (p. 184)
  • Test (p. 186)
  • Answers to Practice Exercises (p. 188)
  • Chapter 7 Modeling Strategy for Assessing Interaction and Confounding (p. 191)
  • Introduction (p. 192)
  • Abbreviated Outline (p. 192)
  • Objectives (p. 193)
  • Presentation (p. 194)
  • Detailed Outline (p. 221)
  • Practice Exercises (p. 222)
  • Test (p. 224)
  • Answers to Practice Exercises (p. 225)
  • Chapter 8 Analysis of Matched Data Using Logistic Regression (p. 227)
  • Introduction (p. 228)
  • Abbreviated Outline (p. 228)
  • Objectives (p. 229)
  • Presentation (p. 230)
  • Detailed Outline (p. 243)
  • Practice Exercises (p. 245)
  • Test (p. 247)
  • Answers to Practice Exercises (p. 249)
  • Chapter 9 Polytomous Logistic Regression (p. 267)
  • Introduction (p. 268)
  • Abbreviated Outline (p. 268)
  • Objectives (p. 269)
  • Presentation (p. 270)
  • Detailed Outline (p. 293)
  • Practice Exercises (p. 295)
  • Test (p. 297)
  • Answers to Practice Exercises (p. 298)
  • Chapter 10 Ordinal Logistic Regression (p. 301)
  • Introduction (p. 302)
  • Abbreviated Outline (p. 302)
  • Objectives (p. 303)
  • Presentation (p. 304)
  • Detailed Outline (p. 320)
  • Practice Exercises (p. 322)
  • Test (p. 324)
  • Answers to Practice Exercises (p. 325)
  • Chapter 11 Logistic Regression for Correlated Data: GEE (p. 327)
  • Introduction (p. 328)
  • Abbreviated Outline (p. 328)
  • Objectives (p. 329)
  • Presentation (p. 330)
  • Detailed Outline (p. 367)
  • Practice Exercises (p. 373)
  • Test (p. 374)
  • Answers to Practice Exercises (p. 375)
  • Chapter 12 GEE Examples (p. 377)
  • Introduction (p. 378)
  • Abbreviated Outline (p. 378)
  • Objectives (p. 379)
  • Presentation (p. 380)
  • Detailed Outline (p. 396)
  • Practice Exercises (p. 397)
  • Test (p. 400)
  • Answers to Practice Exercises (p. 402)
  • Chapter 13 Other Approaches for Analysis of Correlated Data (p. 405)
  • Introduction (p. 406)
  • Abbreviated Outline (p. 406)
  • Objectives (p. 407)
  • Presentation (p. 408)
  • Detailed Outline (p. 427)
  • Practice Exercises (p. 429)
  • Test (p. 433)
  • Answers to Practice Exercises (p. 435)
  • Appendix Computer Programs for Logistic Regression (p. 437)
  • Datasets (p. 438)
  • SAS (p. 441)
  • SPSS (p. 464)
  • Stata (p. 474)
  • Test Answers (p. 487)
  • Bibliography (p. 503)
  • Index (p. 507)

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