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The Resource Modeling Binary Correlated Responses using SAS, SPSS and R, (electronic resource)

Modeling Binary Correlated Responses using SAS, SPSS and R, (electronic resource)

Label
Modeling Binary Correlated Responses using SAS, SPSS and R
Title
Modeling Binary Correlated Responses using SAS, SPSS and R
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Subject
Language
eng
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Cataloging source
AU-PeEL
Nature of contents
dictionaries
Series statement
ICSA Book Series in Statistics
Series volume
v.9
Modeling Binary Correlated Responses using SAS, SPSS and R, (electronic resource)
Label
Modeling Binary Correlated Responses using SAS, SPSS and R, (electronic resource)
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=4178556
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Note
Description based upon print version of record
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Contents
  • Preface -- Part I: Introduction and Review of Modeling Uncorrelated Observations -- Part II: Analyzing Correlated Data Through Random Component -- Part III: Analyzing Correlated Data Through Systematic Components -- Part IV: Analyzing Correlated Data Through the Joint Modeling of Mean and Variance -- Contents -- Part I: Introduction and Review of Modeling Uncorrelated Observations -- Chapter 1: Introduction to Binary Logistic Regression -- 1.1 Motivating Example -- 1.2 Definition and Notation -- 1.2.1 Notations -- 1.2.2 Definitions
  • Categorical Variable in the Form of a Series of Binary Variables -- Relationship Between Response and Predictor Variables -- 1.3 Exploratory Analyses -- 1.4 Statistical Models -- 1.4.1 Chapter 3: Standard Binary Logistic Regression Model -- 1.4.2 Chapter 4: Overdispersed Logistic Regression Model -- 1.4.3 Chapter 5: Survey Data Logistic Regression Model -- 1.4.4 Chapter 6: Generalized Estimating Equations Logistic Regression Model -- 1.4.5 Chapter 7: Generalized Method of Moments Logistic Regression Model -- 1.4.6 Chapter 8: Exact Logistic Regression Model
  • 1.4.7 Chapter 9: Two-Level Nested Logistic Regression Model -- 1.4.8 Chapter 10: Hierarchical Logistic Regression Model -- 1.4.9 Chapter 11: Fixed Effects Logistic Regression Model -- 1.4.10 Chapter 12: Heteroscedastic Logistic Regression Model -- 1.5 Analysis of Data -- 1.5.1 SAS Programming -- 1.5.2 SPSS Programming -- 1.5.3 R Programming -- 1.6 Conclusions -- 1.7 Related Examples -- 1.7.1 Medicare Data -- 1.7.2 Philippines Data -- 1.7.3 Household Satisfaction Survey -- 1.7.4 NHANES: Treatment for Osteoporosis -- References -- Chapter 2: Short History of the Logistic Regression Model
  • 2.1 Motivating Example -- 2.2 Definition and Notation -- 2.2.1 Notation -- 2.2.2 Definition -- 2.3 Exploratory Analyses -- 2.4 Statistical Model -- 2.5 Analysis of Data -- 2.6 Conclusions -- References -- Chapter 3: Standard Binary Logistic Regression Model -- 3.1 Motivating Example -- 3.1.1 Study Hypotheses -- 3.2 Definition and Notation -- 3.3 Exploratory Analyses -- 3.4 Statistical Models -- 3.4.1 Probability -- 3.4.2 Odds -- 3.4.3 Logits -- 3.4.4 Logistic Regression Versus Ordinary Least Squares -- 3.4.5 Generalized Linear Models -- 3.4.6 Response Probability Distributions
  • 3.4.7 Log-Likelihood Functions -- 3.4.8 Maximum Likelihood Fitting -- 3.4.9 Goodness of Fit -- 3.4.10 Other Fit Statistics -- 3.4.11 Assumptions for Logistic Regression Model -- 3.4.12 Interpretation of Coefficients -- 3.4.13 Interpretation of Odds Ratio (OR) -- 3.4.14 Model Fit -- 3.4.15 Null Hypothesis -- 3.4.16 Predicted Probabilities -- 3.4.17 Computational Issues Encountered with Logistic Regression -- 3.5 Analysis of Data -- 3.5.1 Medicare Data -- SAS Output -- SAS Output -- 3.6 Conclusions -- 3.7 Related Examples -- Appendix: Partial Medicare Data time=1 -- References
  • Part II: Analyzing Correlated Data Through Random Component
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{'f': 'http://opac.lib.rpi.edu/record=b3773122'}
Extent
1 online resource (281 p.)
Form of item
electronic
Isbn
9783319238050
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remote

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