Advanced Regression Models with SAS and R
Olga Korosteleva
Résumé
Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book consists of regression models that go beyond linear regression, including models for right-skewed, categorical and hierarchical observations. The book presents the theory as well as fully worked-out numerical examples with complete SAS and R codes for each regression. The emphasis is on model accuracy and the interpretation of results. For each regression, the fitted model is presented along with interpretation of estimated regression coefficients and prediction of response for given values of predictors.
Features:
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- Presents the theoretical framework for each regression.
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- Discusses data that are categorical, count, proportions, right-skewed, longitudinal and hierarchical.
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- Uses examples based on real-life consulting projects.
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- Provides complete SAS and R codes for each example.
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- Includes several exercises for every regression.
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Advanced Regression Models with SAS and R is designed as a text for an upper division undergraduate or a graduate course in regression analysis. Prior exposure to the two software packages is desired but not required.
The Author:
Olga Korosteleva is a Professor of Statistics at California State University, Long Beach. She teaches a large variety of statistical courses to undergraduate and master's students. She has published three statistical textbooks. For a number of years, she has held the position of faculty director of the statistical consulting group. Her research interests lie mostly in applications of statistical methodology through collaboration with her clients in health sciences, nursing, kinesiology, and other fields.
1 Introduction: General and Generalized Linear Regression Models
Definition of General Linear Regression Model
Definition of Generalized Linear Regression Model
Parameter Estimation and Significance Test for Coefficients
Fitted Model
Interpretation of Estimated Regression Coefficients
Model Goodness-of-Fit Check
Predicted Response
SAS Implementation
R Implementation
Example
Exercises
2 Regression Models for Response with Right-skewed Distribution
Box-Cox Power Transformation
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Gamma Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Exercises
3 Regression Models for Binary Response
Binary Logistic Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Probability
SAS Implementation
R Implementation
Example
Prohibit Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Probability
SAS Implementation
R Implementation
Example
Complementary Log-Log Model
Model Definition and Development
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Probability
SAS Implementation
R Implementation
Example
Exercises
4 Regression Models for Categorical Response
Cumulative Logit Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Probabilities
SAS Implementation
R Implementation
Example
Cumulative Prohibit Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients 2
Predicted Probabilities
SAS Implementation
R Implementation
Example
Cumulative Complementary Log-Log Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Probabilities
SAS Implementation
R Implementation
Example
Generalized Logit Model for Nominal Response
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Probabilities
SAS Implementation
R Implementation
Example
Exercises
5 Regression Models for Count Response
Poisson Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Zero-truncated Poisson Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
Implementation
R Implementation
Example
Zero-inflated Poisson Regression Model
Model Definition
Fitted Model 3
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Hurdle Poisson Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Exercises
6 Regression Models for Over-Dispersed Count Response
Negative Binomial Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Zero-truncated Negative Binomial Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Zero-inflated Negative Binomial Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Hurdle Negative Binomial Regression Model
Model Definition 4
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Exercises
7 Regression Models for Proportion Response
Beta Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Zero-inflated Beta Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
One-inflated Beta Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Zero-one-inflated Beta Regression Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Predicted Response
SAS Implementation
R Implementation
Example
Exercises
8 General Linear Regression Models for Repeated Measures Data
Random Slope and Intercept Model
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Model Goodness-of-Fit Check
Predicted Response
SAS Implementation
R Implementation
Example
Mixed Model with Covariance Structure for Error
Model Definition
Coefficients, and Predicted Response
Model Goodness-of-_t Check
SAS Implementation
R Implementation
Example
Generalized Estimating Equations Model
Model Definition
Fitted Model
Model Goodness-of-Fit Check
SAS Implementation
R Implementation
Example
Exercises
9 Generalized Linear Regression Model for Repeated Measures Data
Generalized Linear Mixed Model
Models Definition
Fitted Model, Interpretation, Prediction
Model Goodness-of-Fit Check
SAS Implementation
R Implementation
Example
Generalized Estimating Equations Model
Model Definition
SAS Implementation
R Implementation
Example
Exercises
10 Hierarchical Regression Model
Hierarchical Regression Model for Normal Response
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Model Goodness-of-Fit Check
Predicted Response
SAS Implementation
R Implementation
Example
Hierarchical Regression Model for Other Distributions
Model Definition
Fitted Model
Interpretation of Estimated Regression Coefficients
Model Goodness-of-Fit Check
Predicted Response
SAS Implementation
R Implementation
Examples
Exercises
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Taylor&francis |
| Auteur(s) | Olga Korosteleva |
| Parution | 03/12/2018 |
| Nb. de pages | 14 |
| EAN13 | 9781138049017 |
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