Regression with Social Data Modeling Continuous and Limited Response Variables

ISBN-10: 0471223379
ISBN-13: 9780471223375
Edition: 2004
Authors: Alfred DeMaris
List price: $182.00
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Description: 'Regression With Social Data' presents numerous problems and exercises at the end of each chapter. Real data sets and appendices on statistical and mathematical foundations are also included.

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Book details

List price: $182.00
Copyright year: 2004
Publisher: John Wiley & Sons, Incorporated
Publication date: 9/10/2004
Binding: Hardcover
Pages: 560
Size: 6.40" wide x 9.30" long x 1.50" tall
Weight: 2.002
Language: English

'Regression With Social Data' presents numerous problems and exercises at the end of each chapter. Real data sets and appendices on statistical and mathematical foundations are also included.

Preface
Introduction to Regression Modeling
Chapter Overview
Mathematical and Statistical Models
Linear Regression Models
Generalized Linear Model
Model Evaluation
Regression Models and Causal Inference
What Is a Cause?
When Does a Regression Coefficient Have a Causal Interpretation?
Recommendations
Datasets Used in This Volume
National Survey of Families and Households Datasets
Datasets from the NVAWS
Other Datasets
Statistical Review
Simple Linear Regression
Chapter Overview
Linear Relationships
Simple Linear Regression Model
Regression Assumptions
Interpreting the Regression Equation
Estimation Using Sample Data
Rationale for OLS
Mathematics of OLS
Inferences in Simple Linear Regression
Tests about the Population Slope
Testing the Intercept
Confidence Intervals for [beta subscript 0] and [beta subscript 1]
Additional Examples
Assessing Empirical Consistency of the Model
Conforming to Assumptions
Formal Test of Empirical Consistency
Stochastic Regressors
Estimation of [beta subscript 0] and [beta subscript 1] via Maximum Likelihood
Exercises
Introduction to Multiple Regression
Chapter Overview
Employing Multiple Predictors
Advantages and Rationale for MULR
Example
Controlling for a Third Variable
MULR Model
Inferences in MULR
Omitted-Variable Bias
Modeling Interaction Effects
Evaluating Empirical Consistency
Examination of Residuals
Partial Regression Leverage Plots
Exercises
Multiple Regression with Categorical Predictors: ANOVA and ANCOVA Models
Chapter Overview
Models with Exclusively Categorical Predictors
Dummy Coding
Effect Coding
Two-Way ANOVA in Regression
Interaction between Categorical Predictors
Models with Both Categorical and Continuous Predictors
Adjusted Means
Interaction between Categorical and Continuous Predictors
Comparing Models across Groups, Revisited
Exercises
Modeling Nonlinearity
Chapter Overview
Nonlinearity Defined
Common Nonlinear Functions of X
Quadratic Functions of X
Applications of the Quadratic Model
Testing Departures from Linearity
Interpreting Quadratic Models
Nonlinear Interaction
Nonlinear Regression
Estimating the Multiplicative Model
Estimating the Nonlinear Model
Exercises
Advanced Issues in Multiple Regression
Chapter Overview
Multiple Regression in Matrix Notation
The Model
OLS Estimates
Regression Model in Standardized Form
Heteroscedasticity and Weighted Least Squares
Properties of the WLS Estimator
Consequences of Heteroscedasticity
Testing for Heteroscedasticity
Example: Regression of Coital Frequency
WLS in Practice: Two-Step Procedure
Testing Slope Homogeneity with WLS
Gender Differences in Salary Models, Revisited
WLS with Sampling Weights: WOLS
Omitted-Variable Bias in a Multivariable Framework
Mathematics of Omitted-Variable Bias
Bias in the Cross-Product Term
Example: Bias in Models for Faculty Salary
Regression Diagnostics I: Influential Observations
Building Blocks of Influence: Outliers and Leverage
Measuring Influence
Illustration of Influence Diagnosis
Regression Diagnostics II: Multicollinearity
Linear Dependencies in the Design Matrix
Consequences of Collinearity
Diagnosing Collinearity
Illustration
Alternatives to OLS When Regressors Are Collinear
Exercises
Regression with a Binary Response
Chapter Overview
Linear Probability Model
Example
Problems with the LPM
Nonlinear Probability Models
Latent-Variable Motivation of Probit and Logistic Regression
Estimation
Inferences in Logit and Probit
Logit and Probit Analyses of Violence
Empirical Consistency and Discriminatory Power in Logistic Regression
Empirical Consistency
Discriminatory Power
Exercises
Advanced Topics in Logistic Regression
Chapter Overview
Modeling Interaction
Comparing Models across Groups
Examining Variable-Specific Interaction Effects
Targeted Centering
Modeling Nonlinearity in the Regressors
Testing for Nonlinearity
Targeted Centering in Quadratic Models
Testing Coefficient Changes in Logistic Regression
Variance-Covariance Matrix of Coefficient Differences
Discriminatory Power and Empirical Consistency of Model 2
Multinomial Models
Unordered Categorical Variables
Modeling (M - 1) Log Odds
Ordered Categorical Variables
Exercises
Truncated and Censored Regression Models
Chapter Overview
Truncation and Censoring Defined
Truncation
Censoring
Simulation
Truncated Regression Model
Estimation
Simulated Data Example
Application: Scores on the First Exam
Censored Regression Model
Social Science Applications
Mean Functions
Estimation
Interpretation of Parameters
Analog of R[superscript 2]
Alternative Specification
Simulated Data Example
Applications of the Tobit Model
Sample-Selection Models
Conceptual Framework
Estimation
Nuances
Simulation
Applications of the Sample-Selection Model
Caveats Regarding Heckman's Two-Step Procedure
Exercises
Regression Models for an Event Count
Chapter Overview
Densities for Count Responses
Poisson Density
Negative Binomial Density
Modeling Count Responses with Poisson Regression
Problems with OLS
Poisson Regression Model
Truncated PRM
Censoring and Sample Selection
Count-Data Models That Allow for Overdispersion
Negative Binomial Regression Model
Zero-Inflated Models
Hurdle Models
Exercises
Introduction to Survival Analysis
Chapter Overview
Nature of Survival Data
Key Concepts in Survival Analysis
Nature of Event Histories
Critical Functions of Time: Density, Survival, Hazard
Example: Dissolution of Intimate Unions
Regression Models in Survival Analysis
Accelerated Failure-Time Model
Cox Regression Model
Adjusting for Left Truncation
Estimating Survival Functions in Cox Regression
Time-Varying Covariates
Handling Nonproportional Effects
Stratified Models
Assessing Model Fit
Exercises
Multistate, Multiepisode, and Interval-Censored Models in Survival Analysis
Chapter Overview
Multistate Models
Modeling Type-Specific Hazard Rates
Example: Transitions Out of Cohabitation
Alternative Modeling Strategies
Multiepisode Models
Example: Unemployment Spells
Nonindependence of Survival Times
Model Variation across Spells
Modeling Interval-Censored Data
Discrete-Time Hazard Model and Estimation
Converting to Person-Period Data
Discrete-Time Analysis: Examples
Exercises
Mathematics Tutorials
Answers to Selected Exercises
References
Index

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