## Regression models for categorical dependent variables using StataNearly 50% longer than the previous edition, this second edition covers new topics for fitting and interpreting models included in Stata 9. Many of the interpretation techniques have been updated to include interval as well as point estimates. The book begins with an excellent introduction to Stata and then provides a general treatment of estimation, testing, fit, and interpretation in this class of models. It covers binary, ordinal, nominal, and count outcomes in separate chapters. The final chapter discusses how to fit and interpret models with special characteristics, such as ordinal and nominal independent variables, interaction, and nonlinear terms. |

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#### Review: Regression Models for Categorical Dependent Variables Using Stata

User Review - Stephen Cranney - GoodreadsIt simultaneously explains both the theoretical background behind the statistical procedures and the Stata syntax involved to execute the procedures; so it's perfect for people wanting to learn the methods for practical analytical purposes. Read full review

#### Review: Regression Models for Categorical Dependent Variables Using Stata

User Review - Sultan Almalki - GoodreadsOne of the best references I've read about analysing categorical dependent variables using STATA. Simple, comprehensive and nicely structured. Read full review

### Contents

Introduction | 3 |

Introduction to Stata | 15 |

Estimation testing fit and interpretation | 75 |

Copyright | |

15 other sections not shown

### Common terms and phrases

_cons age ed prst alternative alternative-specific variables asmprobit asprvalue BlueCol Bus Car byte case-specific variables chapter chi2 Coef coefficients compute predicted Conf confidence intervals create dataset default delta method diff discrete change do-file errors estimates store estimation commands example factor change fem mar kid5 independent variables indicates interpretation invc k618 age wc kid5 phd ment labor force Likelihood-ratio test listcoef Log likelihood logistic regression logit model LR test lwg inc mar kid5 phd matrix Menial missing values MNLM multinomial logit multinomial probit NBRM nolog output omitted Number of obs observations odds ratios option ordered logit P>lzl parameters plot poisson Poisson regression Pr(y praccum prchange prcounts predicted probabilities prgen Prob probit model prvalue Pseudo R2 regression model rest(mean specified SPost standard deviation Stata statistics syntax value labels variables constant Wald tests wc he lwg WhiteCol z-test zero zinb ztnb