Regression Models for Categorical and Limited Dependent Variables 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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J. Scott Long 作者

SAGE Publications, Inc

译者

1997-1-9 出版日期

328 页数

USD 112.00 价格

Hardcover

丛书系列

9780803973749 图书编码

Regression Models for Categorical and Limited Dependent Variables 在线电子书图书标签: 统计学计量分析 statistics 美国 methodology 定量研究定量 E

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Regression Models for Categorical and Limited Dependent Variables 在线电子书 epub 下载 mobi 下载 pdf 下载 txt 下载 2024

Regression Models for Categorical and Limited Dependent Variables 在线电子书 pdf 下载 txt下载 epub 下载 mobi 下载 2024

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#学习回归模型的基础书籍，比较适合没有基础的小白入门。推荐搭配任何一本计量经济学和概率论与数理统计教材，以及一个讲的很明白的好老师或者精通且不厌其烦的好同学，这样就能大概看懂了。

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PS 733: Maximum Likelihood Estimation

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PS 733: Maximum Likelihood Estimation

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PS 733: Maximum Likelihood Estimation

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PS 733: Maximum Likelihood Estimation

Regression Models for Categorical and Limited Dependent Variables 在线电子书著者简介

Regression Models for Categorical and Limited Dependent Variables 在线电子书图书目录

Regression Models for Categorical and Limited Dependent Variables 在线电子书 pdf 下载 txt下载 epub 下载 mobi 在线电子书下载

Regression Models for Categorical and Limited Dependent Variables 在线电子书图书描述

A unified treatment of the most useful models for categorical and limited dependent variables (CLDVs) is provided in this book. Throughout, the links among the models are made explicit, and common methods of derivation, interpretation and testing are applied. In addition, the author explains how models relate to linear regression models whenever possible. After a review of the linear regression model and an introduction to maximum likelihood estimation, the book then: covers the logit and probit models for binary outcomes; reviews standard statistical tests associated with maximum likelihood estimation; and considers a variety of measures for assessing the fit of a model. J Scott Long also: extends the binary logit and probit models to ordered outcomes; presents the multinomial and conditioned logit models for nominal outcomes; considers models with censored and truncated dependent variables with a focus on the tobit model; describes models for sample selection bias; presents models for count outcomes by beginning with the Poisson regression model; and compares the models from earlier chapters, discussing the links between these models and others not discussed in the book.

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