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Titlebook: Analyzing Categorical Data; Jeffrey S. Simonoff Textbook 2003 Springer Science+Business Media New York 2003 Analysis.Estimator.Excel.SAS.S

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樓主: PLY
21#
發(fā)表于 2025-3-25 06:36:16 | 只看該作者
Tables with More Structure,endence isn’t very interesting. What would be more promising would be to be able to fit models that allow for some structure in the table. Depending on the form of the table, many such models are possible. These models allow for a general interaction term to be summarized using fewer than (. — 1) (. — 1) degrees of freedom (often, far fewer).
22#
發(fā)表于 2025-3-25 11:31:36 | 只看該作者
Dovil? Budryt?,Vilana Pilinkait?-Sotirovi?This chapter covers the building blocks of the analysis of categorical data. First we discuss the important random variables that are the basis of analysis — the binomial, Poisson, and multinomial distributions.
23#
發(fā)表于 2025-3-25 12:39:10 | 只看該作者
24#
發(fā)表于 2025-3-25 18:09:47 | 只看該作者
25#
發(fā)表于 2025-3-25 21:52:31 | 只看該作者
Gaussian-Based Model Building,near regression model. Most of this material typically is not covered in an introductory statistics course. We will focus on the aspects of advanced regression modeling that are of direct relevance to the categorical data modeling methods discussed in succeeding chapters.
26#
發(fā)表于 2025-3-26 03:49:09 | 只看該作者
27#
發(fā)表于 2025-3-26 07:30:44 | 只看該作者
Analyzing Two-Way Tables,orm of tables of counts, or .. Despite this, it is worthwhile to examine such tables as a separate topic, as the tabular structure can lead to useful insights into appropriate models and modeling strategies. In this chapter we focus on two-way tables, starting with the simplest situation, tables wit
28#
發(fā)表于 2025-3-26 11:33:16 | 只看該作者
29#
發(fā)表于 2025-3-26 12:41:24 | 只看該作者
Multidimensional Contingency Tables, linear model (Poisson regression) point of view, this merely corresponds to incorporating more (nominal or ordinal) predictors into the model, and presents no particular difficulties. The structure in a multidimensional contingency table model, however, and the implied forms of association and inde
30#
發(fā)表于 2025-3-26 17:51:36 | 只看該作者
Regression Models for Binary Data,asic form of categorical data, however, is binary — 0 or 1. It is often of great interest to try to model the probability of success (the outcome coded 1) or failure (the outcome coded 0) as a function of other predictors. Consider a study designed to investigate risk factors for cancer. Attributes
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