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Обычная версия сайта
Магистратура 2020/2021

## Анализ категорийных переменных

Направление: 01.04.02. Прикладная математика и информатика
Когда читается: 2-й курс, 3, 4 модуль
Формат изучения: без онлайн-курса
Прогр. обучения: Прикладная статистика с методами сетевого анализа
Язык: английский
Кредиты: 4

### Course Syllabus

#### Abstract

This course is designed to introduce basic concepts and common statistical models and analyses for categorical data; to provide enough theory, examples of applications in a variety of disciplines (especially in social and behavioral science); and practice using categorical techniques and computer software so that students can use these methods in their own research; to attain knowledge necessary to critically read research papers that use such methods.

#### Learning Objectives

• The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.

#### Expected Learning Outcomes

• Know the most fundamental regression models for binary, ordinal, nominal and count outcomes.
• Have an understanding of the basic principles of binary models and lay the foundation for future learning in the area.
• Have the skill to meaningfully develop an appropriate model for the research question.
• Know the approaches to building the binary logit and probit models.
• Be able to interpret the results of models with non-linear outcomes.
• Know the foundation of multinomial logit models.
• Be able to work with major linear modeling programs, especially SAS, so that they can use them and interpret their output.
• Be able to develop and/or foster critical reviewing skills of published empirical research using applied statistical methods.
• Be able to to criticize constructively and determine existing issues with applied linear models in published work .
• Have the skill to work with statistical software, required to analyze the data.

#### Course Contents

• Introduction to Categorical data analysis
The first session will introduce the main concepts of behind categorical response data, probability distributions for categorical data, and statistical inference for discrete data.
• Contingency tables
The session will discuss Probability structure for contingency tables, comparing proportions with 2x2 tables, the odds ratio, tests for independence, exact inference, and extensions to three-way and larger tables.
• Generalized linear models
The session provides the theoretical basis and the derivation of the components of a generalized linear model, GLM for binary and count data, statistical inference and model checking, and fit-ting GLMs.
• Logistic regression
This sessions provides the framework for interpreting the logistic regression model, inference for logistic regression, logistic regression with categorical predictors, multiple logistic regression, summarizing effects, building and applying logistic regression models, and multicategory logit models.
• Loglinear models for contingency tables
This session covers loglinear models for two-way and three-way tables, inference for Loglinear models, the loglinear-logistic connection, and independence graphs and collapsibility.
• Models for matched pairs
This session will focus on comparing dependent proportions, logistic regression for matched pairs, comparing margins of square contingency tables, and symmetry issues.
• Random effects – GL Mixed Models
This session will look at random effects modeling of clustered categorical data, extensions to multinomial responses or multiple random effect terms, hierarchical models, and final notes on fitting and inference.

#### Assessment Elements

• Take-home project
• Homework Assignments (5 x Varied points)
• In-Class Labs (9-10 x Varied points)

#### Interim Assessment

• Interim assessment (4 module)
0.3 * Homework Assignments (5 x Varied points) + 0.2 * In-Class Labs (9-10 x Varied points) + 0.5 * Take-home project