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Магистратура 2019/2020

Анализ категорийных переменных

Направление: 01.04.02. Прикладная математика и информатика
Когда читается: 2-й курс, 1, 2 модуль
Формат изучения: Full time
Прогр. обучения: Прикладная статистика с методами сетевого анализа
Язык: английский
Кредиты: 4

Программа дисциплины

Аннотация

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.
Цель освоения дисциплины

Цель освоения дисциплины

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

Результаты освоения дисциплины

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

Содержание учебной дисциплины

  • 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.
Элементы контроля

Элементы контроля

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

Промежуточная аттестация

  • Промежуточная аттестация (2 модуль)
    0.3 * Homework Assignments (5 x Varied points) + 0.2 * In-Class Labs (9-10 x Varied points) + 0.5 * Take-home project