Бакалавриат
2021/2022
Модели регрессии
Статус:
Курс по выбору (Фундаментальная и компьютерная лингвистика)
Направление:
45.03.03. Фундаментальная и прикладная лингвистика
Кто читает:
Школа лингвистики
Где читается:
Факультет гуманитарных наук
Когда читается:
3-й курс, 3 модуль
Формат изучения:
с онлайн-курсом
Онлайн-часы:
10
Охват аудитории:
для своего кампуса
Язык:
английский
Кредиты:
3
Контактные часы:
2
Course Syllabus
Abstract
Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models. Special cases of the regression model, ANOVA and ANCOVA will be covered as well. Analysis of residuals and variability will be investigated. The course will cover modern thinking on model selection and novel uses of regression models including scatterplot smoothing. The Johns Hopkins University: https://www.coursera.org/learn/regression-models
Learning Objectives
- to introduce students to multivariable regression
- to introduce students to least squares and linear regression
Expected Learning Outcomes
- is able to do regression analysis
- uses regression models including scatterplot smoothing
Course Contents
- Least Squares and Linear Regression
- Linear Regression & Multivariable Regression
- Multivariable Regression, Residuals, & Diagnostics
- Logistic Regression and Poisson Regression
Assessment Elements
- online course
- discussion with a HSE instructor
- online course
- discussion with a HSE instructor
Bibliography
Recommended Core Bibliography
- Evaluation of Regression Models: Model Assessment, Model Selection and Generalization Error. (2019). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.C9B29B32
Recommended Additional Bibliography
- Minh-Thu Tran-Nguyen, Le-Diem Bui, & Thanh-Nghi Do. (2019). Decision trees using local support vector regression models for large datasets. Journal of Information and Telecommunication, (0), 1. https://doi.org/10.1080/24751839.2019.1686682