Regression Models

2021/2022

Academic Year

ENG

Instruction in English

3

ECTS credits

Delivered at:

School of Linguistics

Course type:

Elective course

When:

3 year, 3 module

Instructors

Iomdin, Boris

Lander, Yury

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

Interim Assessment

• 2021/2022 3rd module
  0.3 * discussion with a HSE instructor + 0.7 * online course

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