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Regular version of the site

Regression Models

2019/2020
Academic Year
ENG
Instruction in English
3
ECTS credits
Course type:
Elective course
When:
2 year, 3 module

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

Learning Objectives

  • to introduce students to multivariable regression
  • to introduce students to least squares and linear regression
Expected Learning Outcomes

Expected Learning Outcomes

  • uses regression models including scatterplot smoothing
  • is able to do regression analysis
Course Contents

Course Contents

  • Least Squares and Linear Regression
  • Linear Regression & Multivariable Regression
  • Multivariable Regression, Residuals, & Diagnostics
  • Logistic Regression and Poisson Regression
Assessment Elements

Assessment Elements

  • non-blocking online course
  • non-blocking discussion with a HSE instructor
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.3 * discussion with a HSE instructor + 0.7 * online course
Bibliography

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