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Course Syllabus

Bachelor 2020/2021

Regression Modeling in Practice

Type: Elective course (Fundamental and Computational Linguistics)
Area of studies: Fundamental and Applied Linguistics
Delivered by: School of Linguistics
Where: Faculty of Humanities
When: 4 year, 3 module
Mode of studies: distance learning
Instructors: Yury Lander
Language: English
ECTS credits: 3
Contact hours: 2

Course Syllabus

Full SyllabusAsk Question

Abstract

This course focuses on one of the most important tools in your data analysis arsenal: regression analysis. Using either SAS or Python, you will begin with linear regression and then learn how to adapt when two variables do not present a clear linear relationship. You will examine multiple predictors of your outcome and be able to identify confounding variables, which can tell a more compelling story about your results. You will learn the assumptions underlying regression analysis, how to interpret regression coefficients, and how to use regression diagnostic plots and other tools to evaluate the quality of your regression model. Throughout the course, you will share with others the regression models you have developed and the stories they tell you. Wesleyan University: https://www.coursera.org/learn/regression-modeling-practice
Learning Objectives

Learning Objectives

  • to introduce students to regression analysis
  • to increase students' competence in choosing the statistical analysis that's most appropriate given the structure of their data, and in understanding the limitations of their data set
Expected Learning Outcomes

Expected Learning Outcomes

  • knows how to adapt when two variables do not present a clear linear relationship
  • is able to identify confounding variables
  • knows the assumptions underlying regression analysis
  • interprets regression coefficients
  • uses regression diagnostic plots and other tools to evaluate the quality of your regression model
Course Contents

Course Contents

  • Introduction to Regression
  • Basics of Linear Regression
  • Multiple Regression
  • Logistic Regression
Assessment Elements

Assessment Elements

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

  • Chatterjee, S., Hadi, A. S., & Ebooks Corporation. (2012). Regression Analysis by Example (Vol. Fifth edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=959808

Recommended Additional Bibliography

  • Montgomery, D. C., Vining, G. G., & Peck, E. A. (2012). Introduction to Linear Regression Analysis (Vol. 5th ed). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1021709