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Course Syllabus

Master 2023/2024

Multidimensional Data Analysis

Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type: Elective course
Area of studies: Applied Mathematics and Informatics
Delivered by: International laboratory for Applied Network Research
Where: Faculty of Social Sciences
When: 1 year, 4 module
Mode of studies: offline
Open to: students of one campus
Master’s programme: Applied Statistics with Network Analysis
Language: English
ECTS credits: 3
Contact hours: 40

Course Syllabus

Full Syllabus

Abstract

This course will take a modern, data-analytic approach to the multiple regression model. Our coverage of the material will emphasize the ways that graphical tools can augment traditional methods for describing how the conditional distribution of a dependent variable changes along with the values of one or more independent variables. The course will examine the basic nature and assumptions of the linear regression model, diagnostic tools for detecting violations of the regression as-sumptions, and strategies for dealing with situations in which the basic assumptions are violated.
Learning Objectives

Learning Objectives

  • The goal of the course is to ensure that students understand topics and principles of applied linear models on an advanced level.
  • The course gives students an important foundation to develop and conduct their own research as well as to evaluate research of others.
Expected Learning Outcomes

Expected Learning Outcomes

  • Have the skill to work with statistical software, required to analyze the data.
  • Be able to develop and/or foster critical reviewing skills of published empirical research using applied statistical methods.
  • Have the skill to meaningfully develop an appropriate model for the research question.
  • Be able to criticize constructively and determine existing issues with applied linear models in published work.
  • Be able to explore the advantages and disadvantages of various linear modeling instruments, and demonstrate how they relate to other methods of analysis.
  • Be able to work with major linear modeling programs, especially R and SAS, so that they can use them and interpret their output.
  • Have an understanding of advanced methods of linear models and related multivariate extensions.
  • Know complex methods of aggregating data and dimensionality reduction.
  • Know innovative, effective methods for presenting the results from statistical investigations of empirical data.
  • Know new insights into the regression analysis.
  • Know various modern extensions to the traditional linear model.
Course Contents

Course Contents

  • Examining data
  • General Linear Models I: The Basics of Least Squares Regression
  • General Linear Models II: Effective Presentation I
  • Regression with Categorical Dependent Variables I
  • Regression with Categorical Dependent Variables II
  • Regression diagnostics I: Unusual observations
  • Regression Diagnostics II: Nonlinearity, Nonnormality, and Heteroskedasticity
  • Resampling techniques for regression
  • Nonlinear regression
  • Nonparametric regression I: Local polynomial regression
  • Nonparametric Regression II: Smoothing Splines
  • Additive regression models (GAM) and graphical regression
Assessment Elements

Assessment Elements

  • blocking Final In-Class or Take-home exam
  • blocking Homework Assignments
  • blocking In-Class Labs
  • blocking Quizzes
Interim Assessment

Interim Assessment

  • 2023/2024 4th module
    0.5 * Final In-Class or Take-home exam + 0.2 * Homework Assignments + 0.2 * In-Class Labs + 0.1 * Quizzes
Bibliography

Bibliography

Recommended Core Bibliography

  • Brown, B. (2012). Multivariate Analysis for the Biobehavioral and Social Sciences : A Graphical Approach. Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=405437
  • 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
  • Rencher, A. C., & Christensen, W. F. (2012). Methods of Multivariate Analysis (Vol. Third Edition). Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=472234

Recommended Additional Bibliography

  • Berry, W. D., & Sanders, M. S. (2000). Understanding Multivariate Research : A Primer For Beginning Social Scientists. Boulder, Colo: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=421170
  • Izenman, A. J. (2008). Modern Multivariate Statistical Techniques : Regression, Classification, and Manifold Learning. New York: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=275789