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Statistical Methods of Analysis

2019/2020
Учебный год
ENG
Обучение ведется на английском языке
6
Кредиты
Статус:
Курс обязательный
Когда читается:
1-й курс, 3 модуль

Преподаватели

Course Syllabus

Abstract

The course covers a wide range of statistical methods and some important machine learning techniques used in today’s business analytics for exploratory and segmentation analysis, as well as for the estimation of relationships and predictive modeling. Students will get ready for data management and the analysis of survey, sales and other types of data commonly used in marketing and management. Students will learn how to use the R language – the most popular language for statistical computing, modeling and data management thanks to the fact that 50% of the course is dedicated to hands-on R coding.
Learning Objectives

Learning Objectives

  • Choose methods adequately corresponding to the objectives of a research project
  • Collect, store, process and analyze data according to high standards
  • Conduct empirical research in management and marketing using modern analytic software tools
  • Develop and apply new research methods
  • Solve economic and managerial problems using best practices of data analysis using modern computational tools
Expected Learning Outcomes

Expected Learning Outcomes

  • Choose methods adequately corresponding to the objectives of a research project
  • Collect, store, process and analyze data according to high standards
  • Conduct empirical research in management and marketing using modern analytic software tools
  • Develop and apply new research methods
  • Solve economic and managerial problems using best practices of data analysis using modern computational tools
Course Contents

Course Contents

  • Introduction. Review of basic probability and statistics concepts.
  • Introduction to the R Language
  • Describing Data
  • Relationships between Continuous Variables
  • Comparing Groups: Tables and Visualizations
  • Comparing Groups: Statistical Tests
  • Identifying Drivers of Outcomes: Linear Models
  • Reducing Data Complexity (Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA))
  • Additional Linear Model Topics (Collinearity, Logistic, Hierarchical Linear Models (HLM))
  • Confirmatory Factor Analysis and Structural Equation Modeling (SEM)
  • Segmentation: Clustering and Classification
  • Choice Modeling (Choice-based conjoint analysis)
  • Association Rules for Market Basket Analysis
Assessment Elements

Assessment Elements

  • non-blocking In-class Activity
  • non-blocking Midterm Exam
  • non-blocking Exam
  • non-blocking Kahoot tests
Interim Assessment

Interim Assessment

  • Interim assessment (3 module)
    0.25 * Exam + 0.25 * In-class Activity + 0.25 * Kahoot tests + 0.25 * Midterm Exam
Bibliography

Bibliography

Recommended Core Bibliography

  • Chapman, C., & Feit, E. M. (2015). R for Marketing Research and Analytics. Cham: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=964737

Recommended Additional Bibliography

  • Bolstad, W. M. (2017). Introduction to Bayesian Statistics (Vol. Third edition). Hoboken, N.J.: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1342637