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Economic Statistics

Учебный год
Обучение ведется на английском языке
Курс обязательный
Когда читается:
2-й курс, 3, 4 модуль


Course Syllabus


This course equips students with basic skills in statistical analysis in economics and management. Student learn principles of sampe design, testing hypotheses, estimation of correlations. Exercises are based on business cases and datasets from business.
Learning Objectives

Learning Objectives

  • be able to design sample for the survey or data collection
  • be able to use distributions for analytical purposes
  • be able to create statistical distributions
  • be able to test hypotheses
  • be able to estimate correlation coefs
  • be able to draw conclusions based on statistical analysis
Expected Learning Outcomes

Expected Learning Outcomes

  • to be able to apply quantitative research methods in the field of business studies
  • Demonstrate knowledge of descriptive statistics and data visualization
  • Demonstrate knowledge of probability concepts
  • be able to find probability distributions for discrete events
  • be able to estimate probability distributions for continuous events
Course Contents

Course Contents

  • Introduction
  • Descriptive Statistics
  • Confidence Intervals
  • Testing Hypotheses
  • Compare Two Populations
  • Correlation
  • Descriptive Statistics of Proportions
  • Hypothesis Testing for Population Proportions
  • Goodness-of-Fit Tests and Contingency Analysis
  • Nonparametric Methods
  • Surveys
Assessment Elements

Assessment Elements

  • non-blocking Seminar Activity
  • non-blocking Home Assignments
  • blocking Oral Exam
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.2 * Home Assignments + 0.7 * Oral Exam + 0.1 * Seminar Activity


Recommended Core Bibliography

  • Fraser C. Business statistics for competitive advantage with Excel 2016: basics, model building, simulation and cases. New York, NY: Springer Science+Business Media, 2016. 475 с.
  • Denis, D. J. (2016). Applied Univariate, Bivariate, and Multivariate Statistics. Hoboken, New Jersey: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1091881
  • Denis, Daniel J. (2015). Applied Univariate, Bivariate and Multivariate Statistics, John Wiley & Sons, Inc. https://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4338227
  • Statistics and Causality : Methods for Applied Empirical Research, edited by Wolfgang Wiedermann, and Eye, Alexander von, John Wiley & Sons, Incorporated, 2016. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4530803.
  • Stowell, Sarah (2014). Using R for Statistics. Apress. https://link.springer.com/book/10.1007%2F978-1-4842-0139-8

Recommended Additional Bibliography

  • Bertail, P., Blanke, D., Cornillon, P.-A., & Matzner-Løber, E. (2019). Nonparametric Statistics : 3rd ISNPS, Avignon, France, June 2016. Cham, Switzerland: Springer. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2044916
  • Biswas, D. (2019). Probability and Statistics: Volume I. [N.p.]: New Central Book Agency. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2239779
  • Pearl, J., Glymour, M., & Jewell, N. P. (2016). Causal Inference in Statistics : A Primer. Chichester, West Sussex, UK: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1161971