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Course Syllabus

Master 2022/2023

Statistical Analysis I

Type: Compulsory course (Population and Development)
Area of studies: Public Administration
Delivered by: Department of Applied Economics
Where: Faculty of Social Sciences
When: 1 year, 1, 2 module
Mode of studies: offline
Open to: students of one campus
Instructors: Ekaterina Aleksandrova
Master’s programme: Population and Development
Language: English
ECTS credits: 6
Contact hours: 64

Course Syllabus

Full Syllabus

Abstract

This course is a gentle introduction to modern applied statistics and econometrics. The course is based on the following principle: first, idea and formal description of mathematical concepts are given, second, these concepts are applied to real-world problems. The course has three main chapters: probability theory, statistics, and econometrics. The statistics’ part explains principles of the basic applied statistical analysis and serves as a bridge between probability theory and the most applied part of the course, econometrics. Econometrics is a collection of mathematical tools which helps to forecast variables, find new dependences and test theories.
Learning Objectives

Learning Objectives

  • The goal of this course is to refresh, broaden, and systematize students’ knowledge of statistics and econometrics and to practice its application. The course is elementary and presents concepts and techniques in way that benefits students of all mathematical backgrounds. Fundamental concepts and methods of statistics and econometrics are introduced with emphasis on interpretation of arguments and application to real-world problems. Every topic will be backed up with an applied exercise.
Expected Learning Outcomes

Expected Learning Outcomes

  • Distinguish between dichotomous, ordinal, categorical, and continuous variables
  • Identify appropriate numerical and graphical summaries for each variable type
  • Compute a mean, median, standard deviation, quartiles, and range for a continuous variable
  • Construct a frequency distribution table for dichotomous, categorical, and ordinal variables
  • Provide an example of when the mean is a better measure of location than the median
  • Interpret the standard deviation of a continuous variable
  • Generate and interpret a box plot, side-by-side box plot, histogram and bar chart
  • Compute and interpret unconditional and conditional probabilities
  • Evaluate and interpret independence of events
  • Explain the key features of some common distributions
  • Calculate probabilities using the distribution formula
  • Define and interpret the standard error
  • Explain sampling variability
  • Apply and interpret the results of the Central Limit Theorem
  • Define point estimate, standard error, confidence level, and margin of error
  • Compare and contrast standard error and margin of error
  • Compute and interpret confidence intervals for means and proportions
  • Compute confidence intervals for the difference in means and proportions in independent samples and for the mean difference in paired samples
  • Identify the appropriate confidence interval formula based on type of outcome variable and number of samples
  • Define null and research hypothesis, test statistic, level of significance, and decision rule
  • Distinguish between Type I and Type II errors and discuss the implications of each
  • Estimate and interpret p-values
  • Explain the relationship between confidence interval estimates and p-values in drawing inferences
  • Appropriately interpret results of analysis of variance tests
  • Identify the appropriate hypothesis testing procedure based on type of outcome variable and number of samples
  • Explain the theoretical foundation of applied linear modeling
  • Define the basic principles behind working with all types of data for building regression models
  • Explore the advantages and disadvantages of various linear modeling instruments, and demonstrate how they relate to other methods of analysis
  • Define of the basic principles of linear models
  • Develop an appropriate model for the research question
  • Apply linear regression models in practice: identify situation where linear regression is appropriate; build and fit linear regression models with software; interpret estimates and diagnostic statistics; produce exploratory graphs
  • Explain the theoretical foundation of regression analysis of qualitative dependent variables
  • Develop an appropriate model for the research question with qualitative data
  • Apply regression models with qualitative data in practice: identify appropriate model; build and fit regression models with software; interpret estimates and diagnostic statistics; produce exploratory graphs
  • Explain the theoretical foundation of time series regression analysis
  • Develop an appropriate model for the research question with time series data
  • Apply regression models for time series data in practice: identify appropriate model; build and fit regression models with software; interpret estimates and diagnostic statistics; produce exploratory graphs
  • Explore and define the endogeneity problem
  • Identify the sources of endogeneity
  • Apply methods which minimise the endogeneity problems
Course Contents

Course Contents

  • Data description and numerical measures
  • Probabilities, probability distributions, sampling and estimation
  • Confidence Intervals and Hypothesis Testing
  • OLS: the assumptions and the properties of OLS estimators
  • Introduction to qualitative dependent variables
  • Introduction to Time Series
  • Endogeneity
Assessment Elements

Assessment Elements

  • non-blocking Homework Assignments
  • non-blocking Project
  • non-blocking Test 1
  • non-blocking Test 2
Interim Assessment

Interim Assessment

  • 2022/2023 2nd module
    0.2 * Homework Assignments + 0.25 * Test 2 + 0.25 * Test 1 + 0.3 * Project
Bibliography

Bibliography

Recommended Core Bibliography

  • Angrist, J. D., & Pischke, J.-S. (2009). Mostly Harmless Econometrics : An Empiricist’s Companion. Princeton: Princeton University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=329761
  • Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics. Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.cup.cbooks.9780521848053
  • Introductory econometrics : a modern approach, Wooldridge, J. M., 2009
  • Stanton A. Glantz. (2012). Primer of Biostatistics, Seventh Edition. McGraw-Hill Education / Medical.
  • Verbeek, M. (2017). A Guide to Modern Econometrics (Vol. 5th edition). Hoboken, NJ: Wiley. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1639496
  • Zhang, Y. (2007). Fundamentals of Biostatistics (6th ed.). Bernard Rosner. The American Statistician, 183. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.a.bes.amstat.v61y2007mmayp183.183

Recommended Additional Bibliography

  • Min, C. K. (2019). Applied Econometrics : A Practical Guide. Abingdon, Oxon: Routledge. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2045438