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Магистерская программа «Прикладная статистика с методами сетевого анализа»

Introduction to Statistics

Учебный год
Обучение ведется на английском языке
Курс адаптационный
Когда читается:
1-й курс, 1 модуль


Лушникова Полина Олеговна

Course Syllabus


The word statistics scares many students. This course is intended for those students who are not confident in their knowledge and abilities on statistics. The course starts with the most basic concepts about sampling and distributions and ends with more advanced concepts about hypotheses and their testing. These topics will help students in more complex courses in the programme.
Learning Objectives

Learning Objectives

  • to give students the opportunity to get acquainted with the basic concepts of statistics
  • to teach how to use statistical terms correctly and work with basic statistical concepts.
Expected Learning Outcomes

Expected Learning Outcomes

  • know the difference between different measurement scales
  • be able to explain the use of different methods in relation to a certain measurement scale
  • know which charts are suitable for which type of data
  • be able to formulate null and alternative statistical hypotheses
  • be able to explain the rejection of statistical hypotheses
  • be able to estimate the mean and variance of a sample
Course Contents

Course Contents

  • The Where, Why, and How of Data Collection
    Data collection methods. Descriptive and inferential procedures. Sampling methods.
  • Graphs, Charts, and Tables—Describing Your Data
    Frequency distribution. Histograms. Bar charts, pie charts, stem-and-leas diagrams. Line charts, scatter diagrams.
  • Describing Data Using Numerical Measures
    Mean, median, mode. Range, variance, standard deviation. Box and whisker graph. Z- scores.
  • Introduction to Probability
    Approaches to assessing probabilities. Addition rule and Multiplication rule. Conditional probability. Bayes’s Theorem.
  • Discrete Probability Distributions
    The expected value. Binomial distribution. Poisson and hypergeometric distribution.
  • Introduction to Continuous Probability Distributions
    Normal distribution. Normal distribution table. Uniform and exponential distributions.
  • Introduction to Sampling Distributions
    Sampling error. Standard deviation of sampling distribution. Central Limit Theorem.
  • Estimating Single Population Parameter
    Point estimate and confidence interval estimate. Z and t distributions. Sample size.
  • Introduction to Hypothesis Testing
    Null and alternative hypothesis. Type I and Type II errors. Decision rule. Test statistic, critical values, p-value.
  • Estimation and Hypothesis Testing for Two Population Parameters
    Logic of hypothesis testing. Independent population mean. Paired sample.
  • Hypothesis Tests and Estimation for Population Variances
    Hypothesis tests for a single population variance. Chi-square distribution. Test variance difference.
  • Analysis of Variance
    ANOVA. F – statistics.
Assessment Elements

Assessment Elements

  • non-blocking Tests
    There will be three tests during the course. They are based on lecture as well as reading materials. Each test is assigned to chapters in the main course book.
  • non-blocking Final test
    The final test includes 10 multiple choice questions about all topics studied during the course.
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.4 * Final test + 0.6 * Tests


Recommended Core Bibliography

  • Agresti, A. (2017). Statistics: The Art and Science of Learning From Data, Global Edition. Pearson.

Recommended Additional Bibliography

  • James T. McClave, & Terry Sincich. (2013). Statistics: Pearson New International Edition. Pearson.