Master
2020/2021
Introduction to Statistics
Type:
Bridging course (Applied Statistics with Network Analysis)
Area of studies:
Applied Mathematics and Informatics
Delivered by:
International laboratory for Applied Network Research
When:
1 year, 1 module
Mode of studies:
offline
Instructors:
Polina Lushnikova,
Dmitry Zaytsev
Master’s programme:
Applied Statistics with Network Analysis
Language:
English
ECTS credits:
3
Contact hours:
20
Course Syllabus
Abstract
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
- 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
- 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
- The Where, Why, and How of Data CollectionData collection methods. Descriptive and inferential procedures. Sampling methods.
- Graphs, Charts, and Tables—Describing Your DataFrequency distribution. Histograms. Bar charts, pie charts, stem-and-leas diagrams. Line charts, scatter diagrams.
- Describing Data Using Numerical MeasuresMean, median, mode. Range, variance, standard deviation. Box and whisker graph. Z- scores.
- Introduction to ProbabilityApproaches to assessing probabilities. Addition rule and Multiplication rule. Conditional probability. Bayes’s Theorem.
- Discrete Probability DistributionsThe expected value. Binomial distribution. Poisson and hypergeometric distribution.
- Introduction to Continuous Probability DistributionsNormal distribution. Normal distribution table. Uniform and exponential distributions.
- Introduction to Sampling DistributionsSampling error. Standard deviation of sampling distribution. Central Limit Theorem.
- Estimating Single Population ParameterPoint estimate and confidence interval estimate. Z and t distributions. Sample size.
- Introduction to Hypothesis TestingNull and alternative hypothesis. Type I and Type II errors. Decision rule. Test statistic, critical values, p-value.
- Estimation and Hypothesis Testing for Two Population ParametersLogic of hypothesis testing. Independent population mean. Paired sample.
- Hypothesis Tests and Estimation for Population VariancesHypothesis tests for a single population variance. Chi-square distribution. Test variance difference.
- Analysis of VarianceANOVA. F – statistics.