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Regular version of the site

# Introduction to Statistics

2023/2024
ENG
Instruction in English
3
ECTS credits
Course type:
Compulsory course
When:
1 year, 1 module

### Course Syllabus

#### Abstract

This course is an introductory course in network analysis, designed to familiarize graduate students with the general concepts and basic techniques of network analysis in sociological re-search, gain general knowledge of major theoretical concepts and methodological techniques used in social network analysis, and get some hands-on experience of collecting, analyzing, and mapping network data with SNA software. In addition, this course will provide ample opportu-nities to include network concepts in students’ master theses work.

#### 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

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

#### Course Contents

• The Where, Why, and How of Data Collection
• Graphs, Charts, and Tables—Describing Your Data
• Describing Data Using Numerical Measures
• Introduction to Probability
• Discrete Probability Distributions
• Introduction to Continuous Probability Distributions
• Introduction to Sampling Distributions
• Estimating Single Population Parameter
• Introduction to Hypothesis Testing
• Estimation and Hypothesis Testing for Two Population Parameters
• Hypothesis Tests and Estimation for Population Variances
• Analysis of Variance

• Tests
• Final test

#### Interim Assessment

• 2023/2024 1st 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.