Master
2021/2022
Introduction to Statistics
Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
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
Open to:
students of one campus
Instructors:
Maria Vorobeva
Master’s programme:
Applied Statistics with Network Analysis
Language:
English
ECTS credits:
3
Contact hours:
28
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
- 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
