Course description:

In today's world, accurate and professional data analysis is an essential requirement in scientific life. During the course, students will learn about data collection methods, simple descriptive statistical analysis, sampling surveys, hypothesis testing, and the process of building statistical models. An appropriate statistical environment is essential for proper analysis, so during the course we will learn the basics of using R and RStudio software. This allows us to immediately test our theoretical knowledge in practice. After completing the course, students will be able to perform statistical analyses independently and understand advanced methods on their own.

Topics:

• Basic statistical concepts (Variable types, scales, data sources) + R introduction
• Basic statistical concepts (ratios, simple descriptive indicators) + R introduction 2
• Descriptive statistics indicators and their properties
• Analysis of distributions
• Data visualization in R with GGplot2 package
• Sampling techniques (IID and its properties) – Mathematical background (laws of large numbers, Central Limit Theorem) + simulation
• Hypothesis testing I – theory and practice (one sample tests)
• Hypothesis testing II – tests for more samples (comparing group means)
• nonparametric tests (Chi-squares tests for independece and distributions)
• Correlation and bivariate regression in view of causality
• Multiple regression model – OLS properties, estimation and interpretation
• Nonlinear effects (logarithm, quadratic term) in regression models + checking SLM assumptions
• Maximum Likelihood estimation in practice – basic concept of logistic regression