Statistical Inference (SL_106)
About Study Course
Objective
This course introduces students with the basics of mathematical statistics. It covers the classical methods of mathematical statistics. Students will learn how to distinguish between different data structures and how to apply descriptive statistical methods. They will learn how to estimate the central tendency, variance and other parameters of interest. For biostatistical applications when several samples have to be compared statistical testing procedures are of great importance. At the end of this course students will know how to apply such testing procedures, how to make power analysis to determine the necessary sample size in practical applications. Finally, it is important to analyse the association between different variables and perform more precise dependence analysis using regression analysis which will also be covered in this course.
Prerequisites
1) Familiarity with probability theory.
2) Basic knowledge in R is required, as the software package R will be used for computation and case study applications.
Learning outcomes
• demonstrate extended knowledge of concepts and procedures in the collection, organisation, presentation and analysis of data;
• describe fundamental techniques for statistical inference;
• recognize and independently applied the main libraries and tools for statistical analysis in program R.
Students will be able independently:
• to input and prepare data for further statistical analysis in program R;
• use specific significance tests including, z-test t-test (one and two sample), chi-squared test and different goodness-of-fit tests in program R;
• find confidence intervals for parameter estimates in program R;
• do correlation analysis, ANOVA and compute and interpret simple linear regression between two and more variables in program R.
Students will be competent:
• to evaluate and choose the appropriate statistical methods and tools and construct a statistical model describing a problem based on different, also non-standard real-life situations;
• to choose independently, perform, and interpret a statistical procedure that answers a given statistical problem;
• to present a statistical analysis in a technical report;
• to independently use a computational program for simulation and interpretation of statistical models, as well as for data analysis.
