Contents:
The content of the course consists of the following topics:
data collection, descriptive statistics, introduction to probability theory;
introduction to probability distributions: binomial, normal, and student;
estimation, testing hypotheses, constructing confidence intervals;
application of a binomial test for a population proportion;
application of t-tests to standard situations; one sample, two sample, one sample with paired observations;
correlation and Simple linear regression with associated t-tests for coefficients;
using statistical software, in particular R-Commander;
ethical issues, as touching upon good statistical practice, will be discussed in class.
In the course it will be shown, where these statistical concepts are applied in scientific research. In the tutorials the practical problems are introduced, and a detailed program is given linking the content of the course to the tutorials.
Learning outcomes:
After successful completion of this course, students are expected to be able to:
- remember and understand basic ideas of statistical inference and data collection
- determine and explain the appropriate statistical procedure, given the description of the experiment, the research question, and the type of data
- carry out the needed analyses for the discussed standard situations and assess the results in terms of the problem
- perform a hypothesis test for intercept and slope and validate the model assumptions of a simple linear model
- independently analyze data with the computer software R-Commander
