Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
After passing the course the students can conduct simple statistical analyses. They know how to calculate summary statistics and how to properly visualize data. Students are able to select suitable summary statistics and parameter estimates for different types of data sets and construct bootstrap confidence intervals for the estimated parameters. Students are able to select suitable statistical tests for different testing settings. They know how to apply different t-tests, chi-square tests and nonparametric tests and understand the general statistical assumptions that are required for applying these tests. Students are able to detect different types of dependencies between variables and they are familiar with univariate and multivariate linear regression analysis. They can conduct linear regression analysis in practice and they understand the underlying model assumptions.
Schedule: 11.01.2021 - 14.04.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Pauliina Ilmonen
Teacher in charge (applies in this implementation): Jukka Kohonen
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
The course is an introduction to statistical analysis and statistical inference. Course topics include estimation, simple parametric and nonparametric tests, statistical dependence and correlation, linear regression analysis and analysis of variance. Software R is used in this course.
Assessment Methods and Criteria
Homework assignments, exercise points, exam
Lectures 24 h (2), Exercises 24 h (2), Homework assignments 40 h, reading and studying the lecture materials 40 h
Lectures slides and the textbook Sheldon M. Ross, Introduction to Probability and Statistics for Engineers and Scientists (5. p), Academic Press 2014.
Substitutes for Courses
Mat-2.2104 Introduction to Statistical Inference, MS-C2104 Introduction to statistical inference
MS-A05XX First course in probability and statistics, MS-A00XX Matrix algebra
- Teacher: Jukka Kohonen