| Learning objectives |
The basic objective of this course is to
familiarize the students with the principles of probability theory
and statistics. The student will acquire knowledge about what
statistics and probability are and expand their experience base by
applying a variety of probability and statistical principles in
exercises and case studies. The goal is to enable the students to
interprete and understand basic statistical concepts as they apply
in business, economics, different types of companies or
institutions and industries.
Following the course the students can:
- Identify key theories, models and concepts of probability and
statistics.
- Use graphical and numerical methods for exploring and
summarizing data on a single categorical or quantitative
variable.
- Describe basic probability and how probability helps us
understand randomness.
- Choose and justify appropriate descriptive and inferential
methods for examining and analyzing data and drawing
conclusions.
- Analyze the association between categorical, discrete, and
continuous variables, using contingency tables, correlation,
regressions, and analysis of variance.
- Communicate the conclusions and interpretations of statistical
analysis.
|
| Course prerequisites |
| None |
| Examination |
|
Statistics:
|
| Exam
ECTS |
7,5 |
| Examination form |
Home assignment - written product |
| Individual or group exam |
Individual exam |
| Size of written product |
Max. 15 pages |
| Assignment type |
Written assignment |
| Duration |
Written product to be submitted on specified date
and time. |
| Grading scale |
7-point grading scale |
| Examiner(s) |
One internal examiner |
| Exam period |
Winter, The exam is a 4 hour home assignment.
The exam must be completed and uploaded to Digital Exam within the
time period. |
| Make-up exam/re-exam |
Same examination form as the ordinary
exam
|
|
| Course content, structure and pedagogical
approach |
|
The course will, through lectures and exercises, cover:
- Descriptive statistics, both numerical and graphical.
- Statistical inference; estimator, confidence intervals and
significance tests of hypotheses.
- Analysis of contingency tables.
- Regression analysis; simple, multiple and covariance
analysis.
- One-way and two-way analysis of variance.
|
| Description of the teaching methods |
| Lectures, exercises and computer
classes. |
| Feedback during the teaching period |
| Discussions with lecturer and teacing assistant
during lectures, exercises and computer workshops. Final exam only
asessed with a grade, with no personal feedback. Answer to exam
paper will be made available after the exam, enabling the students
to compare their answers to the correct ones in order to understand
the grade awarded. |
| Student workload |
| Attending lectures |
28 hours |
| Attending exercises |
18 hours |
| Attending computer (JMP) workshops |
4 hours |
| Attending exam |
4 hours |
| Preparation for lectures |
30 hours |
| Preparation for exercises |
20 hours |
| Preparation for JMP workshops |
12 hours |
| Revisions before exam |
90 hours |
|
| Expected literature |
|
Book: Agresti A., C. Franklin (2014): “Statistics: The Art and
Science of Learning from Data, Perason New International Edition”,
Third Edition.
Supplementary notes
|
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