The objectives of the course are to introduce students to probability theory, descriptive statistics and inferential statistics. After the course, students have acquired knowledge about probabilities, commonly used probability distributions, statistical parameters as expectation and variance, random samples and they can perform calculations relating to these notions. They can summarize data and use data to estimate parameters, to create confidence intervals and to perform hypothesis tests. Furthermore, they have the knowledge to interpret the corresponding results. They also show some basic abilities in the field of IT, especially Excel.
Relation with other courses
The methods and techniques learned in this course will be applied in many other courses. We specifically mention Statistics and Data Management 2 (which uses this course as a starting point and develops regression theory) and Marketing Management for IBA (as part of developing the research plan and analyzing the data).
Please note, that all IBA courses are only open to IBA students. It is not possible to use this course as an elective for other programs.
This course has four examinations: Test 1 (20%), Test 2 (20%), Test 3 (45%) and Team Assignment (15%). The Team Assignment is compulsory for all students who did not do it before. To pass the course the unrounded grade for Test 3 should be at least 5.0. There is one re-exam (85%) for all three tests at the same time. There is no resit for the Team Assignment. More information about the theory that is assessed during these tests as well as the examination rules for repeaters (they do not participate in the Team Assignment anymore, Test 3 has 60% weight and Re-exam has 100% weight) will be announced via Canvas at the start of the course.
This course provides a statistical basis that can be used in other course both in the first year and in later years of the study programme. The topics concern the following subjects:
- Probability theory (the classical definition of probability, probability rules)
- Random variables, probability distributions, probability density functions
- Expectation, variance, standard deviation, covariance, correlation
- Sampling theory
- Descriptive statistics: population and sample statistics, graphs;
- Estimation, confidence intervals and hypothesis testing.