Lecturers:

Assoc. Prof. Dimitar Atanasov, PhD
Asst. Prof. Ivan Vankov, PhD
Asst. Prof. Yolina Petrova, PhD

Course Description:

Competencies:

Students graduating the course will have basic knowledge in probability theory and will know how to describe data using numeric measure and graphical plots. They will also be introduced to the fundamentals of frequentist statistical inference.

Prerequisites:
The course requires basic numeric skills and logical reasoning skills.

Types:
Full-time Programmes

Types of Courses:
Lecture

Language of teaching:
English

Topics:

1. History and introduction in statistics
2. Measurement Scales
3. Measures of Central Tendencies
4. Measures of Central Tendencies - Exercises
5. Plotting Data
6. Correlation. Pearson Coefficient
7. Probability. Types of events - independent/related, mutually exclusive, complementary. Frequency. Calculating probabilities. Joint probability. Conditional probability.
8. Probability theory. Random variables. Independent and dependent variables. Between-group and within-group manipulation.
9. Review of material. Exercises. Real world examples. Discussion.
10. Describing random variables.
11. Probability distributions. The normal probability distribution.
12. Standardization. Z transformation. Z scores.
13. Sampling distributions. Standard error.
14. Review of material. Exercises. Real world examples. Discussion.
15. Test

Bibliography:

Agresti, A. (2012). Statistics: The Art and Science of Learning from Data, Pearson, ISBN: 9780321755940