PROBABILITY AND STATISTICS
- Academic year
- 2025/2026 Syllabus of previous years
- Official course title
- PROBABILITY AND STATISTICS
- Course code
- ET7007 (AF:558840 AR:322101)
- Modality
- On campus classes
- ECTS credits
- 6
- Degree level
- Bachelor's Degree Programme
- Educational sector code
- SECS-S/01
- Period
- 3rd Term
- Course year
- 1
- Where
- RONCADE
Contribution of the course to the overall degree programme goals
The course provides knowledge of descriptive statistics, probability and inference, as well as skills in the use of specific programs for analysing data and reporting.
At the end of the course, the student will be able to identify suitable models and methodologies in the context of interest; moreover he will learn to interpret and communicate the obtained results, with the aim of driving appropriate decisions.
Expected learning outcomes
- to know the main tools for graphical representation and summary of a dataset
- to know the basic concepts of probability calculus and distributions for inference
- to know the basic methodologies of statistical inference
2. Ability to apply knowledge and understanding:
- to use specific programs for data analysis and reporting
- to use the appropriate terminology in all the processes of application and communication of the acquired knowledge
3. Ability to judge:
- to apply the acquired knowledge in a specific context, identifying the most appropriate models and methods
4. Communication skills:
- to present in a clear and exhaustive way the results obtained from a statistical analysis
- to know how to interact with the other students and with the instructor during the classes and on the virtual forum
5. Learning skills:
- to use and integrate information from notes, books, slides, videos and practical lab sessions
- to assess the achieved knowledge through quizzes, exercises and assignments during the course
Pre-requirements
Contents
Descriptive statistics: population and samples; types of variables; basic graphical representations and summaries for numerical variables and factors; relationship between two factors and the Chi-squared statistics; relationship between two numerical variables, correlation and regression.
Probability: sample space, events and probability; independence; discrete and continuous random variables; the binomial and the normal distributions.
Inference: sample distributions; estimation of the mean and the standard deviation of a population; confidence intervals; hypotheses testing and p-values.
Referral texts
Assessment methods
Among the activities proposed during the classes there will be quizzes to assess understanding of basic concepts and group works. Active participation in these activities is evaluated 7 points maximum.
The final exam (26 points) is a Moodle quiz composed by exercises similar to those solved or assigned during the course. The quiz is an open book exam, so that it is possible to use all the material in Moodle and R for the calculations. An example of exam will be made available in Moodle.
Students with a grade in the written exam equal or higher than 22, may ask to improve their final grade with an oral exam. The oral exam substitutes the activities during the course, it is about the whole program of the course and may increase or decrease the grade in the written exam.
Type of exam
Grading scale
- adequate ability to use specific knowledge for data analysis
- sufficient ability to apply the acquired knowledge in a specific context, identifying the most appropriate probabilistic models and methods
- limited ability to critically interpret the obtained results
- sufficient communication skills, using rigorous formulas and appropriate terminology
2. Scores in the range of 22-26 will be assigned when:
- good ability to use specific knowledge for data analysis
- adequate ability to apply the acquired knowledge in a specific context, identifying the most appropriate probabilistic models and methods
- sufficient ability to critically interpret the obtained results
- adequate communication skills, using rigorous formulas and appropriate terminology
3. Scores in the range of 26-30 will be assigned when:
- excellent ability to use specific knowledge for data analysis
- good or excellent ability to apply the acquired knowledge in a specific context, identifying the most appropriate probabilistic models and methods
- good ability to critically interpret the obtained results
- good or excellent communication skills, using rigorous formulas and appropriate terminology
4. Honors will be granted to students in the range 3. that have participated with commitment and interest to the activities during the course or that have shown excellent mastery of the subject.