Contribution of the course to the overall degree programme goals
This course focusses on data analysis, probability theory and statistical inference following a concept-driven approach. Students will learn the main practical concepts that are widely applied in economics and social sciences, when professionals and researchers need to collect and analyse data in order to take decisions or make predictions. The technical use of mathematics will be kept at a very basic level, in order to introduce undergraduate students to probabilitic reasoning and statistical methods.
Expected learning outcomes
The course aims at introducing students to the probabilistic and statistical tools used in the analysis and in the interpretation of empirical evidence in economics and in the social sciences, in order to allow them to interact with researchers and practitioners working in those fields.
Basic knowledge of mathematics (middle school level)
1. Statistics: scopes and methods. Data description: graphical and numerical summaries. Data collection.
2. Probability, probability distributions, sampling distributions.
3. Statistical inference: point and interval estimation.
4. Statistical inference: testing statistical hypothesis.
5. Association between variables: contingency and regression.
Agresti A., Franklin C. (2013): Statistics. The art and science of learning from data. Pearson. Chapters 1-10; Chapter 11 (sections 11.1-11.3); Chapter 12 (sections 12.1-12-3); Chapter 13 (sections 13.1-13.3)
Students must pass a written final exam consisting of questions and excercises. No book will be allowed. The exam evaluates the knowledge and the understanding of the main models amd methods presented during the course.
The professor will use interactive lecture-style presentations and students will be required to actively participate.
Students are invited to register to the course on Moodle platform (moodle.unive.it), where they can find additional meterial.
Last update of the programme