INTRODUCTION TO PROBABILITY FOR ECONOMICS

This course offers an introductory overview of descriptive statistics, probability theory, and statistical inference. It is designed to equip undergraduate students with practical concepts commonly used in scientific research. These concepts are essential for professionals and researchers who collect and analyze data to make informed decisions and predictions. The course strikes a balance by keeping mathematical complexity at a basic level while introducing students to probabilistic reasoning and statistical methods.

The course aims to introduce students to probabilistic and statistical tools for analyzing and interpreting empirical evidence in scientific research, preparing them to collaborate effectively with researchers and practitioners across various fields.

Students are expected to have a basic knowledge of mathematics at a middle school level.

1. Descriptive statistics: graphical and numerical summaries; regression.
2. Probability, random variables and sampling distributions.
3. Statistical inference: interval estimation and hypothesis testing.

Agresti A., Franklin C., and Klingenberg B. (2017): Statistics. The art and science of learning from data. Pearson. Chapters 1-9.

The final exam is a 60-minute written test consisting of multiple-choice questions. Examples of written tests are available in the dedicated course area on the university's Moodle e-learning platform. The maximum score for this test is 30/30.
All students who pass the written test (by obtaining a score of no less than 18/30) have the option to request an oral integration. For students who achieve a score of no less than 27/30 on the written exam, the oral exam is mandatory.
The exam aims to assess: i) the understanding of the fundamental concepts; ii) the ability to solve simple exercises; iii) the capacity for logical reasoning and the appropriate use of statistical and probabilistic terminology.

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The final grade is based on both written and oral exam performance, evaluating:
a) understanding of key concepts;
b) reasoning and argumentation skills;
c) correct use of statistical-probabilistic language (including formal notation).
Grading Scale:
- 18–20: Barely sufficient, with several conceptual gaps;
- 21–23: Sufficient, meets minimum expectations;
- 24–26: Good understanding and application;
- 27–30: Very good to excellent performance;
Honors ("lode") will be awarded only to those who exhibit exceptional judgment and an in-depth understanding of the topics covered.

The course will be presented in a lecture-style format.

Students are encouraged to register for the course on the Moodle platform (moodle.unive.it), where they can find supplementary materials.