STATISTICAL TOOLS FOR DECISION-MAKING
Syllabus
EN
IT
Learning Objectives
The course is designed to provide students with a solid foundation in statistical methods, crucial for analyzing data and making informed decisions in various fields such as business, economics, and the social sciences. By the end of the course, students will acquire a comprehensive understanding of descriptive statistics, enabling them to summarize and interpret data through measures of central tendency, variability, and distribution.
They will also be introduced to probability theory, learning to assess uncertainty and model random events, which is essential for real-world decision-making under risk. In the section on inferential statistics, students will dive deeper into estimation methods, focusing on maximum likelihood estimation and least squares. These techniques are fundamental for developing predictive models and making estimations about populations based on sample data.
Moreover, a significant portion of the course will be dedicated to the introduction and practical use of R, one of the most widely used software tools in data analysis and statistical computing. Students will develop proficiency in data manipulation, performing statistical tests, generating visualizations, and applying advanced statistical models. Mastery of R will enable them to not only understand theoretical concepts but also apply them to real datasets, giving them hands-on experience with real-world applications and preparing them for data-driven decision-making in their future careers.
They will also be introduced to probability theory, learning to assess uncertainty and model random events, which is essential for real-world decision-making under risk. In the section on inferential statistics, students will dive deeper into estimation methods, focusing on maximum likelihood estimation and least squares. These techniques are fundamental for developing predictive models and making estimations about populations based on sample data.
Moreover, a significant portion of the course will be dedicated to the introduction and practical use of R, one of the most widely used software tools in data analysis and statistical computing. Students will develop proficiency in data manipulation, performing statistical tests, generating visualizations, and applying advanced statistical models. Mastery of R will enable them to not only understand theoretical concepts but also apply them to real datasets, giving them hands-on experience with real-world applications and preparing them for data-driven decision-making in their future careers.
Prerequisites
No formal pre-requisites
Program
Topic 1 - Descriptive statistics: types of data; graphical representations; means; variability; contingency tables; correlation; simple linear regression.
Topic 2 - Probability: introduction to probability theory and elementary probability rules; random variables; common families of distributions; sampling distributions.
Topic 3 - Statistical inference: point estimation; confidence intervals; hypothesis testing; introduction to multiple linear regression. Applications in R.
Topic 4 - Introduction to the statistical software R: syntax, functions, and graphical procedures.
During each lesson, the instructor presents the planned content with the help of slides and interacts with the students, encouraging critical thinking and dialogue
Topic 2 - Probability: introduction to probability theory and elementary probability rules; random variables; common families of distributions; sampling distributions.
Topic 3 - Statistical inference: point estimation; confidence intervals; hypothesis testing; introduction to multiple linear regression. Applications in R.
Topic 4 - Introduction to the statistical software R: syntax, functions, and graphical procedures.
During each lesson, the instructor presents the planned content with the help of slides and interacts with the students, encouraging critical thinking and dialogue
Books
1. Slides of the course.
2. Alan Agresti, Christine Franklin, “Statistics: The Art and Science of Learning from Data” Pearson; 4th International Edition, ISBN 9781447964186.
2. Alan Agresti, Christine Franklin, “Statistics: The Art and Science of Learning from Data” Pearson; 4th International Edition, ISBN 9781447964186.
Bibliography
Alan Agresti, Christine Franklin, “Statistics: The Art and Science of Learning from Data” Pearson; 4th International Edition, ISBN 9781447964186.
W. N. Venables, D. M. Smith, “An Introduction to R”, Version 4.3.1 (2023-06-16)
W. N. Venables, D. M. Smith, “An Introduction to R”, Version 4.3.1 (2023-06-16)
Teaching methods
The course offers a well-balanced combination of theoretical and practical sessions, allowing students to develop both a solid conceptual foundation and applied skills. During the practical sessions, students will have the opportunity to apply the theoretical tools they have learned by using the R software, under the careful supervision of the instructor. Interactivity is a key component of every lesson: theoretical concepts are continuously enhanced with practical examples, case studies, and real-world applications, encouraging active student participation. The course fosters dialogue and exchange between students and the instructor, creating a dynamic and collaborative environment that promotes a deep understanding of the topics covered and the development of critical skills in the field of statistics.
Exam Rules
Midterm exam
There will be one elective midterm (written) exam, consisting of theoretical questions and exercises. It is highly recommended to attend the midterm exam. The final grade will consider both the results of the midterm and the final exams. The grade of the midterm exam CANNOT be rejected; it must be accepted. Only the final grade can be rejected. In that case, the student must repeat the (oral) final exam on the whole program.
Students who failed or did not attend the midterm exam will be evaluated only through the final oral exam.
Final exam
The final exam will consist of a written test and a mandatory oral test only for those with a grade higher than 26/30 on the written exam. For those who have taken the midterm, the written test will cover only the second part of the syllabus; while for those who have not taken the midterm, the test will cover the entire syllabus. Both the final and midterm exams will take place in person.
Non-attending students (with less than 80% attendance and no midterm taken) can choose when to enroll, either in the first or second round, covering the whole program.
Grading criteria:
Unsuitable: significant deficiencies and/or inaccuracies in knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalizations, and limited critical and judgment skills; topics are presented incoherently and with inappropriate language.
18-20: barely sufficient knowledge and understanding of the topics, with possible generalizations and imperfections; sufficient capacity for analysis, synthesis, and independent judgment; topics are often presented incoherently and with inappropriate/technical language.
21-23: surface knowledge and understanding of the topics; ability to analyze and synthesize correctly with sufficiently coherent argumentation and appropriate/technical language.
24-26: fair knowledge and understanding of the topics; good analytical and synthetic skills with rigorously presented arguments, though not always with appropriate/technical language.
27-29: complete knowledge and understanding of the topics; considerable capacity for analysis and synthesis. Good independent judgment. Arguments presented in a rigorous manner and with appropriate/technical language.
30-30L: very good level of knowledge and thorough understanding of the topics. Excellent analytical and synthetic skills, with independent judgment. Arguments are expressed in an original manner and in appropriate technical language.
There will be one elective midterm (written) exam, consisting of theoretical questions and exercises. It is highly recommended to attend the midterm exam. The final grade will consider both the results of the midterm and the final exams. The grade of the midterm exam CANNOT be rejected; it must be accepted. Only the final grade can be rejected. In that case, the student must repeat the (oral) final exam on the whole program.
Students who failed or did not attend the midterm exam will be evaluated only through the final oral exam.
Final exam
The final exam will consist of a written test and a mandatory oral test only for those with a grade higher than 26/30 on the written exam. For those who have taken the midterm, the written test will cover only the second part of the syllabus; while for those who have not taken the midterm, the test will cover the entire syllabus. Both the final and midterm exams will take place in person.
Non-attending students (with less than 80% attendance and no midterm taken) can choose when to enroll, either in the first or second round, covering the whole program.
Grading criteria:
Unsuitable: significant deficiencies and/or inaccuracies in knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalizations, and limited critical and judgment skills; topics are presented incoherently and with inappropriate language.
18-20: barely sufficient knowledge and understanding of the topics, with possible generalizations and imperfections; sufficient capacity for analysis, synthesis, and independent judgment; topics are often presented incoherently and with inappropriate/technical language.
21-23: surface knowledge and understanding of the topics; ability to analyze and synthesize correctly with sufficiently coherent argumentation and appropriate/technical language.
24-26: fair knowledge and understanding of the topics; good analytical and synthetic skills with rigorously presented arguments, though not always with appropriate/technical language.
27-29: complete knowledge and understanding of the topics; considerable capacity for analysis and synthesis. Good independent judgment. Arguments presented in a rigorous manner and with appropriate/technical language.
30-30L: very good level of knowledge and thorough understanding of the topics. Excellent analytical and synthetic skills, with independent judgment. Arguments are expressed in an original manner and in appropriate technical language.