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 simple/multiple linear regression. Applications in R.
Topic 4 - Introduction to the statistical software R: syntax, functions, and graphical procedures.
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 simple/multiple linear regression. Applications in R.
Topic 4 - Introduction to the statistical software R: syntax, functions, and graphical procedures.
Books
1. Slides of the course (+ additions during the lectures)
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”
W. N. Venables, D. M. Smith, “An Introduction to R”
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 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 practical exercises. It is highly recommended to attend the midterm exam.
The midterm consists of 15 multiple-choice questions and 2 open questions, to be answered in 1 hour and will cover all topics taught up to the exam date.
The midterm exam is open to both attending and non-attending students, but with different modalities: attending students can choose 10 out of 15 multiple-choice questions and reply only to those. Instead, for full points, non-attending students should reply to all 15 multiple-choice questions. The 2 open questions are expected to be answered by all students.
Note: The status of attendance or non-attendance is computed based on the number of hours of the course already taught as of the date of the midterm. So students are non-attending if they attended less than 80% of the hours of lectures up to the date of the midterm.
The midterm exam is considered PASSED in case of a positive grade (> or = 18), FAILED in case of a negative grade (<18), or in case of absence or withdrawal – this applies to both attending and non-attending students.
The final grade will consider both the results of the midterm and of the final exam. 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 final exam on the whole program.
Students who failed or did not attend the midterm exam will be evaluated only through the final exam.
FINAL EXAM
The final exam will be a written test. For those who have done and passed the midterm, the written test will have questions only on the second part of the program, while for those who have not done or not passed the midterm, the test will be based on the whole program. Both the final and midterm exams will be in person.
The final exam will have the same structure as the midterm: 15 multiple-choice + 2 open questions (1 hour). However, it differs according to whether the student is attending or not and has passed the midterm or not.
For the students who have PASSED the midterm
If they are ATTENDING: they will take a written exam only on the remaining topics taught after the midterm exam. Moreover, they can choose 10 out of 15 multiple-choice questions and reply only to those.
If they are NON-ATTENDING: they will take a written exam only on the remaining topics taught after the midterm exam. However, for full points, they should reply to all 15 multiple-choice questions.
For the students who have FAILED the midterm
If they are ATTENDING: they will take a written exam on the entire program of the course. But, they can choose 10 out of 15 multiple-choice questions and reply only to those.
If they are NON-ATTENDING: they will take a written exam on the entire program of the course. Furthermore, for full points, they should reply to all 15 multiple-choice questions.
The examination will be graded according to the following criteria:
Unsuitable: important deficiencies and/or inaccuracies in the knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalisations and limited critical and judgment skills; the topics are exposed in an incoherent manner and with inappropriate language.
18-20: barely sufficient knowledge and understanding of the topics, with possible generalisations and imperfections; sufficient capacity for analysis, synthesis and autonomy of judgement; the topics are frequently exposed in an inconsistent manner and with inappropriate/technical language;
21-23: surface knowledge and understanding of the topics; ability to analyse and synthesise correctly with sufficiently coherent logical argumentation and appropriate/technical language.
24-26: fair knowledge and understanding of the topics; good analytical and synthetic skills with rigorously expressed arguments but not always appropriate/technical language.
27-29: complete knowledge and understanding of the topics; considerable capacity for analysis and synthesis. Good autonomy of judgement. Arguments presented in a rigorous manner and with appropriate/technical language.
30-30L: very good level of knowledge and thorough understanding of topics. Excellent analytical and synthetic skills and independent judgement. Arguments expressed in an original manner and in appropriate technical language.
There will be one elective midterm written exam, consisting of theoretical questions and practical exercises. It is highly recommended to attend the midterm exam.
The midterm consists of 15 multiple-choice questions and 2 open questions, to be answered in 1 hour and will cover all topics taught up to the exam date.
The midterm exam is open to both attending and non-attending students, but with different modalities: attending students can choose 10 out of 15 multiple-choice questions and reply only to those. Instead, for full points, non-attending students should reply to all 15 multiple-choice questions. The 2 open questions are expected to be answered by all students.
Note: The status of attendance or non-attendance is computed based on the number of hours of the course already taught as of the date of the midterm. So students are non-attending if they attended less than 80% of the hours of lectures up to the date of the midterm.
The midterm exam is considered PASSED in case of a positive grade (> or = 18), FAILED in case of a negative grade (<18), or in case of absence or withdrawal – this applies to both attending and non-attending students.
The final grade will consider both the results of the midterm and of the final exam. 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 final exam on the whole program.
Students who failed or did not attend the midterm exam will be evaluated only through the final exam.
FINAL EXAM
The final exam will be a written test. For those who have done and passed the midterm, the written test will have questions only on the second part of the program, while for those who have not done or not passed the midterm, the test will be based on the whole program. Both the final and midterm exams will be in person.
The final exam will have the same structure as the midterm: 15 multiple-choice + 2 open questions (1 hour). However, it differs according to whether the student is attending or not and has passed the midterm or not.
For the students who have PASSED the midterm
If they are ATTENDING: they will take a written exam only on the remaining topics taught after the midterm exam. Moreover, they can choose 10 out of 15 multiple-choice questions and reply only to those.
If they are NON-ATTENDING: they will take a written exam only on the remaining topics taught after the midterm exam. However, for full points, they should reply to all 15 multiple-choice questions.
For the students who have FAILED the midterm
If they are ATTENDING: they will take a written exam on the entire program of the course. But, they can choose 10 out of 15 multiple-choice questions and reply only to those.
If they are NON-ATTENDING: they will take a written exam on the entire program of the course. Furthermore, for full points, they should reply to all 15 multiple-choice questions.
The examination will be graded according to the following criteria:
Unsuitable: important deficiencies and/or inaccuracies in the knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalisations and limited critical and judgment skills; the topics are exposed in an incoherent manner and with inappropriate language.
18-20: barely sufficient knowledge and understanding of the topics, with possible generalisations and imperfections; sufficient capacity for analysis, synthesis and autonomy of judgement; the topics are frequently exposed in an inconsistent manner and with inappropriate/technical language;
21-23: surface knowledge and understanding of the topics; ability to analyse and synthesise correctly with sufficiently coherent logical argumentation and appropriate/technical language.
24-26: fair knowledge and understanding of the topics; good analytical and synthetic skills with rigorously expressed arguments but not always appropriate/technical language.
27-29: complete knowledge and understanding of the topics; considerable capacity for analysis and synthesis. Good autonomy of judgement. Arguments presented in a rigorous manner and with appropriate/technical language.
30-30L: very good level of knowledge and thorough understanding of topics. Excellent analytical and synthetic skills and independent judgement. Arguments expressed in an original manner and in appropriate technical language.
Attendance Rules
Attendance is required from the very first lesson and it is necessary to attend at least 80% of the course to be considered an attending student.