STATISTICS
STATISTICA
| A.Y. | Credits |
|---|---|
| 2022/2023 | 6 |
| Lecturer | Office hours for students | |
|---|---|---|
| Nicola Maria Rinaldo Loperfido | Half an hour after each class, during class times. During other times of the academic year the student time is agreed with the lectures using e-mail messages. |
| Teaching in foreign languages |
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Course with optional materials in a foreign language
English
This course is entirely taught in Italian. Study materials can be provided in the foreign language and the final exam can be taken in the foreign language. |
Assigned to the Degree Course
| Date | Time | Classroom / Location |
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| Date | Time | Classroom / Location |
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Learning Objectives
The course introduces the main descriptive statistics, which are more and more used both in business and economics studies. These concepts constitute the basis of more advanced and applied statistical methods, as for example data mining, big data and web scraping. Theory will be illustrated by means of real data ets which will be discussed during the lectures. The statistical concepts are introduced with simple examples, informally described and then formally defined by means of definitions. Their theoretical properties will be stated. The practical applications will be critically examined.
Program
1. Univariate descriptive statistics. Introductory concepts (population, sample, case, variable), distributions (simple, frequencies, densities), measures of location (mean, mode, median), measures of scatter (variance, entropy, concentration), measures of shape (skewness, kurtosis), graphical displays (hystogram, Pareto chart, box-plot).
2. Bivariate descriptive statistics. Bivariate distributions (contingency tables, scatter plot, stereogram), association (expected frequencies, contingencies, chi-square, joint entropy), concordance (Kendall's tau, Spearman's rho), regression (group means, conditional entropy, Goodman and Kruskal lambda), simple linear regression (definition, residuals, variants).
Bridging Courses
Mathematics
Learning Achievements (Dublin Descriptors)
1. Knowledge and under standing. The student will know the basic statistical methods and their use in marketing strategies.
2. Applying knowledge and understanding. The student will be able to explore data sets and detect their latent structures.
3. Making judgements. The student will be able to choose the most appropriate methods for data exploration and to evaluate the quality of the obtained results.
4. Communication skills. The student will learn to communicate the results of the exploratory analyses by means of graphs, tables, slides and reports.
5. Learning skills. The student will be able to connect the contents of the course with the methods learnt in other courses or by self-teaching.
Teaching Material
The teaching material prepared by the lecturer in addition to recommended textbooks (such as for instance slides, lecture notes, exercises, bibliography) and communications from the lecturer specific to the course can be found inside the Moodle platform › blended.uniurb.it
Supporting Activities
Notes written by the lectures. They include solutions to exercises, worked exercises, summary schemes, examination rules, fake exams.
Teaching, Attendance, Course Books and Assessment
- Teaching
1. Classes. Presentation of the theory, discussion of real data sets, informal checking of learning progresses. Teching is interactive, in order to motivate student participation.
2. Office hours. While classes are given, there are weekly office hours, whose time is fixed before the classes themselves. When there are no classes office hours are decided together with the student by e-mail.
- Innovative teaching methods
Flipped classroom
- Attendance
Class attendance is not mandatory but it is recommended.
- Course books
Notes written by the lectures. They include solutions to exercises, worked exercises, summary schemes, examination rules, fake exams.
- Assessment
Thirty exercises chosen from the teacheing material. Each exercise with a correcct solution corresponds to a point. The sum of points constitutes the final mark. The student has one hour at her/his disposal for the exam.
- Disabilità e DSA
Le studentesse e gli studenti che hanno registrato la certificazione di disabilità o la certificazione di DSA presso l'Ufficio Inclusione e diritto allo studio, possono chiedere di utilizzare le mappe concettuali (per parole chiave) durante la prova di esame.
A tal fine, è necessario inviare le mappe, due settimane prima dell’appello di esame, alla o al docente del corso, che ne verificherà la coerenza con le indicazioni delle linee guida di ateneo e potrà chiederne la modifica.
Additional Information for Non-Attending Students
- Teaching
Individual study and Office hours. While classes are given, there are weekly office hours, whose time is fixed before the classes themselves. When there are no classes office hours are decided together with the student by e-mail.
- Attendance
Class attendance is not mandatory but it is recommended.
- Course books
Notes written by the lectures. They include solutions to exercises, worked exercises, summary schemes, examination rules, fake exams.
- Assessment
Written exam based on questions and exercises from the teaching material.
- Disabilità e DSA
Le studentesse e gli studenti che hanno registrato la certificazione di disabilità o la certificazione di DSA presso l'Ufficio Inclusione e diritto allo studio, possono chiedere di utilizzare le mappe concettuali (per parole chiave) durante la prova di esame.
A tal fine, è necessario inviare le mappe, due settimane prima dell’appello di esame, alla o al docente del corso, che ne verificherà la coerenza con le indicazioni delle linee guida di ateneo e potrà chiederne la modifica.
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