Statistics
A.Y. 2021/2022
Learning objectives
The main objective of the course is to ensure that students acquire an adequate knowledge and degree of understanding of the appropriate tools to synthetically describe one or more characters of interest that are found in the most various fields (political, administrative, sociological, historical, legal, economic, etc.). This description can be made by aggregating the data observed in tables, giving an adequate graphical representation, constructing appropriate position and variability indices, identifying the most appropriate measures that highlight the relationships. The statistical description must be accompanied by statistical induction, when the survey is not total but partial; in this case, the knowledge of the aforesaid characters is not in "certain" terms but only "probable" and has the purpose of providing indications on the entire population of reference. The basic topics of Probability and Statistical inference are therefore provided. Knowledge and understanding of these tools require a strong application capacity. Students will have to develop a marked independence of judgment, in order to be able to adequately choose the most suitable techniques for solving the proposed problems, and they will have to demonstrate that they also possess communication skills, essential to be able to explain the methodologies and logical paths used in solving the questions. Finally, they must acquire a more and more refined learning ability, which will allow them to face new situations with a high degree of autonomy.
Expected learning outcomes
At the end of this course, the student is expected to know and use the main statistical tools necessary for the analysis of phenomena in different fields (social, economic, etc) and in their various manifestations. The student will be able to organize the observed data of one or more phenomena of interest in a frequency or contingency table, to synthesize their main features and relationships through appropriate univariate or bivariate indices, and to infer more general results about the population from the observed sample data.
Lesson period: Activity scheduled over several sessions (see Course syllabus and organization section for more detailed information).
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
A-K
Lesson period
Second trimester
For the training activity in the academic year 2021/22, more specific information will be provided in the coming months, based on the evolution of the public health situation.
Course syllabus
Statistics: definition and fields of application.
The classification of statistical phenomena and the concept of reference statistical population.
The description of statistical data.
Organization of data in frequency tables and graphical representation.
Position indexes: mode, median, quantiles, arithmetic mean
Dispersion indexes: range, variance and standard deviation, coefficient of variation.
The concept of random event, probability of an event and hints about random variables
Bernoulli and Binomial random variables.
The Normal (or Gaussian) random variable and the use of statistical tables.
Sampling and sampling distributions.
Introduction to sample estimation.
Hypothesis testing for one or more samples.
Regression model in a deterministic and inferential context.
Multiple regression model.
ANOVA: analysis of variance
The classification of statistical phenomena and the concept of reference statistical population.
The description of statistical data.
Organization of data in frequency tables and graphical representation.
Position indexes: mode, median, quantiles, arithmetic mean
Dispersion indexes: range, variance and standard deviation, coefficient of variation.
The concept of random event, probability of an event and hints about random variables
Bernoulli and Binomial random variables.
The Normal (or Gaussian) random variable and the use of statistical tables.
Sampling and sampling distributions.
Introduction to sample estimation.
Hypothesis testing for one or more samples.
Regression model in a deterministic and inferential context.
Multiple regression model.
ANOVA: analysis of variance
Prerequisites for admission
No prior knowledge is required
Teaching methods
Lectures and classroom exercises.
During the lectures the teacher uses both the blackboard and slides to be shown on the PC. Lectures focus on the most theoretical issues but are always accompanied by numerical examples.
During the exercises the teacher, after possibly recalling the necessary theoretical references seen in class, solves numerical exercises, which require the use of the scientific calculator and the statistical tables.
During the lectures the teacher uses both the blackboard and slides to be shown on the PC. Lectures focus on the most theoretical issues but are always accompanied by numerical examples.
During the exercises the teacher, after possibly recalling the necessary theoretical references seen in class, solves numerical exercises, which require the use of the scientific calculator and the statistical tables.
Teaching Resources
Statistica, Iacus, McGraw-Hill.
Chapters
1. Il mondo aleatorio
2. Il mondo dei dati
3. Modelli probabilistici a fini previsivi
4. Inferenza statistica
5. Relazioni tra più fenomeni
Chapters
1. Il mondo aleatorio
2. Il mondo dei dati
3. Modelli probabilistici a fini previsivi
4. Inferenza statistica
5. Relazioni tra più fenomeni
Assessment methods and Criteria
Written exam, with an optional mid-term exam.
The complete written exam, lasting 90 minutes, consists of (4+4) multiple choice questions with 4 possible answers, of which only one is correct; and (2+2) numerical exercises. The structure of the exam allows the student to check the theoretical and practical skills learned by the students during the lessons and exercises.
The optional mid-term test consists of a 45-minute written exam with the same structure as the full exam, referred to the part of the program carried out up to the date of the test (4+2).
If the mid-term exam is passed, it allows the student to perform a final 45-minute test on the second part of the course in any of the 6 official rounds. If the second test is also passed, the student passes the exam (but can decide to retake the second test or the complete exam if he/she is not satisfied with the final grade).
If the first mid-term exam is not passed, the student will have to take the complete written exam in one of the official rounds.
The complete written exam, lasting 90 minutes, consists of (4+4) multiple choice questions with 4 possible answers, of which only one is correct; and (2+2) numerical exercises. The structure of the exam allows the student to check the theoretical and practical skills learned by the students during the lessons and exercises.
The optional mid-term test consists of a 45-minute written exam with the same structure as the full exam, referred to the part of the program carried out up to the date of the test (4+2).
If the mid-term exam is passed, it allows the student to perform a final 45-minute test on the second part of the course in any of the 6 official rounds. If the second test is also passed, the student passes the exam (but can decide to retake the second test or the complete exam if he/she is not satisfied with the final grade).
If the first mid-term exam is not passed, the student will have to take the complete written exam in one of the official rounds.
SECS-S/01 - STATISTICS - University credits: 9
Lessons: 60 hours
Professor:
Siletti Elena
L-Z
Lesson period
Third trimester
SECS-S/01 - STATISTICS - University credits: 9
Lessons: 60 hours
Professor:
Ballante Elena