ID:
500026
Duration (hours):
66
CFU:
9
SSD:
STATISTICA
Year:
2025
Overview
Date/time interval
Secondo Semestre (16/02/2026 - 23/05/2026)
Syllabus
Course Objectives
The course aims at illustrating the foundations of Statistics also through statistical learning based on data analysis.
In particular, the course is addressed to provide the basic tools of Statistics which can be used by students in order to reach the understanding and the solving of different problems such as, for instance, those related to the economic and business areas.
To this purpose, the main basic topics will be focused on:
1) descriptive statistics;
2) probability;
3) statistical inference.
With regard to point 1), we will proceed to:
a) provide a specific classification of the different types of variables under study based on their nature, differing between qualitative and quantitative variables which in turn are further classified into nominal or ordinal qualitative variables and discrete or continuous quantitative variables;
b) introduce the main central tendency measures (mean, mode, median, quartiles) and variability measures (variance, standard deviation, range, etc).
With regard point 2), we will proceed to:
a) introduce the notion of classical probability as well as the concepts concerning the probability of union and intersection events;
b) illustrate the main probability distributions, considering the cases of discrete probability distributions (Binomial and Poisson probability distribution) and continuous probability distributions (Normal distribution).
With regard to point 3), we will proceed to:
a) define the concept of sampling distributions;
b) introduce the Central Limit Theorem;
c) specify the notion of confidence intervals for the unknown population mean in the different scenarios (known or unknown population variance) and for the unknown population proportion;
d) provide the construction of statistical tests for the unknown mean, variance and proportion of the population both in the univariate case and in the bivariate case (intended as the comparison between the means, variances and proportions of two different populations).
Finally, we will proceed to discuss the functional association existing between two variables and explained through the construction of the simple linear regression model in turn based on the correlation notion.
The linear regression model will also be extended to the multivariate case. Finally, classification models will be also introduced.
At the end of the course the student must have achieved the required skills to: with reference to point 1), provide a descriptive summary of the data using synthetic indicators; with reference to point 2), proceed to the calculation of the probability of simple or compound events by evaluating which type of the probability distributions presented along the course is the most appropriate; with reference to pint 3), solve inferential problems with the aim of stating whether the conclusions related to the sample under study can be extended to the whole population.
Finally, the student must be able to apply linear and non-linear statistical models (and the related measures of fit and significance) together with classification models.
Furthermore, the student must be able to express the obtained results through the proper statistical language.
In particular, the course is addressed to provide the basic tools of Statistics which can be used by students in order to reach the understanding and the solving of different problems such as, for instance, those related to the economic and business areas.
To this purpose, the main basic topics will be focused on:
1) descriptive statistics;
2) probability;
3) statistical inference.
With regard to point 1), we will proceed to:
a) provide a specific classification of the different types of variables under study based on their nature, differing between qualitative and quantitative variables which in turn are further classified into nominal or ordinal qualitative variables and discrete or continuous quantitative variables;
b) introduce the main central tendency measures (mean, mode, median, quartiles) and variability measures (variance, standard deviation, range, etc).
With regard point 2), we will proceed to:
a) introduce the notion of classical probability as well as the concepts concerning the probability of union and intersection events;
b) illustrate the main probability distributions, considering the cases of discrete probability distributions (Binomial and Poisson probability distribution) and continuous probability distributions (Normal distribution).
With regard to point 3), we will proceed to:
a) define the concept of sampling distributions;
b) introduce the Central Limit Theorem;
c) specify the notion of confidence intervals for the unknown population mean in the different scenarios (known or unknown population variance) and for the unknown population proportion;
d) provide the construction of statistical tests for the unknown mean, variance and proportion of the population both in the univariate case and in the bivariate case (intended as the comparison between the means, variances and proportions of two different populations).
Finally, we will proceed to discuss the functional association existing between two variables and explained through the construction of the simple linear regression model in turn based on the correlation notion.
The linear regression model will also be extended to the multivariate case. Finally, classification models will be also introduced.
At the end of the course the student must have achieved the required skills to: with reference to point 1), provide a descriptive summary of the data using synthetic indicators; with reference to point 2), proceed to the calculation of the probability of simple or compound events by evaluating which type of the probability distributions presented along the course is the most appropriate; with reference to pint 3), solve inferential problems with the aim of stating whether the conclusions related to the sample under study can be extended to the whole population.
Finally, the student must be able to apply linear and non-linear statistical models (and the related measures of fit and significance) together with classification models.
Furthermore, the student must be able to express the obtained results through the proper statistical language.
Course Prerequisites
Although there are no formal prerequisites, it is suggested to have a basic knowledge of the main topics concerning the general Math course in order to facilitate the understanding and formalisation of the contents proposed in the Statistics course.
Teaching Methods
The teaching methods for the Statistics course consist of:
• face-to-face lessons, where the topics of the course will be addressed from a theoretical and practical point of view through the illustration of examples. In particular, the face-to-face lessons will be supported by the use of Power Point slides (made available through the e-learning KIRO platform) and by the use of the blackboard for in-depth analysis relating to calculation developments and the formalisation of the theoretical aspects;
• in-class exercises, typically carried out on the last day of lectures of the week. Exercises will be proposed in relation to the topics faced along each week: the exercises will be carried out by the teacher using the blackboard in order to allow students to follow and understand the single steps which lead to the final solution.
Additional exercises to be carried out individually will be suggested on the e-learning KIRO platform. These exercises will be solved during the tutoring of the following week.
Suggested exercises will be also uploaded on the e-learning KIRO platform. These exercises should be carried out individually by the students. They will be solved during the tutorial of the following week.
• face-to-face lessons, where the topics of the course will be addressed from a theoretical and practical point of view through the illustration of examples. In particular, the face-to-face lessons will be supported by the use of Power Point slides (made available through the e-learning KIRO platform) and by the use of the blackboard for in-depth analysis relating to calculation developments and the formalisation of the theoretical aspects;
• in-class exercises, typically carried out on the last day of lectures of the week. Exercises will be proposed in relation to the topics faced along each week: the exercises will be carried out by the teacher using the blackboard in order to allow students to follow and understand the single steps which lead to the final solution.
Additional exercises to be carried out individually will be suggested on the e-learning KIRO platform. These exercises will be solved during the tutoring of the following week.
Suggested exercises will be also uploaded on the e-learning KIRO platform. These exercises should be carried out individually by the students. They will be solved during the tutorial of the following week.
Assessment Methods
The learning assessment methods will be based on a general written test or, alternatively, two partial written tests (one halfway through the course, the other one at the end of the course). The tests will consist of exercises related to the program.
In the case of two partials each one will consist of 3 exercises and each score will weigh 50% of the final grade.
The intermediate test will last 1 hour and will be made up of 3 written exercises similar to those carried out in class. The grade will be between 0 and 30 with honors, and each exercise will weight from 8 to 12 points depending on the difficulties.
The final test will last 1 hour and will consist of 3 written exercises on the second part and similar to those carried out in class. The final grade will be in the range between 0 and 30 with honors and each exercise will weight from 8 to 12 points depending on the difficulties. The final score (including the intermediate and final tests) will be determined as the simple average of the two previous scores.
If the intermediate test is not taken, the final exam will last 1 hour and half and will consist of 5 written exercises covering the entire program. Each exercise will weight from 4 to 8 points depending on the difficulties. The exam grade will be in the range between 0 and 30 with honors, with the test being passed upon reaching 18/30. It will be possible to use the calculator and the quantile tables of the fundamental distributions which will be directly provided by the teachers during the exam.
The grades will be announced via the e-learning KIRO platform or ESSE3.
Texts
The reference book containing the course's topics is: Pelosi M.K, Sandifer T.M, Cerchiello P., Giudici P.: Statistica. INTRODUZIONE ALLA STATISTICA. IMPARARE DAI DATI, McGraw-Hill, 3rd Edition (2025). Any additional teaching material will be provided by the teacher by means of the course's e-learning KIRO platform.
Contents
The topics covered in the Statistics course are structured as follows: 1. Univariate analysis;
2. Probability
; 3. Probability distributions
; 4. Sampling distributions and confidence intervals;
5. Hypothesis testing;
6. Univariate Hypothesis Testing;
7. Bivariate Hypothesis Testing; 8. Bivariate analysis of the data
;
9. Simple linear regression models
; 10. Multiple linear regression models
; 11. Classification models.
Course Language
Italian
More information
Students enrolled in the Inclusive Learning Modalities programme (“Modalità didattiche inclusive") are requested to contact the Professor and the Degree Course Coordinator in order to assess specific needs and define targeted support actions.
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