Bachelor Degree in Business Administration and Economics

Updated A.Y. 2021-2022

Course Program 

The course is structured into the following sub-parts:

Part I: Descriptive Analysis
Introduction to Statistics
Types of data and variables
Summarizing and graphic data

Part II: Probability
Experiments, outcomes, sample spaces and probability axioms
Marginal and conditional probability
Independent events
Main probability distributions: Bernoulli, Binomial, Normal, Chi-Square, t-statistics, F

Part III: Inference
Estimators
Hypothesis testing

Part IV: Regression analysis
Simple and multiple inear regression model: Assumptions and properties

This module (Module I) will cover Part I and Part II. A detailed Syllabus is available on the course's website.

Course Methodology

The course offers two weekly classes, aimed at illustrating the theoretical foundations of statistical instruments and at implementing via examples and exercises.  A devoded weekly appointment will be devoted to practice with exercises and problems.

Material  

The main textbook is Introductory Statistics (8th Edition), by P. S. Mann, edited by Wiley.

Slides presented in class and additional material will be also provided on the course's website.

Exam  structure
The written exam covers the whole program of the course.
It includes multiple-choices as well as open questions, on both theoretical notions and applied issues, featuring e.g. graphs, computation of descriptive statistics and  estimation outputs applied to small datasets. This will allow to evaluate the student in terms of understanding and interpretation of the final results of an basic statistical analysis. 
Final mark ranges between 18 (minimum to pass the exam) and 30 (maximum mark).

Exam rules

• There will be no mid-term exam
• Students must book via Delphi for the exam. Please notice that students not booked on Delphi will not be allowed to take the exam.
• The exam can be taken only once per session.
• The students are allowed to bring with them in the exam room pens, calculator, and ID document. Textbooks, notes, smartphones or any other material are not allowed and their use by the students will invalid the exam. In such an event, the student will be allowed to re-sit the exam during the following session.

• Final marks are published on the dedicated web page (normally within one week after the exam) and are uploaded on the Delphi system so to be individually received by email by candidates.

Course objectives (5 Dublin Descriptors)

Learning Outcomes

The course provides the fundamental theoretical notions of the statistical analysis and illustrates the basic techniques for organizing, summarizing and graphically representing ungrouped data. Basics of statistical inference and correlation analysis (ANOVA, linear and multiple regression model) will also be illustrated.

Knowledge And Understanding
The students will know the fundamental theoretical notions of the analysis of any type of statistical data, including those aimed at summarizing, organizing and graphically representing the data with the most suitable measures and graphs. They will also be able to formulate and conduct simple hypothesis testing and to set basic regression analysis on small datasets.

Applying Knowledge And Understanding
The students will be able to produce and interpret basic statistical analyses. The use of examples based on real data (taken from Bank of Italy, Eurostat, Istat, NBER, etc…    )  is focal  throughout the entire course. The objectives of the course, thus, include the ability of recognizing the different types of data (cross-section, time-series, panel) and their peculiarities, the ability of producing descriptive statistics and suitable graphical representations, the ability of remembering and understanding the basics of statistical inference as well as of conducting a correct correlation analysis, via the estimation of linear regression models.

Making Judgements
The students will be able to autonomously evaluate which, among the several statistical tools acquired, is the most appropriate for the problem at hand. They will be able to formulate statistical hypotheses and to test them with suitable data, properly found among the tons of data available to date. Finally, they will be able to set up a linear regression analysis, evaluating the reliability and suitability of the model based on the estimation output.

Communication Skills
The students will be able to use data to get indications about economic phenomena and to effectively summarize and communicate them, also via e.g. graphical representations. Moreover, they will be able to produce short reports describing the research question, the statistical hypotheses to be tested, the dataset used (highlighting the nature of the dataset, potential outliers, ect) and the statistical tools used for the analysis. They will also be able to produce, interpret, evaluate and compare linear regression models.

Learning Skills
The course endows the students with an operative ability in terms of data retrieval and analysis of data, which is functional to the following courses and appreciated by the labour market. Moreover, it will allow students to evaluate and interpret more independently and effectively the numerous statistics daily provided by all types of media.

Updated A.Y. 2019-2020

Course Program 

The course is structured into the following sub-parts:

Part I: Descriptive Analysis
Introduction to Statistics
Types of data and variables
Summarizing and graphic data

Part II: Probability
Experiments, outcomes, sample spaces and probability axioms
Marginal and conditional probability
Independent events
Main probability distributions: Bernoulli, Binomial, Normal, Chi-Square, t-statistics, F

Part III: Inference
Estimators
Hypothesis testing

Part IV: Regression analysis
Simple and multiple inear regression model: Assumptions and properties

This module (Module I) will cover Part I and Part II. A detailed Syllabus is available on the course's website.

Course Methodology

The course offers two weekly classes, aimed at illustrating the theoretical foundations of statistical instruments and at implementing via examples and exercises.  A devoded weekly appointment will be devoted to practice with exercises and problems.

Material  

The main textbook is Introductory Statistics (8th Edition), by P. S. Mann, edited by Wiley.

Slides presented in class and additional material will be also provided on the course's website.

Exam  structure
The written exam covers the whole program of the course.
It includes multiple-choices as well as open questions, on both theoretical notions and applied issues, featuring e.g. graphs, computation of descriptive statistics and  estimation outputs applied to small datasets. This will allow to evaluate the student in terms of understanding and interpretation of the final results of an basic statistical analysis. 
Final mark ranges between 18 (minimum to pass the exam) and 30 (maximum mark).

Exam rules

• There will be no mid-term exam
• Students must book via Delphi for the exam. Please notice that students not booked on Delphi will not be allowed to take the exam.
• The exam can be taken only once per session.
• The students are allowed to bring with them in the exam room pens, calculator, and ID document. Textbooks, notes, smartphones or any other material are not allowed and their use by the students will invalid the exam. In such an event, the student will be allowed to re-sit the exam during the following session.

• Final marks are published on the dedicated web page (normally within one week after the exam) and are uploaded on the Delphi system so to be individually received by email by candidates.

Course objectives (5 Dublin Descriptors)

Learning Outcomes

The course provides the fundamental theoretical notions of the statistical analysis and illustrates the basic techniques for organizing, summarizing and graphically representing ungrouped data. Basics of statistical inference and correlation analysis (ANOVA, linear and multiple regression model) will also be illustrated.

Knowledge And Understanding
The students will know the fundamental theoretical notions of the analysis of any type of statistical data, including those aimed at summarizing, organizing and graphically representing the data with the most suitable measures and graphs. They will also be able to formulate and conduct simple hypothesis testing and to set basic regression analysis on small datasets.

Applying Knowledge And Understanding
The students will be able to produce and interpret basic statistical analyses. The use of examples based on real data (taken from Bank of Italy, Eurostat, Istat, NBER, etc…    )  is focal  throughout the entire course. The objectives of the course, thus, include the ability of recognizing the different types of data (cross-section, time-series, panel) and their peculiarities, the ability of producing descriptive statistics and suitable graphical representations, the ability of remembering and understanding the basics of statistical inference as well as of conducting a correct correlation analysis, via the estimation of linear regression models.

Making Judgements
The students will be able to autonomously evaluate which, among the several statistical tools acquired, is the most appropriate for the problem at hand. They will be able to formulate statistical hypotheses and to test them with suitable data, properly found among the tons of data available to date. Finally, they will be able to set up a linear regression analysis, evaluating the reliability and suitability of the model based on the estimation output.

Communication Skills
The students will be able to use data to get indications about economic phenomena and to effectively summarize and communicate them, also via e.g. graphical representations. Moreover, they will be able to produce short reports describing the research question, the statistical hypotheses to be tested, the dataset used (highlighting the nature of the dataset, potential outliers, ect) and the statistical tools used for the analysis. They will also be able to produce, interpret, evaluate and compare linear regression models.

Learning Skills
The course endows the students with an operative ability in terms of data retrieval and analysis of data, which is functional to the following courses and appreciated by the labour market. Moreover, it will allow students to evaluate and interpret more independently and effectively the numerous statistics daily provided by all types of media.

Updated A.Y. 2018-2019

Course Program 

The course is structured into the following sub-parts:

Part I: Descriptive Analysis
Introduction to Statistics
Types of data and variables
Summarizing and graphic data

Part II: Probability
Experiments, outcomes, sample spaces and probability axioms
Marginal and conditional probability
Independent events
Main probability distributions: Bernoulli, Binomial, Normal, Chi-Square, t-statistics, F

Part III: Inference
Estimators
Hypothesis testing

Part IV: Regression analysis
Simple linear regression model: Assumptions and properties
Multiple linear regression model: Assumptions and properties

This module (Module I) will cover Part I and Part II. A detailed Syllabus is available on the course's website.

Course Methodology

The course offers two weekly classes, aimed at illustrating the theoretical foundations of statistical instruments and at implementing via examples and exercises. Besides, one weekly lecture will be specifically devoted to practice with exercises and problems.

Material  

The main textbook is Introductory Statistics (8th Edition), by P. S. Mann, edited by Wiley.

Slides presented in class and additional material will be also provided on the course's website.

Exam  structure
The final evaluation is based on a closed-book written exam, covering the entire course’s program. It includes open questions, multiple choices questions, and exercises aimed at evaluating the knowledge and understanding of the theoretical as well as applied notions illustrated during the course.

Exam rules

• There will be no mid-term exam
• Students must book for the exam. Please notice that students not booked will not be allowed to take the exam.
• The exam can be taken only once per session.
• The students are allowed to bring with them in the exam room pens, calculator, and ID document. Textbooks, notes or other material are not allowed and their use by the students will invalid the exam. In such an event, the student will be allowed to re-sit the exam during the following session.

• Final marks are published on the dedicated web page (normally within one week after the exam) and are uploaded on the Delphi system so to be individually received by email by candidates.

Course objectives (according to the 5 Dublin Descriptors)

Knowledge And Understanding
The course provides the students with the theoretical undergrounds of basic statistical analyses. The course will start with an overview of the descriptive analyses, illustrating for each type of data, the most suitable graphical representations and summary statistics, highlighting points of strength and caveats. Then, the fundamentals of Probability will be given, followed by the presentation of the main features of the mostly used random variables models (Bernoulli, Binomial, Normal, Chi-square, t-Student, F). Then, sampling distributions and hypotheses tests will be illustrated, with specific attention to tests about the mean and variance of the population. Finally, simple linear regression and multiple regression models will be presented, specifying the main assumptions and properties.

Applying Knowledge And Understanding
The students will be able to produce and interpret basic statistical analyses. The use of real data (taken from Eurostat, Istat, NBER, etc…) and of appropriate statistical software is focal all throughout the course classes and lectures. The objectives of the course, thus, include the ability of finding the appropriate dataset, recognizing the different types of data (cross-section, time-series, panel) and their peculiarities, the ability of producing descriptive statistics and suitable graphical representations, as well as of conducting a correct correlation analysis, via the estimation of linear regression models.

Making Judgements
The students will be able to evaluate which, among the different statistical tools acquired, is/are the most appropriate for the problem at hand. They will be able to formulate statistical hypotheses and to test them with suitable data, properly found among the tons of data available to date. Finally, they will be able to set up a linear regression analysis, evaluating the reliability and suitability of the model based on the estimation output.

Communication Skills
The students will be able to use data to get indications about economic phenomena and to effectively communicate them, via e.g. graphical representations and summary statistics. Moreover, they will be able to produce short reports describing the research question, the statistical hypotheses to be tested, the dataset used (highlighting the nature of the dataset, potential outliers, etc) and the statistical tools used for the analysis. They will also be able to produce, interpret, evaluate and compare linear regression models.

Learning Skills
The course endows the students with a substantial operative ability in terms of data retrieval and analysis of data, which is functional to the following courses and appreciated by the labor market. Moreover, it will allow students to evaluate and interpret more independently and effectively the numerous statistics daily provided by all types of media.

Updated A.Y. 2017-2018

Course Program 

The course is structured into four sub-parts, described in what follows.

Part I: Descriptive Analysis
Introduction to Statistics
Types of data and variables
Summarizing and graphic data

Part II: Probability and main distributions
Experiments, outcomes, sample spaces and probability axioms
Marginal and conditional probability
Independent events 
Bayes theorem
Discrete random variables: Bernoulli, Binomial
Continuous random variables: Normal, Chi-Square, t-statistics, F

Part III: Inference
Estimators
Hypothesis testing

Part IV: Regression analysis
Simple linear regression model: Assumptions and properties
Multiple linear regression model: Assumptions and properties

This module (Module I) will cover Part I and Part II.

Course Methodology

The course offers three weekly classes, aimed at illustrating the theoretical foundations of statistical instruments and at implementing them via examples and exercises. Besides, one weekly lecture will guide the students in practicing with paper and pencil exercises as well as with real data statistical analysis.  

Material  

The main textbook is Introductory Statistics (8th Edition), by P. S. Mann, edited by Wiley.

Slides presented in class and additional material will be also provided on this website.

Exam

The final evaluation is based on a closed-book written exam, covering the entire course’s program.

It includes open questions, multiple choices questions, and exercises aimed at evaluating the knowledge and understanding of the theoretical notions illustrated during the course. Part of the exam focuses on the computation and interpretation of the coefficients and the diagnostics of linear regression models, aimed at evaluating the ability of students of applying the knowledge acquired in the course.

Other rules

• Students must book for the exam.
• The exam can be taken only once per session.
• Final marks are published on the dedicated web page (normally within one week after the exam) and are uploaded on the Delphi system so to be individually received by email by candidates.
• Midterm written exams will be held during the weeks scheduled for the intermediate tests. If the first midterm exam is passed, and the candidate accepts the mark, the candidate can also take the second mid term, that will be held before Christmas break, and will concern the topics covered in the second part of the course only.The final evaluation will be the average of the two marks obtained in the midterm and in the final exam, with weights equal to 50% and 50% respectively.
• Students who are not satisfied with the mark of one or both the midterm exams can resit and take the full exam in the regular exam sessions.
• Please notice that no part of the program will be waived if the final exam is taken during regular exam sessions, even if one or both midterm exams are passed. This means that if you take your final exam in January/February or June/July or September you will have to take the full exam, even if you got a sufficient mark in one or both the midterm exams.

Course objectives (according to the 5 Dublin Descriptors)

Knowledge And Understanding
The course provides the students with the theoretical undergrounds of basic statistical analyses. The course will start with an overview of the descriptive analyses, illustrating for each type of data, the most suitable graphical representations and summary statistics, highlighting points of strength and caveats. Then, the fundamentals of Probability will be given, followed by the presentation of the main features of the mostly used random variables models (Bernoulli, Binomial, Normal, Chi-square, t-Student, F). Then, sampling distributions and hypotheses tests will be illustrated, with specific attention to tests about the mean and variance of the population. Finally, simple linear regression and multiple regression models will be presented, specifying the main assumptions and properties.

Applying Knowledge And Understanding
The students will be able to produce and interpret basic statistical analyses. The use of real data (taken from Eurostat, Istat, NBER, etc…) and of appropriate statistical software is focal all throughout the course classes and lectures. The objectives of the course, thus, include the ability of finding the appropriate dataset, recognizing the different types of data (cross-section, time-series, panel) and their peculiarities, the ability of producing descriptive statistics and suitable graphical representations, as well as of conducting a correct correlation analysis, via the estimation of linear regression models.

Making Judgements
The students will be able to evaluate which, among the different statistical tools acquired, is/are the most appropriate for the problem at hand. They will be able to formulate statistical hypotheses and to test them with suitable data, properly found among the tons of data available to date. Finally, they will be able to set up a linear regression analysis, evaluating the reliability and suitability of the model based on the estimation output.

Communication Skills
The students will be able to use data to get indications about economic phenomena and to effectively communicate them, via e.g. graphical representations and summary statistics. Moreover, they will be able to produce short reports describing the research question, the statistical hypotheses to be tested, the dataset used (highlighting the nature of the dataset, potential outliers, etc) and the statistical tools used for the analysis. They will also be able to produce, interpret, evaluate and compare linear regression models.

Learning Skills
The course endows the students with a substantial operative ability in terms of data retrieval and analysis of data, which is functional to the following courses and appreciated by the labor market. Moreover, it will allow students to evaluate and interpret more independently and effectively the numerous statistics daily provided by all types of media.