Unit STATISTICS FOR BUSINESS
- Course
- Management and food italian culture
- Study-unit Code
- A003260
- Curriculum
- In all curricula
- Teacher
- David Aristei
- Teachers
-
- David Aristei
- Hours
- 42 ore - David Aristei
- CFU
- 6
- Course Regulation
- Coorte 2025
- Offered
- 2025/26
- Learning activities
- Caratterizzante
- Area
- Discipline matematico-statistiche
- Sector
- SECS-S/03
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- Italian
- Contents
- The course is organized in four main parts:
1) Introduction to business statistics and data sources for business analysis
2) Measures of association
3) The linear regression model
4) Regression models for binary dependent variables - Reference texts
- Main textbook:
- Bracalente B., Cossignani M., Mulas A.: “Statistica Aziendale”, McGraw-Hill, Milano 2009.
- Biggeri L., Bini M., Coli A., Grassini L., Maltagliati M.: “Statistica per le decisioni aziendali”, 2/Ed., Pearson, Milano 2017
Additional suggested textbook:
- Bassi, F., Ingrassia, S.: “Statistica per analisi di mercato. Metodi e strumenti”, Pearson, Milano 2022.
Class slides and materials used during the laboratory sessions will be distributed through the UniStudium e-larning platoform - Educational objectives
- This course will introduce students to the most relevant statistical methods for business analysis.
In particular, theoretical lectures will be focused on the presentation of regression models for the analysis of economic and business data.
Specific attention will be also devoted to on empirical applications. Every week there will be a practical laboratory session in which students will apply concepts learned during the theoretical lectures, using statistical software. - Prerequisites
- This course requires basic knowledge of descriptive and inferential statistics and probability theory.
- Teaching methods
- Face-to-face lessons completed with practical laboratory activities
- Other information
- Attending students are to complete some (non-compulsory) take-home assignments
- Learning verification modality
- Written examination After grading, the instructor reserves the right to invite the student for an additional oral examination.
- Extended program
- 1) Introduction to business statistics Data sources for business analysis 2) Data analysis: measures of association Pearson’s linear correlation coefficient Inference of association measures 3) The linear regression model Simple and multiple regression models Parameters estimation by ordinary least squares Goodness-of-fit Hypothesis testing Prediction Diagnostic analyses: non-linearity, heteroscedasticity and multicollinearity issues 4) Regression models for binary dependent variables The linear probability model The logistic regression (logit) model Estimation of logit parameters Interpretation of results and inference Predicted probabilities Measures of goodness-of-fit
- Obiettivi Agenda 2030 per lo sviluppo sostenibile
- 4, 8, 9, 12