- Docente: Silvia Pacei
- Credits: 3
- Language: Italian
- Teaching Mode: In-person learning (entirely or partially)
- Campus: Rimini
- Corso: Second cycle degree programme (LM) in Business Administration and Management (cod. 6796)
Course contents
1. Business Databases
Data sources for businesses. Data from probability and non-probability sample surveys.
The organization of information in the data matrix.
Descriptive data analysis using SAS software.
2. The Use of Multiple Linear Regression Models for the Analysis of Business and Economic Data
Assumptions underlying the model. Estimation and interpretation of model parameters. Properties of the estimators. Assessment of the goodness-of-fit of the linear model. Overall significance testing of the model. Tests on individual parameters. Residual analysis. The problem of multicollinearity. Inclusion of qualitative explanatory variables.
Nonlinear regression models.
Examples and applications in SAS.
3. Data Analysis for Business Strategy: Market Segmentation
The distance (or dissimilarity) matrix. Euclidean distance, Mahalanobis distance, and Minkowski distance. Similarity measures.
Market segmentation. A priori and a posteriori classification methods.
A posteriori classification methods: hierarchical cluster analysis. Steps in hierarchical cluster analysis. Hierarchical clustering methods. Selection of the number of clusters and criteria for evaluating cluster solutions.
A posteriori classification methods: non-hierarchical cluster analysis. Steps in non-hierarchical cluster analysis. The k-means clustering method. Selection of the number of clusters and cluster profiling.
Examples of applications in SAS.
Prerequisites
Basic knowledge of probability axioms, random variables, estimators and their properties, confidence intervals, hypothesis testing, and simple linear regression.
Readings/Bibliography
Lecture slides provided by the instructor (for students attending the course), available on the University's Virtual Learning Environment.
Recommended textbooks:
F. Bassi Francesca and S. Ingrassia, Statistica per analisi di mercato. Metodi e strumenti, Pearson, 2022 (Chapters 3, 5, 6, and 8).
J. H. Stock and M. W. Watson,Introduzione all'econometria (Italian edition edited by F. Peracchi), 2nd edition, Pearson Education, 2009 (Chapter 8).
Teaching methods
Lectures and laboratory sessions.
Due to the nature of the activities and teaching methods adopted, attendance requires that all students complete Modules 1 and 2 of the mandatory e-learning training on health and safety in study environments, available at:
https://corsi.unibo.it/magistrale/aegi/formazione-obbligatoria-su-sicurezza-e-saluteAssessment methods
Students are required to submit a written report and participate in an oral discussion based on a group practical project consisting of the application, in SAS, of the methods covered in the course to a given dataset. In addition, students must take an individual oral examination on the course topics not included in the practical project.
The final grade is the average of the grades obtained in the two assessment components.
Students are allowed one opportunity to decline the final grade.
Office hours
See the website of Silvia Pacei