CORSO DI STUDIO: Corso di Laurea Magistrale in Economia Aziendale                                                 a.a. 2015/2016

Denominazione insegnamento/Course Title

Laboratory of Statistics and Mathematics
Laboratorio di matematica e statistica:metodi quantitativi per le decisioni aziendali e le analisi di mercato

Lingua dell’insegnamento: Inglese

Crediti e ore di lezione: 9 CFU 72-80 ore di lezione

Moduli:  NO

Settore/i scientifico disciplinare: SECS-S/03 - SECS-S/06

Docente (nome e cognome): Luca Grassetti
Indirizzo email: luca.grassetti@uniud.it
Pagina web personale: http://people.uniud.it/page/luca.grassetti

Prerequisiti e propedeuticità/Requirements

The course is the first statistics course in the Business Economics Master Degree. None bridging course is provided for the present teaching.

Preliminary knowledge consists of basic statistics topics. Advanced statistics courses (as for instance Statistics 2 course – Economics Bachelor Degree) can help the students' learning process.

Conoscenze e abilità da acquisire/Knowledge and skills

The course unit aims to raise awareness of the basic statistical knowledge applied to business economics problems. In particular students will be able to face the measurement issue in the economic framework as a tool for the consumer behaviour analysis and the evidence based decision-making process.

Course specific knowledge

The course supply the students with basic tools for quantitative analysis in the development of evidence based policies.

At the end of the course unit the students will be able to:

• recognise the classical measurement and data collection issues;
• distinguish between primary and secondary consumer data;
• organise a (market) survey adopting the optimal solutions at each step of the research development: the selection of sampling method, the construction of the measurement tool, the data collection and preliminary data analysis;
• develop a descriptive preliminary analysis in order to discover the potentials and issues of data (as, for instance, the presence of missing data or outliers);
• apply the main tools of statistical inference and explain their results (e.g. hypothesis testing);
• study the relationship between variables (both qualitative and quantitative ones) also considering the regression approach;
• apply the main tools of multivariate statistical analysis (factor analysis, principal components analysis and cluster analysis);
• appreciate the potentials of R statistical software.

Soft skills

• topics faced during the semester introduce the statistical tools that students can use during the degree courses. In order to try to use the concepts that are theoretically introduced during the course the students will develop an empirical activity.
• the students will be able to apply the optimal statistical tool given the empirical framework (from the measurement issue to the analysis of collected data)
• the group works aim at developing the communication skills of students using the ability to synthetise based on statistical summary statistics and graphical tools

the skills developed during the teaching can be easily applied in other contexts in order to understand the results of the quantitative analysis

Programma e contenuti dell'insegnamento/Course description

The course aims at introducing the students to the development of market surveys or simply to the collection and study of data supporting the decision makers. After a brief preface we will focus on four main arguments:

• Collecting, preparing and checking the data

1. Measurement, errors and data for consumer research
2. Secondary consumer data
3. Primary consumer data
4. Data preparation and descriptive statistics

• Sampling, probability and Inference

1. Sampling
2. Hypothesis testing
3. Analysis of variance (ANOVA)

• Relationships among variables

1. Correlation and regression
2. Association and logistic analysis
3. Factor analysis and principal component analysis

• Classification and segmentation techniques (10 lectures).

Cluster analysis

Attività di apprendimento e metodi didattici previsti/Teaching and Learning activities

Course slides (that will be released during the semester) cover the entire course programme but they must be integrated with some other didactic materials.

The main topics will follow the textbook outline. Some specific arguments will be developed following specific alternative material (specific notes and books chapters).

The theory lectures will be completed by some exercise lectures developed considering both didactic and real data examples.

The teaching activity will also consider a group work in which the students will be able to test their knowledge.

Modalità di verifica dell'apprendimento/Examination

The final examination consists in:

The written exam and the group work contribute to the final mark for 21 and 9 points respectively. In order to be considered in the final mark computation both works must be positively evaluated. The written exam aims at testing the theoretical skills while the group works are used to evaluate the capacity to apply the studied concepts. The optional oral exam is an integration of the written exam. A maximum of three points can be assigned to this test. The test can also be negatively evaluated.  Honours will be assigned to students of marked excellence.

Testi / Bibliografia/Bibliography

Mazzocchi M. (2008) “Statistics for Marketing and Consumer Research”, Sage, London

Chapter 8 in Sheater S. (2009) “A Modern Approach to Regression with R”, Springer, New York

Chapter 6 in Faraway (2006) “Extending the linear model with R: generalized linear, mixed effects and nonparametric regression models”, Chapman & Hall/CRC, Boca Raton

Strumenti a supporto della didattica/Further readings and support material

Course slides (theory and exercise) available on “Materiale didattico” website. Additional didactic materials (free available on the web):

Cluster analysis material (two different notes and a book chapter)

Tesi di laurea/Thesis

The present course is mainly thought as a support for the other courses of the Master Degree in Business Economics. Notwithstanding, it is possible to develop the thesis within the course framework. The thesis work will be mainly empirical. The development of theoretical thesis could be difficult given the lack in statistical basis characterising the students of this specific course. Given the statistical framework, the focus of the final dissertation must be on the measurement issues and on the empirical analysis of business economics phenomena.

Note/Remarks

The present course aims at introducing the students to the empirical analysis of business economics datasets. Consequently the specific teaching activity can be considered as propaedeutic to the development of specific empirical analyses proposed in the other Master degree course units. The students’ evaluation does not present differences between attenders and non-attenders. Non-attenders cannot participate to group works and for this reason their final marks will be computed on the basis of written and optional oral exams only.