Student guide Faculty of Economics A.Y. 2008/09

Quantitative Methods for Economics, Finance and Management
Lesson timetable, Notice board, Additional material
Lecturers
SACCARDI ALBERTO
PALLINI ELENA
PALLINI ELENA
LEUCARI VALENTINA
LEUCARI VALENTINA
Aim of the course
The course provides an overview of the principal multivariate statistical methods used for interpreting economic observations and supporting managerial decision-making.
The teaching method will alternate lectures on the theory with lessons focusing more on practical applications. Students will be presented with real-world examples, and learn to use the main specialist software programs during the practical part of the course. At the end of the course, participants will have the ability to independently replicate the analysis methods presented during the lectures, and a sound grasp of data processing and analysis, particularly with respect to the interpretation and use of the results obtained.
 
Syllabus
Introduction to the course. Internal data sources: Data Warehousing and Customer Data Base systems.
·         External data sources: market research. Qualitative and quantitative research. Sampling methods.
·         Introduction to the software application. Extraction of a simple random sample.
·         Introduction to the statistical analysis of data. Univariate analysis: frequency distributions (measures of location, dispersion and shape). Study of concentration.
·         Bivariate analysis: contingency tables, tests and measures of association. Study of the correlation and average dependence.
·         Introduction to multivariate statistical analysis. Factor analysis by the method of principal components: method.
·         Factor analysis by the method of principal components: applications.
·         Linear regression analysis: method.
·         Linear regression analysis: applications.
·         Logistic regression analysis: method.
·         Logistic regression analysis: applications.
·         Introduction to time series analysis: classical approach part 1.
·         Introduction to time series analysis: classic approach part 2. The ARIMA class of models: methodological overview.
 
Examinations
For students who attend class
Assessment will be based on
a short written exam with multiple choice questions,
and a practical group project activity.
Both the above activities will contribute to the final result.
For distance students:
Written examination.
 
Reading list
For both students who attend class and distance students.
 
J. D. JOBSON, Applied Multivariate Data Analysis, New York, Springer, 1992
C. CHATFIELD, The Analysis of Time Series: An Introduction, London, Chapman and Hall, 2004
L. MOLTENI, G. TROILO, Ricerche di marketing, Milano, Mc Graw Hill, 2003.
Notes and slides supplied by the lecturers.