The aim of the course is to enable students to expand and deepen their knowledge and skills in various areas of statistics and of statistical analysis of large data sets.

After completing the course the student will:

• Have a solid knowledge of statistical methodologies and techniques for analysing large data sets.
• Be able to process and describe the information contained in large data sets.
• Have an in-depth understanding of the mechanisms that justify the choice of one method over another.
• Be able to select and use the basic statistical data analysis tools using the SPSS statistical software.
• Be able to interpret correctly the software graphs and results.
• Solve problems with real-world data by using an interdisciplinary approach.

• Towards a Philosophy of Data Analysis.
• Data management, data sources, sample and population, measurement and nature of variables, coding, data entry and data cleaning, extreme values, missing values.
• Univariate and bivariate statistical analysis – Constructing Graphical Displays – Contingency tables – Correlation – Hypothesis tests – Analysis of Variance – Non parametric procedures.
• Multivariate techniques for data analysis and applications in economics.
• Distinction (reducing, clustering, interaction) of data analysis methods – Factor analysis – Cluster analysis.
• Overview of SPSS – Using SPSS to analyze large sets of real data.

Lectures (3 hours per week) and Laboratory work (1 hour per week)

• Written exam at the end of the semester
• Project (it is optional and count for 60% of the final grade)

• Use of e-class to support teaching, laboratory work and communication with students.
• Use of SPSS Statistical software.