• use Stata in reading and manipulating the data;
• calculate and interpret measures of correlation, partial correlation, and regression coefficients from different regression models and conduct tests based on cross section, time-series, and survey data;
• test hypothesis and perform advanced statistical techniques to explore and analyse the relationship among variables;
• critically discuss and recommend solutions to problems encountered in the analysis of a specific phenomenon;
• critically interpret and present the results and make recommendations based upon the results of the analysis;

In a world in which we are constantly surrounded by data, figures, and statistics, it is imperative to understand and to be able to use quantitative methods. Statistical models and methods are among the most important tools in decision-making, business planning and economic analysis. The purpose of the course is to provide master’s students with business analytical skills necessary to solve managerial problems in today’s competitive decision-making environment. It aims to familiarize business students with the basic principles, techniques, and applications of descriptive statistics as well as in depth data analysis.

Even though managers across the wide range of industries may not be necessarily involved in the applications of analytical skills, they should be able to understand the research design proposed and adequately judge the quality of research given its implications for decision making. Accordingly, this course is particularly useful to master students, in that it teaches them the state of the art tools and methods to analyze and process data rigorously, thus correctly addressing business related issues or hypotheses.

The statistical software we will use in this course is STATA, one of the most powerful statistical programs able to process small as well as large databases whether they are cross section, time series, panel data, or simply survey data. You will experience a “hands on software” approach and an interactive learning in analyzing these types of data. The simple and consistent command structure of this statistical program makes it rather easy to learn and to become proficient in its application.

The purpose of this course is to provide to master’s-level students with state of the art analytical skills for analyzing data rigorously. Thus, this course will address the quantitative skills with respect to data mining such as reading, summarizing and analysing the data; testing hypothesis and performing cross-section, time series and survey data estimation analysis. However, the course also considers problems related to data analysis and interpretation.

This course will help the students build upon their technical skills. For instance students will learn how to use STATA to perform data mining; prepare statistical analysis (table statistics, graphs for data exploration, testing hypothesis and performing statistical regressions/techniques to explore the relationship among variables); interpret and present the results in a comprehensive way.

The course consists of lectures that introduce the basic concepts and their applications to real business (and economic) data, such as cross-section, time series and survey data. The statistical program the students will get acquainted with and use throughout the course is Stata. Lectures will be interactive with ‘hands on software’ approach such that lecturing and learning are closely integrated. This approach will enable the students to learn how to use the software and it will facilitate the understanding of the course concepts and their applications on real data, as well as increase the class dynamics with lively in the class discussions.

At the end of the course the students will know how to use Stata in reading and analysing the data; testing hypothesis and applying cross-section, time series and survey data estimation techniques.

Indicative topics to be covered in this course:

• Introduction to STATA, the software and the output
• Introduction to linear regression analysis
• Evaluation of linear regression analysis: regression diagnostics
• The use of qualitative variables: Limited Dependent variable models
• Multinomial logit and probit models
• Model specification errors
• Forecasting: Time series analysis

The required textbook for this course is:

Damodar Gujarati. Econometrics by Example, Palgrave Macmillan (latest edition)