STATISTICS FOR EMPIRICAL RESEARCH IN MANAGEMENT

The course is one of the first term activities of the Ph.D. teaching program in Management that allows students to acquire knowledge and understanding of some of the main statistical concepts and their use in business management activities. The course aims at providing an introduction to statistical modelling and its application to Management studies. The students will learn analytical methods for visualizing, mining, and interpreting information. Special emphasis will be given to the critical interpretation of results. Learning will be supported by adequate statistical computing software (e.g. R).

At the end of the course, students will be expected to have acquired the skills to develop a critical, personal and rigorous analysis of business phenomena and their consequence for management studies, through the use of suitable statistical methods. They must also be able to present and discuss the results and the management strategies derived from the developed models.

1. Knowledge and understanding
- know the terminology and basic concepts of probability and statistical inference
- understand the strengths and limitations of the statistical approaches used to analyze real phenomena.
- know the standard statistical models and some advanced methods for the analysis and the prediction and their application to Management studies.

2. Ability to apply knowledge and understanding
- understand the main aspects of the statistical analyses;
- know how to determine the best statistical models for analysis and prediction
- know how to present strategies for management studies based on the achieved results.

3. Making judgements
- be able to assess critically appraise the estimated models
- be able to critically assess under which circumstances the analyses are reliable

4. Communication
- know how to present, discuss and justify the information achieved by the analyses
- know how to report the results in written form

It is assumed that students have a working knowledge of basic probability and statistics.
An introduction to statistics can be found in Ross. S.M. Introductory Statistics. 3d edition, Elsevier.
Understanding of the topics covered in Chapters 1-9 of the above book will be assumed throughout the course. Students may consider alternative textbooks that cover the same topics.

1. Review of elementary probability and statistics
1.1 Elementary probability
1.2 Summary statistics, estimation and hypothesis testing
2. Multivariate linear regression models
2.1 Review of single linear regression models (OLS)
2.2 Multiple linear regression
2.3 Inference and critical interpretation regression models
2.4 Dichotomous and categorial independent variables, ANOVA and ANCOVA
2.5 Multicollinearity and variable selection
3. Panel data methods
3.1 Introduction to panel data
3.2 Panel data regression
3.3 Fixed and random effects estimation
4. Generalized linear models
4.1 Introduction to generalized linear models
4.2 Probit and logit models for binary outcomes
5. Clustering and classification methods.
5.1 Generalized linear models as classifiers
5.2 Measurements of accuracy in classification problems
5.3 Clustering methods beyond generalized linear models

The practical implementation of the statistical methods presented will be showcased via adequate statistical software (e.g. R)

Students are encouraged to suggest topics relevant to their research program

Main textbooks:
1. Introductory econometrics : a modern approach / Jeffrey M. Wooldridge. 5th edition, Boston : Cengage
2. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani An Introduction to Statistical Learning: With Applications in R, 2nd edition, Springer

Additional readings:
Ross. S.M. Introductory Statistics. 3d edition, Elsevier.
Trosset, Michael W. An introduction to statistical inference and its applications with R. CRC Press
Additional suggested reading and materials made available on the Moodle platform

During the term students will be asked to carry out some small assignments in which they will be asked to perform a data-analysis task applying the topics discussed in class. The assignments will be assessed on a pass/fail scale: if a student has failed an assignment they will be asked to re-do it until the data-analysis is deemed to be sufficient.
Students who have obtained a sufficient grade in all assignments will have reached a 18/30 mark and are allowed to take a written exam worth 15 points to improve their grade. The written exam will last 45 minutes and will assess the understanding of the more theoretical concepts presented the course.

Theoretical lectures complemented by lab classes. Methods will be discussed and illustrated through applications to real data making use of dedicated software. Teaching material prepared by the lecturer will be distributed during the course. The statistical software used in the course is R (www.r-project.org).

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