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 predictive modelling and its application to Management studies. The students will learn business and predictive analytics methods for visualizing, mining, and interpreting information and special emphasis will be given to critical interpretation of results. An integral part of the course is learning the basis of the R software for statistical computing.

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 statistical methods suitable for the analysis. They must also be able to present and discuss the results and the management strategies derived from the developed models.

In particular, students should:
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 critically assess under which circumstances the analyses are reliable
- be able to assess the goodness of the estimated models

4. Communication
- know how to present, discuss and prove the information achieved by the analyses
- know how to write a report of the results in form of scientific paper
- know how to argue management decisions in an effective way.

Working knowledge of basic probability and statistics. A gentle introduction to statistics can be found in Ross. S.M.(2010) Introductory Statistics. 3d edition, Elsevier. Understanding of the topics covered in Chapters 1-9 of the above book will be assumed though the course. Students may consider alternative textbooks that cover the same topics.

1. Review of elementary probability and inferential statistics
3. Multivariate linear regression models
4. Inference and critical interpretation in linear multivariate regression models
6. Dichotomous independent variables, interaction effects
7. Multicollinearity and variable selection
8. Generalised linear models
9. Non linear models
10. Panel data techniques
11. Time series analysis
12. Machine learning

To support the theoretical knowledges acquired during the course, each topic will be developed by using the R statistical software. In particular, R will be briefly introduced and the approaches and models used in the analyses will be developed used particular packages provided in R.

Notes, slides, data and other material necessary to follow lectures and to attain the intended learning outcomes are downloadable from the e-learning platform moodle.unive.it.

Mandatory texts:

1. Trevor Hastie, Robert Tibshirani, Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction: Data Mining, Inference, and Prediction, Second Edition
2. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani An Introduction to Statistical Learning: With Applications in R
3. Scott Cunningham, Causal Inference, The Mixtape, Yale University Press

Additional readings:

Other reading material will be suggested by the teacher during the course

The exam consists of writing a report in form scientific paper about the results of the statistical analysis of a dataset assigned to each student by the lecturer and of its critical oral presentation.
The evaluation will be based to the understanding of the material, on the material prepared by the student and on the efficacy of the presentation.
Homeworks will be assigned to practice the understanding and the skills achieved during 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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