STATISTICS

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 or Python).

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 capacity:
- 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. Independence of judgment:
- 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.
Basic 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.

Tentative syllabus (topics covered will be adjusted depending on student's basic skills at the beginning of the course*)
0. The nature of data and the relevance of statistical analysis
1. Exploring and summarizing data: Refresher of descriptive statistics
2. From data to learning and decision making: Refresher of inferential statistics
2.1 Refresher of the fundamentals of probability for statistical analysis
2.2 Statistical estimation and hypothesis testing
3. Towards model-based prediction: The simple linear regression model
3.1 Descriptive statistics for simple linear regression models: Ordinary least squares estimates (OLS)
3.2 Inference for simple linear regression models: Variability, intervals and tests
3.3 Regression-based prediction
4. Beyond simple linear regression (e.g. multiple linear regression, generalized regression models)

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

Students are encouraged to suggest data and examples relevant to their research program.

* Priority will be given to an in-depth understanding of the fundamental concepts and the capacity to build up from there, rather than on covering a widespread range of topics.

Main textbooks:
1. Introductory econometrics : a modern approach / Jeffrey M. Wooldridge. 5th edition, Boston : Cengage
2. Learning statistics with Python/ Ethan Weed (https://ethanweed.github.io/pythonbook/landingpage.html )
3.Learning Statistics with R/ Danielle Navarro (https://learningstatisticswithr.com/ )

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
- Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani An Introduction to Statistical Learning: With Applications in R, 2nd edition, Springer

Additional suggested reading and materials made available on the Moodle platform

Evaluation will be carried out in two parts:
- A written exam (45-60 minutes): mainly focused on the theoretical concepts presented during the course, also evaluating the capacity for critical thinking.
- A project/report (submitted via Moodle on the day of the exam): mainly focused on the practical application of the concepts presented during the course and the capacity to communicate results in a statistically formal manner.
Students must pass at least one of the two parts. This guarantees a final mark of at least 18; higher marks with depend on the grade of the other part.

written

For attending and non-attending students alike:
- Sufficient (18-22 points): to students who demonstrate a sufficient theoretical knowledge base and capacity to apply the concepts covered throughout the course, as well as a sufficient capacity to elaborate, interpret and present results using the specific language and mathematical notation associated to the statistical models and methods covered during the course
- Good (23-26 points): to students who demonstrate a good theoretical knowledge base and capacity to apply the concepts covered throughout the course, as well as a good capacity to elaborate, interpret and present results using the specific language and mathematical notation associated to the statistical models and methods covered during the course
- Very good (23-26 points): to students who demonstrate a very good superior theoretical knowledge base and capacity to apply the concepts covered throughout the course, as well as a very good or superior capacity to elaborate, interpret and present results using the specific language and mathematical notation associated to the statistical models and methods covered during the course, and at least a basic capacity to identify relations between different concepts covered throughout the course and formulate independent judgement.
- Honors will be granted to students exhibiting an excellent knowledge base anc capacity to apply the concepts covered during the course through the use of specific language and mathematical notation, including the identification of relationships between different concepts and definitions.

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 Python