STATISTICAL METHODS FOR BUSINESS ANALYSIS

The course is one of the activities chosen by the student in the Master’s degree program in Administration, Finance and Control that allows students to acquire knowledge and understanding of some of the main statistical concepts and their use in business management activities. The aim of the course is to provide students with modern business and predictive analytics methods for visualizing, mining, and interpreting information available through business databases with the aim of planning strategies to support and improve the decision process.

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 through tools and statistical methods suitable for the analysis. They must also be able to present in a communicative way the results obtained and the strategies proposed through graphic and numerical synthesis deriving from the models developed. In particular, students should:
1. Knowledge and understanding
- know the terminology and basic concepts of descriptive and inferential statistics;
- understand the strengths and limitations of the statistical approaches used to analyze real phenomena;
- know the basic statistical models for the analysis and the prediction of business phenomena.
2. Ability to apply knowledge and understanding
- understand the main aspects of the descriptive and inferential statistical analyses;
- know how to determine the best statistical models for analysis and prediction;
- know how to present business strategies 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 argue business decisions in an effective way.

Basic concepts of Statistics at the level of an introductory undergraduate course. See for example the book: Anderson, D. R., Sweeney, D., Williams, T. (2014) Statistica per le analisi economico-aziendali, Apogeo, or chapters 1-12 of: Illowsky, D., Dean, S. (2022) Introductory Statistics, OpenStax (freely available at link https://openstax.org/details/books/introductory-statistics?Book%20details ).
In particular, students are assumed to be able to apply their knowledge and understanding about the concepts and methods concerning descriptive and inferential statistics.

1. Organization of business datasets
2. Visualizing information
3. Linear and non linear predictive models
4. Introduction to data mining
5. Applications to business datasets
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.

Referral texts:
1. G. James, D. Witten, T. Hastie (2020) Introduzione all'apprendimento statistico. Con applicazioni in r. Piccin-Nuova Libraria.
2. Jank, W. (2011). Business Analytics for Managers. Springer.

Additional readings
Other reading material suggested by the teacher during the course.

The exam consists of a preparation and a presentation about the statistical analysis of a dataset selected by the student or assigned by the lecturer.
In particular, the exam aims to verify that the student has acquired the concepts presented during the lessons, is familiar with the software and has learned how to integrate this knowledge and skills to solve business problems. Furthermore, the critical and personal ability to carry out the analysis will be evaluated, even in conditions of collaboration with other students.
The assessment of the work will take into account the following elements:
• Correct use of statistical methods
• Correct interpretation of the results
• Clear language
• Originality of the analysis and variety of techniques employed for the study problem
• Ability to summarize essential and most relevant information for the problem.
Each element will adequately contribute to the formation of the grade, regardless of whether the student is an attendee or non-attendee, respecting the following gradation:
A. Scores in the 18-22 range will be assigned in the presence of:
•⁠ ⁠sufficient knowledge and applied understanding with reference to the course program;
•⁠ ⁠limited ability to apply knowledge by formulating independent judgments;
•⁠ ⁠sufficient communication skills, especially in relation to the use of the specific language of the statistical methods used;
B. Scores in the 23-26 range will be assigned in the presence of:
•⁠ ⁠fair knowledge and applied understanding with reference to the course program;
•⁠ ⁠fair ability to apply knowledge by formulating independent judgments;
•⁠ ⁠fair communication skills, especially in relation to the use of the specific language of the statistical methods used;
C. Scores in the 27-30 range will be assigned in the presence of:
•⁠ ⁠good or excellent knowledge and applied understanding with reference to the course program;
•⁠ ⁠good or excellent ability to apply knowledge by formulating independent judgments;
•⁠ ⁠fully appropriate communication skills, especially in relation to the use of the specific language of the statistical methods used;
D. Honors will be awarded in the presence of excellent knowledge and applied understanding with reference to the program, judgment ability, and communication skills.

The course consists of fifteen lessons to introduce various methods for organizing, visualizing, and analysing information available through business databases. 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).

Italian

1. Students should register in the related course web page of the university e-learning platform moodle.unive.it

2. Accessibility, Disability and Inclusion
Accommodation and support services for students with disabilities and students with specific learning impairments
Ca’ Foscari abides by Italian Law (Law 17/1999; Law 170/2010) regarding support
services and accommodation available to students with disabilities. This includes students with
mobility, visual, hearing and other disabilities (Law 17/1999), and specific learning impairments (Law 170/2010). If you have a disability or impairment that requires accommodations (i.e., alternate testing, readers, note takers or interpreters) please contact the Disability and Accessibility Offices in Student Services: disabilita@unive.it.

written and oral