DATA ANALYSIS

The course is one of the interdisciplinary activities of the three-year degree course in Business Economics and Management that allows students to acquire the knowledge and understanding of some of the main statistical concepts and their use in administrative and business management activities. The aim of the course is to provide students with skills that allow them to view, extract and interpret information from sample surveys and available databases to plan strategies to support decision making.

By the end of the course, through frontal lectures, the study and analysis of the reference texts and the suggested materials, students will be able to:

1. KNOWLEDGE and UNDERSTANDING
1.1 know the terminology and basic principles of descriptive and inferential statistics of analysis of business phenomena

2. ABILITY to APPLY KNOWLEDGE and UNDERSTANDING
2.1 extract, interpret and communicate information originating from sample surveys and available databases
2.2 understand the main aspects of the descriptive and inferential statistical analyses
2.3 choose and apply statistical models for the analysis and prediction of business phenomena


3. MAKING JUDGEMENTS
3.1 critically assess both the reliability of the assumptions underlying the analyzes carried out and the goodness of the proposed models and the results achieved.
3.2 assess the goodness of the models proposed and the results achieved

4. COMMUNICATION
4.1 present information extracted from sample surveys and available databases
4.2 successfully analyze the proposed models and the results achieved

Ethics&Responsibility:
Numbers do not lie, but their interpretaion and representation can be misleading. Ethics in statistics is more than good practice: the responsible statistical practitioner seeks to understand and mitigate known or suspected limitations, defects, or biases in the data or methods and communicates potential impacts on the interpretation, conclusions, recommendations, decisions, or other results of statistical practices.

Statistics, in particular students should be able to handle basics of descriptive and inferential statistics

During the course the following topics will be analyzed:

1. Refesh inferential statistics: point estimation and hypothesis testing
2. The analysis of dependence: refresh correlation and presentation of non metric correlation
3. The analysis of dependence: simple regression
4. The analysis of dependence: multiple regression

In order to support the theoretical knowledge acquired during the course, each theme may be developed also through the use of the statistical software R.

- On-line material available in the moodle Platform from the course web site
- Book
in English: Hermann C, Schomaker M, Shalabh. Introduction to Statistics and Data Analysis. Springer, 2016
in Italian: Paganoni A.M., Ieva F. and Vitelli V. (2016). Laboratorio di statistica con R, 2 edizione, Pearson

Students will be assessed by written examinations in the form of exercises covering the material proposed in class-

written

Regarding the grading scale (criteria for assigning grades):
A. Scores in the 18-22 range will be assigned in the presence of:
Sufficient knowledge and understanding of the course program;
Limited ability to apply knowledge and formulate independent judgments;
Sufficient ability to communicate using the appropriate technical language of the subject.

B. Scores in the 23-26 range will be assigned in the presence of:
Fair knowledge and understanding of the course program;
Fair ability to apply knowledge and formulate independent judgments;
Fair ability to communicate using the appropriate technical language of the subject.

C. Scores in the 27-30 range will be assigned in the presence of:
Good to excellent knowledge and understanding of the course program;
Good to excellent ability to apply knowledge and formulate independent judgments;
Good to excellent ability to communicate using the appropriate technical language of the subject.
D. Honors will be awarded in the presence of outstanding knowledge and applied understanding of the program, excellent judgment skills, and exceptional communication abilities.

Teaching:
The programme will develop with a careful balance of teaching and learning. This is delivered by Lectures where theorethical concepts are presented alternated with exercise sessions that are solved in class. Lectures will take a variety of approaches. In some lectures, the lecturer will focus on presenting new material, often writing out arguments, examples and calculations by hand and adjusting the pace of the delivery to suit students’ understanding. In other lectures, students may be expected to have studied material beforehand and the lecture will be an interactive session to develop students' understanding.

Independent learning:
Students are expected to spend significant time on independent study. This will typically include accessing resources online, reading journal articles and books, reviewing lecture notes and practising with the exercises.

Group Learning:
Students are expected to spend significant time also on working in groups to solve the exercises. This will give them the opportunity to deepen their understanding and develop improved communications and team work skills