1. 1 STATISTICS FOR BUSINESS DECISION MAKING Academic year 2014-2015 Dott. Francesca Gagliardi

2. 2 ISSUES OF THE COURSE The course provides a panoramic view of the most important statistical tools that allow performance measurement. It allows students to reach the necessary skills in order to take decisions under uncertainty when a large amount of quantitative and qualitative information are available. The core of the course are statistical methods for business decision making.

3. 3 BACKGROUND It is a course of advanced statistics, even if more conceptual than mathematical-statistical aspects are given. Elements of basic descriptive and inferential statistics are needed.

4. 4 MATERIAL FOR THE COURSE There is not a unique book. Slides from the professor will be provided for each lesson and with them also references to textbooks.

5. 5 GENERAL INFORMATION SCHEDULED LESSONS The course begin the 7th of October 2014 with the following timetable: –Monday 8.30 Room A11 –Tuesday 8.30 Room A8 –Thursday 8.30 Room A11 –Friday 8.30 Room A11. This lesson will be done only if necessary. The professor will inform the students week by week if it will be done.

6. 6 GENERAL INFORMATION EXAM –MID TERM EXAMS Two mid term written exams are foreseen. The first at the end of the 6 credit program, the second at the end of the course. –FINAL EXAMS All the exams are written.

7. 7 GENERAL INFORMATION MEETING WITH PROFESSOR The professor can meet students every Tuesday after the lesson from 10 to 11 a.m. CONTACTS –E-mail: gagliardi10@unisi.it gagliardi10@unisi.it –Phone: 0577 232646

8. 8 Review of basic statistics The course begins with a review of basic statistical concepts needed for the development of the program

9. 9 Then, we will deal with… Marketing samples Simple and multiple linear regresssion Index numbers Balanced scorecard system Performance measurment Frontier production analysis Total and partial productivity indexes

10. 10 … External efficiency Here the course for 6 CFU ends. Then for the remaining 3 CFU: Logit model Cluster analysis Factor analysis