Course 2015-2016 a.y.

Go to class group/s: 14

GIO (6 credits - I sem. - OB  |  SECS-S/01)

Course Director:
SIMONE PADOAN

Course Objectives

This course aims to provide a high understanding of quantitative methods so that, after its attendance, students will be able to perform data analysis to support management decision making.

The course is delivered with an emphasis on introductory and advanced concepts of statistics for data analysis, where statistical techniques are taught in order to give students confidence in preparing accurate and informative data summaries, experiments, surveys and interpretation of research management reports. The practical activities are implemented using specific statistical software STATA. It is essential that students develop skills for data processing as well as the interpretation of results.

Course Content Summary

• Simple and multiple regression.
• Anova.
• Longitudinal data analysis.
• Factorial analysis.
• Categorical data analysis.
• Logistic regression.
• Loglinear regression.
• Sampling.

Detailed Description of Assessment Methods

• Examination: 21/30.
• Assignment: 9/30.
  

At the end of the course there is an exam to test the knowledge acquired. There is a written exam with questions and exercises on the topics taught in class (see the short description). The maximum grade for the final exam is 21 points. Alternatively, students can complete two mid-term exams during the course. The maximum grade for the mid-term exams are 10 and 11 points respectively. If the second mid-term exam is not handed in, the student must take the final exam. During the course there is also an assignment (empirical analysis) that students should perform on their own or in groups and hand in before the end of the course. This assignment is worth a maximum of 9 points. The grade of the assignment is valid also for the following academic years, hence do not need to repeat the assignment. The examination procedures are the same for students who attend and do not attend the classes.

Textbooks

• Slides and other materials from the course.
• A. Agresti, B. Finlay, Statistical Methods for the Social Sciences, Prentice Hall, 2009,fourth Edition.
  

Exam textbooks & Online Articles (check availability at the Library)

Prerequisites

This course requires background knowledge of elementary statistics such as introductory concepts of descriptive and inferential statistics. It is sufficient to have attended at least one basic statistics course during a three year degree. For students who have not taken a course in statistics or have a superficial knowledge of statistics, it is recommended that they attend the training course before the start of the academic year.

Last change 07/07/2015 11:49