BUSINESS STATISTICS

The course aims to develop students' knowledge in the analysis of economic and business data, and to develop students' skills to apply the knowledge transmitted during the course to the real context, with particular attention to the choice of the most appropriate methods as well as to the interpretation of results. This way, students will be able to independently conduct a data analysis with the aim of supporting decision making.

Students at the end of the course are expected to:
1. know the main statistical tools for multivariate data analysis to obtain information from observed data;
2. apply the method and models learned during the course to the real context, using appropriate software;
3. be able to conduct, independently, a study of economic-business data;
4. to present the results of their analysis starting from the relevant business-economic context.

Basic knowledge of probability and statistics

1. Introduction to the course
2. How to conduct a data analysis: research design
3. Data Analysis
a. Basics of Business Analytics
b. Multivariate Statistical Analysis for business data

Commented slides and R scripts will be uploaded on the moodle page during the course.
The main referral text for case studies is:
Malhotra, N.K., 2018, Marketing Research: An Applied Orientation (7th edition). Pearson.
The main referral text for the methods is:
Anderson, D. R., Sweeney, D. J., Williams, T. A., Camm, J. D., & Cochran, J. J., Statistics for business &
economics. Cengage Learning.

The achievement of the course objectives will be assessed in two ways: through a written exam composed by open-ended questions with short answers and multiple choice questions, and a data analysis task to be solved with R, composed by two exercises (lab exam). The final mark will be assigned as the average of the marks obtained in the written and lab parts, conditional on a minimum threshold of 12 in both parts.

The written part (30 minutes) and the laboratory part (1 hour) will be held on the same day. Each question in the written part is worth 4 points, for a total of 32 points, and each exercise in the laboratory part is worth 16 points, also for a total of 32 points. To pass, students must achieve an average score of 18, conditional on a score of at least 12 points in both parts. Students pass if they achieve a sufficient score in both parts (4.5 questions correct in the written part, one complete exercise with code and comments plus part of another exercise), or by scoring 12 points in the written part (3 correct questions) and at least 24 points in the laboratory part (two exercises correct in terms of code), or by scoring 24 points in the written part (6 correct questions) and 12 points in the laboratory part (at least one exercise correct in terms of code, or two partially correct with code and comments). The highest grade will be assigned to students who correctly complete all questions in the written part, apply the correct code, and comment thoroughly and correctly on the exercises in the laboratory part. The code to be used in the exam will be provided with the exam text; it is not necessary to memorize it but to know how to apply and interpret it. At the end of the course, a mock exam will be conducted so that students can familiarize themselves with the exam format.

Lectures will be complemented by R lab sessions

English

Slides and notes, as well as R exercises, will be available on moodle during the course.

written