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
Basic knowledge of R software

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 assessment will take place in two ways: through a written test consisting of open-ended questions with short answers and quizzes, and a data analysis test using R, composed of two exercises.
In this second part, the student's ability to conduct data analysis to support business decisions will be tested. The laboratory part will simulate a business case with specific research questions.

The final grade will be the average of the scores obtained in the two tests, provided that a minimum score of 12 is achieved in both.
The written part (30 minutes) and the laboratory part (one hour) will be held on the same day, in this order. Each question in the written part is worth 4 points, for a total of 32 points, while each exercise in the laboratory part is worth 16 points, also totaling 32 points.

written

A (Sufficient - Pass, 18-20 points average)
The student achieves an average of at least 18 with a minimum score of 12 points in both parts, which corresponds to:
At least 4.5 correct questions in the written part.
At least one complete exercise with code and comments plus part of another in the laboratory part.
Alternatively, the student can pass by:
Scoring 12 points in the written part (3 correct questions) and at least 24 points in the laboratory part (2 exercises correct in terms of code).
Scoring 24 points in the written part (6 correct questions) and 12 points in the laboratory part (one exercise correct in terms of code, or two partially correct with code and comments).

B (Good - 21-26 points average)
The student exceeds the minimum passing requirements and demonstrates a solid understanding of the material by:
Answering more than 5 questions correctly in the written part.
Providing one fully correct laboratory exercise with code and comments and a significant portion of a second exercise.
Alternatively, a student may compensate with a stronger written performance (e.g., 6+ correct answers) or a stronger lab performance (e.g., 2+ fully correct exercises).

C (Very Good - 27-30 points average)
The student demonstrates a high level of competence by:
Answering at least 6 questions correctly in the written part.
Completing two exercises in the laboratory part with correct code and comments.
Alternatively, the student may achieve this level with an excellent performance in one part (e.g., all correct written questions) while still meeting a B level in the other part.

D (Excellent - 30 and Honors)
The student achieves the highest level of performance by:
Answering all written questions correctly.
Applying the correct code and providing thorough and accurate comments in all laboratory exercises.
This level is awarded to students who demonstrate both technical accuracy and the ability to interpret and explain their results clearly, showing how to make their analysis useful for the case study under examination.

Lectures will be complemented by R lab sessions

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