64839 - Business Statistics

Learning outcomes

This course introduces students to the study of the main applied statistical methods to extract useful information from business databases and to support the management decision process. Thanks to a working knowledge of methods, at the end of the course students are able (a) to select the most appropriate statistical methodology to analyse the business phenomena, (b) to critically interpret empirical results and (c) to effectively report the analysis to non-statisticians. The course in Business Statistics has been design to be integrated with the course in Econometrics which specifically focuses on the relationships among variables. The two courses compose the course in “Quantitative methods for management I.C.”, and provide the student with a quantitative toolbox to support data-driven decision making.

Course contents

The course is designed to provide students with a working knowledge of hypothesis testing and regression analysis for understanding and interpreting multivariate data in business.

Special focus will be put on interpreting estimation results. At the end of the course students will be able to:

• understand and properly apply hypothesis testing, multiple linear regression, and probit models for binary dependent variables
• critically interpret empirical results obtained using the above tools
• correctly communicate the information contained in empirical results (special emphasis will be placed on this skill)

The following contents will be covered:

• Recap on the following key concepts/tools: random variables, Probability Density functions and Cumulative Distribution Function, Normal and Standard Normal Distribution, Use of the Standard Normal Table
• Recap on the Simple Linear Regression
• Multiple Linear Regression and hypothesis testing on regression coefficients
• Some useful extensions of the linear regression:
  • Log transformations (log-log, log-linear and linear-log models)
  • Binary variables in regressions (intercept shift and interaction terms)
    
• Probit models for binary dependent variables
• In the lab:
  • Introduction to Stata
  • Descriptive analysis and graphs in Stata
  • Hypothesis testing in Stata
  • Estimating multiple linear regressions (and extensions) in Stata
  • Estimating probit models in Stata

Readings/Bibliography

• R. C. Hill, W. E. Griffiths and G. C. Lim, "Principles of Econometircs", 4th edition, New York: John Wiley and Sons

• Stock, James H., and Mark W. Watson. "Introduction to econometrics" Pearson (any edition)

Teaching methods

During the course theoretical and practical sessions will be held.

During practical sessions empirical knowledge of the proposed methods will be reached through real-world case studies performed using Stata.

Stata is available in all the computer labs in the Campus and a Campus licence of Stata is available to all students enrolled in the course.

The UNIBO e-learning platform (VIRTUALE) will be used to share teaching materials and to assign periodical home assignments to students.

Assessment methods

Student learning is assessed through a mandatory final exam on EOL (Exams Online). The exam will take place in the computer lab. Students are required to enrol thorugh Almaesami.

The test consists of three sections:

1. Multiple choice/short-answer questions (35% of the Exam Score, 6 questions);
2. Two open-ended questions focusing on the interpretation of statistical results. This section is aimed at assessing students’ ability to understand statistical outputs and methods and to translate them into applied conclusions. If time allows for the discussion of a scientific article during the course, one of these questions may be based on that article. (20% of the Exam Score)
3. Five short essay-type questions. A dataset will be provided, and you will need to use suitable Stata commands to answer the questions. You will report the answers in a Word file and upload it together with the Stata do-file on the EOL platform. Application of the statistical methods and Stata commands covered during the course is required (45% of the Exam Score).

The test duration is 90 minutes. The exam structure may be subject to change; any modifications will be communicated in class.

The exam is partially open-book: students may consult the teaching materials available on Virtuale during the exam. During the exam, students are not allowed to bring notes or printed materials, use mobile phones or other devices, browse the web, or communicate with classmates or other individuals, whether in person, by phone, via online chat, or by any other means. The use of AI is prohibited. Any use of AI, or any of the behaviours listed above, constitutes a violation of academic integrity.

The grading system is on a 0-30 range, the following grid applies:

• <18 failed
• 18-23 sufficient
• 24-27 good
• 28-30 very good
• 30 cum laude honors

The instructor reserves the right to administer an oral exam exclusively in cases of suspected irregularities during the written exam.

Optional bonus

During the first 30 hours of the course, students will be assigned 5 weekly problem sets to be completed at home and uploaded to Virtuale by the stated deadline.

These assignments are intended to encourage regular study and practice. They do not replace any part of the programme, and the final exam will cover the whole course content.

The bonus will be awarded as follows:

• Students who submit at least 4 valid assignments out of 5 by the deadline will receive a 3-point bonus.
• Students who submit 3 valid assignments out of 5 will receive a 2-point bonus.
• No bonus will be awarded for fewer than 3 valid submissions.

An assignment will be considered valid if it is submitted on time and represents a genuine attempt to solve the assigned problems.

After each weekly deadline, one submitted assignment will be randomly selected. The student who submitted it will be required to correct the assignment at the board, discuss the solution in class and explain the reasoning. If the selected student is absent, they will be asked to discuss the assignment during the following class. The bonus will still be confirmed if the assignment contains errors, provided that the student shows that they engaged seriously with the work and understands the main steps of the solution.

Individual assignments will not be corrected or graded. Solutions will be made available on Virtuale for self-study and self-assessment.

Assignments may be completed in collaboration with classmates, and students may consult course materials, other learning resources, and, where appropriate, AI-based tools. However, each student is responsible for the assignment they submit and must be able to explain and discuss it in class.

In accordance with the University guidelines this component of the assessment allows the use of AI as a support tool. For this reason, the assessment also includes a random oral check aimed at verifying students’ ability to understand, explain, and critically evaluate the work submitted.

The weekly assignments provide an optional bonus and are not required to achieve the maximum grade. Students may obtain the maximum grade, including honours, based solely on their performance in the final written exam.

The bonus will be added to the written exam score only if the latter is at least 17/30

Teaching tools

The UNIBO e-learning platform (VIRTUALE) will be used to share teaching materials and to assign periodical home assignments to students. The teaching material is particularly rich, and it is composed of:

• Slides/Lecture notes: summarising theoretical concepts shown in class
• Stata datasets (named “Example 1”, “Example 2”, etc) used to formulate examples described in the slides (students can use these datasets to replicate examples discussed in class)
• Do files, lecture notes and Stata datasets: with these tools students are able to follow the practical sessions step by step and to completely replicate them at home.
• Stata Assignments and Solutions which will be regularly proposed to students
• Miscellanea: exercises, focus notes, sample tests will be uploaded when needed

Office hours

See the website of Sara Capacci