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 are able to:

• understand and use properly hypothesis testing and some extensions of multiple linear regression
• 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:

Module 1 (30 hours)

• 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 of Multiple Linear Regression, Hypothesis testing / significance test
• 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)
• In the lab:
• Introduction to Stata
• Descriptive analysis and graphs in Stata
• Hypothesis testing in Stata
• Estimating multiple linear regressions (and extensions) in Stata

Module 2 (15 hours)

• limited dependent variable models (probit and logit)
• environmental applications of probit and logit models, focusing on non-market valuation through stated preference techniques and contingent valuation
• In the lab, students will engage in econometric analysis of contingent valuation data 

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)

 

For further insights on environmental economics and environmental assessment topics:

• Perman, R., Ma, Y., McGilvray, J., & Common, M. (2003). Natural resource and environmental economics.
• Garrod, G., & Willis, K. G. (1999). Economic valuation of the environment.

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. Moreover, 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.

Home assignments will serve to reinforce class concepts and get familiarity with the software. Students are allowed and encouraged to work together on home assignments. However, a separate write-up is expected from each student, in his/her own words. Home assignments will not be graded, solutions will be provided for self-assessment.

Assessment methods

Student learning is assessed through a mandatory final exam and a group project. The test accounts for 70% of the final grade, while the group project contributes 30%. The final grade is a combination of these two components.

The final exam will be a written test in computer lab. Students are required to enrol using Almaesami.

The test contains three sections: 1) multiple choice/short-answers section (35% of Exam Score, 6 questions); 2) free response section on regression outputs (20% of Exam Score, 2 questions); 3) practical section with Stata (45% of Exam Score, 5 questions). The test duration is 75 minutes. (Please notice that the test structure might change; any modifications will be communicated in class).

Work on a group project: groups of 2-3 students will work in teams on a contingent valuation analysis. Details about group projects will be provided in class.

The final grade 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

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

See the website of Elena Benedetti