06445 - Business Statistics

Course contents

Statistical Data for Business Management

National and international official and unofficial statistics; big data from the web; data from management information systems; internal surveys (limitations and opportunities of directly collecting information from employees, suppliers, and consumers).

Organization and Validation of Information (Review)

Data quality. Preliminary analyses: handling missing data, identification and treatment of outliers, standardization techniques.

Statistical Methods for Business Decision-Making (Descriptive Approach)

Basic Key Performance Indicators (KPIs): averages, moving averages, exponential smoothing. Risk-based KPIs: mean-standard deviation charts, momentum indicators (oscillators), RiskMetrics (overview).
KPIs for complex phenomena: composite indicators for business and finance. Construction of composite indicators: conceptual model and statistical weighting (aggregation) techniques of elementary indicators.
Communicating KPIs to different business departments.

Statistical Methods for Business Decision-Making (Inferential Approach)

Identifying trends: mathematical functions – linear regression – versus stochastic trend models. Credit risk assessment (logistic regression). Non-linear classification and prediction techniques (supervised machine learning). The Delphi method.

Evaluation of statistical models in business (measuring predictive accuracy).
Expected loss and decision maker’s risk appetite. Evaluating predictive accuracy: predictors, prediction errors, loss/cost functions, optimality of predictors under subjective loss functions. Ranking predictive accuracy: descriptive approaches and inferential tests.

Readings/Bibliography

Teacher's slides

Teaching methods

The course is delivered both in the classroom (where theoretical aspects are covered) and in the laboratory, where simulations and business/case studies are conducted.

For students planning to attend the laboratory sessions in person, it is mandatory to complete prior training on safety in study environments via e-learning https://elearning-sicurezza.unibo.it/ (modules 1 and 2).

OPTIONAL (i.e., for a limited number of students), it is possible to apply to discuss a case study with the teacher (in the classroom) (no expected loss or gain).

Assessment methods

The exam aims to verify the achievement of the following learning objectives:

• knowledge of the statistical tools presented during the lectures

• ability to apply these tools

• ability to use the results obtained to interpret the phenomenon under study and to support decision-making processes.

The exam is oral, usually preceded by a written test that includes theoretical questions on methods and exercises where students demonstrate their ability to apply the acquired tools. Each question is scored from 0 to 36 points, with 6 points assigned for no answer. The final written test score is calculated as the average of the individual question scores.

During the exam, the use of supporting materials such as textbooks, handouts, notes, or electronic devices is not allowed.

Registration for the exam is required via the electronic notice board, respecting the established deadlines. Students who fail to register by the deadline must promptly notify the academic office (and in any case before the official closure of registration lists). Admission to the exam in such cases is at the instructor’s discretion.

The exam grade is recorded on the date scheduled and indicated in Almaesami. Students may review their written test and request clarifications before the oral exam. Alternative office hours for reviewing the test may be granted only in exceptional cases with valid justification. The grade may be recorded in the student’s absence.

Teaching tools

Teacher's slides. Computer sessions with Excel, Gretl and R

Office hours

See the website of Andrea Guizzardi