Unit STATISTICS FOR BUSINESS DECISIONS
- Course
- Business economics and management
- Study-unit Code
- A003024
- Location
- TERNI
- Curriculum
- In all curricula
- Teacher
- David Aristei
- Teachers
-
- David Aristei
- Barbara Guardabascio (Codocenza)
- Hours
- 21 ore - David Aristei
- 21 ore (Codocenza) - Barbara Guardabascio
- CFU
- 6
- Course Regulation
- Coorte 2022
- Offered
- 2022/23
- Learning activities
- Caratterizzante
- Area
- Statistico-matematico
- Academic discipline
- SECS-S/03
- Type of study-unit
- Obbligatorio (Required)
- Type of learning activities
- Attività formativa monodisciplinare
- Language of instruction
- Italian
- Contents
- The growing availability of large amounts of data in an always rapid and timely manner, typical of the information society, has led to the need to have adequate methodologies and tools for quantitative decision-making processes in the context of Business, Management and Marketing.
The course of Statistics for Business Decisions aims to provide knowledge about the main statistical methodologies adopted for the analysis of company data and useful for supporting managerial decisions.
The data may be from an internal source of the company, such as those relating to customers or users, or may derive from specific market research or statistical surveys conducted by the company itself.
The presence of data of different nature (qualitative and quantitative) requires the students to acquire adequate skills that allow them to justify the logic underlying the adoption of a specific analysis technique and to formulate, in a critical and rigorous way, reasoning on the main aspects economic and corporate by obtaining concise information to support decisions and the management of corporate risk situations.
The statistical methods presented range from sampling techniques to detect data of business interest, to regression models to analyse the causes of business phenomena and make predictions.
The in-depth presentation of the methods is accompanied by examples and business cases. There will be also exercises on firm-level data. - Reference texts
- Reading material on each course topic (handouts, slides, data sets, R scripts), will be made available to the students by the course instructors during the course.
Reference books:
Bracalente, B., Cossignani, M., Mulas, A. (2009), Statistica Aziendale, McGraw-Hill, Milano 2009.
Other suggested readings:
Biggeri, L., Bini, M., Coli, A., Grassini, L., Maltagliati, M. (2017): Statistica per le decisioni aziendali, Seconda Edizione, Pearson, Milano.
Bassi, F., Ingrassia, S. (2022): “Statistica per analisi di mercato. Metodi e strumenti”, Pearson, Milano. - Educational objectives
- By the end of the course students should:
Know the fundamental theoretical notions of statistical analysis for business decision.
Illustrate the main survey techniques, organizing, summarizing and graphically representing a dataset. They will also be able to formulate and conduct simple inferential procedures and regression analyses on small datasets.
Produce and interpret basic statistical analyses. The objectives of the course, thus, include the ability of recognizing the different types of data and selecting among several statistical tools the most appropriate for the problem at hand. - Prerequisites
- The statistical methods introduced in the course are strongly dependent on basic mathematical tools, therefore it is expected that students had successfully attended and completed the planned mathematics courses.
- Teaching methods
- The course is carried out through lectures and practical exercises using data. Techniques will be introduced by examples and described in mathematical formulas. Focus will be on the practical implementation of each technique, R coding and interpretation of results.
- Other information
- Attending classes is strongly advised.
- Learning verification modality
- 1) The exam includes a written test in person, lasting 1 hour and 40 minutes, divided into two parts: 2 open-ended theoretical questions and 2 exercises.
2) If for future exam sessions, students could have it also in distance, in addition to the written test in person, it will be possible to take the oral exam at a distance (interview of about 45 minutes, divided like the written test into theoretical questions and exercises).
3) Only during the face-to-face written test is it possible to consult a form (containing only formulas, without demonstrations or comments; maximum 1 A4 sheet front / back). - Extended program
- Introduction to business statistics and data sources for business analysis
The data sources for business analysis: primary sources and secondary sources. The quality of statistical information.
The European Statistical System (ESS) and the National Statistical System (SISTAN)
Internal secondary statistical sources. Presentation of the main official and unofficial external sources relevant in company analyzes
The economic indicators and the main Istat surveys. The I.stat data warehouse reading and downloading data.
2. Sample surveys for the collection of data of corporate interest
The statistical survey and its phases: formulation of objectives, identification of the reference population, statistical sampling, identification and collection methods, questionnaire design, data collection, data coding and archiving, data analysis, drafting of a research report.
Probabilistic sampling techniques and parameter estimation process.
Simple random sampling: procedure for extracting and calculating point and interval estimators. Sampling error and determination of the sample size.
Systematic sampling. Stratified sampling and related procedures for estimating parameters and constructing confidence intervals.
Non-sampling errors and related treatment methods.
Data collection techniques; the detection tool; types of questions, evaluation scales, classification of statistical features.
3 Preliminary data analysis: association measures
The data matrix; column profile analysis: the association between pairs of statistical characters; association indices for qualitative (nominal and ordinal scales), quantitative and mixed variables.
4. The regression model for the analysis of causal relationships between variables and company forecasts
The simple linear regression model: parameter estimation and related significance tests; dummy explanatory variable; the simple regression model for business forecasts.
The multiple linear regression model: reminder of matrix algebra; the estimation of the parameters and the relative significance tests; the goodness of the fit; the explanatory variables dummy and slope-dummy; the multiple regression model for business forecasts.