Ateneo - UPO

The course illustrates some of the most important statistical methods for analyzing economic-business data related to business management.
The content is mainly modulated in the following areas. Descriptive statistics; Sampling and statistical inference; simple and multiple linear regression models and logistic regression model; time series analysis. All topics covered will be contextualized to the economic and business field.

Newbold, Carlson, Thorne. Statistica 2/ed. Pearson (2010)

L. Biggeri, M. Bini, A. Coli, L. Grassini, M. Maltagliati. Statistica per le decisioni aziendali. Pearson (2017)

D.C. Montgomery. Controllo Statistico della Qualità. Seconda Edizione. McGraw hill (2006)

Further teaching material prepared by the professor will be published on the web page of D.I.R. (https://www.dir.uniupo.it)

The course aims to deepen some basic concepts of descriptive statistics, sampling and statistical inference, regression models and time series analysis. The aim of the course is to provide the appropriate tools to deal with quantitative and statistical information problems with critical competence and ability, and to be able to properly interpret the results of statistical and quantitative analyses. Along with the mathematical-statistical aspects, the course develops conceptual and logical aspects and application to concrete cases, also with the help of an appropriate software.

An equivalent knowledge to that acquired in the following courses is required: Mathematical Methods I and II and Statistics of the bachelor degree CLEA. (Detailed programs and Lecture Notes can be found on their specific D.I.R. web pages; an equivalent textbook is: Fulvia Mecatti, Statistica di base, 2/ed., McGraw-Hill.)

Lectures including both theory and exercises, also with a dedicated softwares.

Further information will be published during the course on D.I.R. (https://www.dir.uniupo.it)

The exam consists of a discussion on a case study to verify the practical knoledge and an oral exam to verify the methodological knowledge acquired during the course.
Please note that after the third attempt to take the exam in a calendar year, enrollment is automatically blocked until the following calendar year.

1. Descriptive statistics: brief review of the main techniques for analyzing qualitative and quantitative statistical variables.
2. Sampling: the simple random sample and sample distributions. Sampling from finite populations (with applications to auditing).
3. Statistical Inference: confidence intervals and hypothesis testing.
4. Regression models: linear correlation analysis. Simple and multiple linear regression models. Logistic regression.
5. Time series analysis.
The program may be subject to change.

Students will be expected to know and understand the statistical tools introduced in the course, know how to apply them to small data samples and know how to interpret the results.