Università degli Studi di Napoli "Parthenope"

Teaching schedule

Academic year: 
2020/2021
Belonging course: 
Course of Master's Degree Programme on MARKETING AND INTERNATIONAL MANAGEMENT
Disciplinary sector: 
ECONOMIC STATISTICS (SECS-S/03)
Language: 
Italian
Credits: 
9
Year of study: 
2
Teachers: 
Cycle: 
First Semester
Hours of front activity: 
72

Language

Italian

Course description

The course aims to provide students the knowledge useful for the measurement, processing and synthesis of business and market phenomena. The course also aims to combine the theoretical approach with applications, thus the presentation of statistical methodologies for the analysis of business data will be integrated with exercises that allow students to acquire the ability to apply the methods learned and to correctly interpret the results.

Expected learning outcomes:
Knowledge and understanding:
The student should be able to understand the methodologies for data analysis to be used to achieve the required objectives and be able to interpret the results obtained.
Applying knowledge and understanding:
The student should be able to identify the most appropriate statistical method for solving the proposed problem with respect to the type of data available. The student should be able to analyse statistical data also through specific software and to communicate the results effectively.
Making judgements:
Once the main statistical methodologies for data analysis have been learned, the student should be able to understand their limits and areas of application. The student will thus be able to use the learned tools critically.
Communication:
The student should be able to explain clearly and exhaustively and with an appropriate technical language the characteristics of the data, the methods of analysis and the results obtained.
Lifelong learning skills:
The student should be able to demonstrate a good learning ability and autonomy in deepening the topics of the course based on additional bibliographic references provided by the teacher.

Prerequisites

Basic knowledge of descriptive statistics and main notions on random variables and probability distributions. However, during the first lessons of the course, the main contents of basic statistics will be revisited in light of the applications.

Syllabus

Review of descriptive and inferential statistics. Insights into hypothesis testing. Hypothesis testing for dependent population. Analysis of variance (ANOVA): one-way ANOVA and two-way ANOVA. Post-hoc tests.
Simple and multiple linear regression. The hypotheses and their verification. Inference in regression models. Regression with dichotomous explanatory variables and with interaction variables. Procedures for defining the optimal regression model. Comparison between models.
Logistic regression models for binary, ordinal and multinomial data. Main methods of data reduction. Distance matrix and dissimilarity index. Cluster analysis. Classification methods.

Part I (24 hours)
Review of descriptive and inferential statistics. Insights into hypothesis testing. Hypothesis testing for dependent population. Analysis of variance (ANOVA): one-way ANOVA. Post-hoc tests.

Part II (24 hours)
Simple and multiple linear regression. The hypotheses and their verification. Inference in regression models. Regression with dichotomous explanatory variables and with interaction variables.

Part III (24 hours)
Procedures for defining the optimal regression model. Comparison between models. Logistic regression models for binary, ordinal and multinomial data. Analysis of variance (ANOVA): two-way ANOVA

Teaching Methods

The course is organised in traditional lectures and exercises with the active participation of the students. The methodologies for the quantitative analysis of business phenomena will be introduced and, at the same time, their implementation will be carried out through Excel and SPSS.

Textbooks

-Levine, D.M., Szabat, K.A., Stephan, D.F. Statistica (settima edizione). Pearson (cap. 2, 3, 12, 13).
-Anderson D., Sweeney D., Williams T. (2014), Statistica per le analisi economico-aziendali, Maggioli Editore (cap. 8, 9, 10, 11.1).
-Giuliani, D., Dickson, M.M. (2015), Analisi Statistica con Excel, Apogeo Education.

Learning assessment

The learning assessment is on an ongoing basis during the course through the discussion of the works prepared by the students and, in the sessions scheduled by the academic calendar, through an oral examination on all the topics of the program. The final grade takes into account all the steps described in the evaluation process.

More information