ECONOMIC STATISTICS
cod. 1003960

Academic year 2016/17
1° year of course - First semester
Professor
Fabrizio LAURINI
Academic discipline
Statistica economica (SECS-S/03)
Field
Statistico-matematico
Type of training activity
Characterising
63 hours
of face-to-face activities
9 credits
hub: PARMA
course unit
in - - -

Learning objectives

a) Knowledge and understanding. The course introduces the knowledge of new quantitative skills, useful in the teachings taught in other courses after this. In particular, it provides expertise on the main statistical methods for analysis of economic phenomena and business of various kinds, and deepens the problems of parameter estimation and diagnostic selection of a statistical model. These techniques include: evaluation of averages, variability, simple and partial correlation, hypothesis testing (and associated results of the elementary theory of probability); additionally, they introduce the simple linear regression model and its generalization to multiple regression, with application to cross-sectional and longitudinal problems. Attending class and solving exercises will increase the student's ability to develop autonomously, the type of "statistical decision problem" that can be faced in the economic and business environment, which characterizes the nature of the degree in International business Development.

b) Applying knowledge and understanding. At the end of the course, the student will be able to implement independently the techniques discussed above. The student will have therefore developed advanced expertise, with associated diagnostic skills, which are essential ingredients in building a good statistical model, with the possible support of an appropriate information system.


c) making judgements. At the end of the course, the student will be able to perform autonomously all the considerations regarding the problems of economic and business statistics. In addition, the student will be able to correctly interpret the results of such analysis, even when made by other users or experts. By studying the content of the course, the student matures, therefore, a high degree of autonomy aimed at the correct judgment of the application of proper technique and the associated ability to rework the quantitative knowledge acquired, with the objective of significantly maximizing the information content in terms of business development.
d) communication skills. At the end of the course, students can interact constructively with management figures of each profile. The ability to summarize the statistical information of complex nature, providing, in addition, effective quantitative synthesis, allows students to contribute with their views to the development and the creation of economic and business decision making.
e) learning skills. Students will have the opportunity to assimilate the results of key economic statistics which underlie the construction of a statistical model with applications to economic and business decision making. After completing the course, the student will have learnt the key concepts in order to accurately use quantitative tools, if they become necessary in the solution of concrete problems of business and economic.

Prerequisites

It is strongly suggested to attend the course Economics Statistics Communications Skills.

Course unit content

The course covers the basic statistical methods and analysis of economic and business data. The focus will be on a set of techniques widely used in practice. In particular, the methods considered will start from examining synthesis of a variable (frequency distributions, averages, indexes of variability), the calculation of simple index numbers ending with the study of the link between two or more variables. For every technique it will be explained the rationale and purpose of knowledge, while the little emphasis will be given to technical details and mathematical derivations, even if there will be room for the "inference" underlying many methodologies. In this logic, specific business problems that each technique, or a suitable combination thereof, can help solving will be presented (with simplified examples so that they can be carried out in detail in the classroom) and some time will be devoted to provide critical interpretation of the results. An important aspect, which will be taken up several times during the course, in the use of computers and the support that spreadsheets of Microsoft Excel can provide for the actual application of the methods discussed in class.

Full programme

- - -

Bibliography

1) Main reference
Notes covering all contents and textbook with exercises (in preparation).

2) Further references
a) Paul Newbold, William Carlson, Betty Thorne (2012) “Statistics for Business and Economics”, Pearson, 8th Edition, 792 p.
b) Cortinhas Carlos, Black Ken (2012) “Statistics for Business and Economics”, Wiley, 834 p.
c) Mark L. Berenson, David M. Levine, Timothy C. Krehbiel (2012) “Basic business statistics: concepts and applications”, Pearson, 12th Edition, 889 p.

Teaching methods

The knowledge and understanding will be assessed with 2 short exercises over which build accurate comments. Each exercise has mark of 7/30

The ability to apply knowledge will be assessed with 2 more exercises, slightly technical, whose mark value is 8/30 each.

Judgement ability to learn will be assessed through the drafting of relevant comments regarding the 2 technical exercises above.

The ability to communicate with technical language will be assessed by the appropriate links between different points of the program in the event of an oral supplementation of the test.

Assessment methods and criteria

Written exam with oral supplementation optional.

Other information

- - -

2030 agenda goals for sustainable development

- - -

Contacts

Toll-free number

800 904 084

Contacts

Toll-free number
800 904 084

Student registry office

E. segreteria.economia@unipr.it

Quality assurance office

Education manager:
dott.ssa Giovanna Colangelo
T. +39 0521 902296
E. didattica.sea@unipr.it
E. giovanna.colangelo@unipr.it

President of degree course

prof. Paolo Fabbri
E. paolo.fabbri@unipr.it

Faculty advisor

prof.ssa Donata Tania Vergura
E. donatatania.vergura@unipr.it

Carrier guidance delegate

prof. Franco Mosconi
E. franco.mosconi@unipr.it

Tutor professors

prof. Paolo Fabbri
E. paolo.fabbri@unipr.it
prof. Vincenzo Dall'Aglio
E. vincenzo.dallaglio@unipr.it

Erasmus delegate

prof. Andrea Cilloni
E. andrea.cilloni@unipr.it

Quality assurance manager

prof. Vincenzo Dall'Aglio
E. vincenzo.dallaglio@unipr.it

Internships

prof. Paolo Fabbri
E. paolo.fabbri@unipr.it

Student Tutors

dott. Dalila Baldini
E. dalila.baldini@unipr.it
dott. Chiara Bacchilega
E. chiara.bacchilega@unipr.it