65877 - Economic Statistics in Agrofood Systems

Learning outcomes

At the end of the course the student knows the database management system (DBMS), the notions of descriptive statistics, as well as the bases of statistical inference and the significance tests used in the analysis of variance and regression. In addition, the student is able to critically analyze the main statistical sources (national, european and global) and the related structural analyzes of the agricultural and agri-food sector, with particular regard to the mathematical models of analysis used. At the end of the theoretical study phase, the student is able to structure and manage a data base, to construct the statistical analysis of the data collected and to carry out the relative publication of the results. Some basic notions of national accounting complete the student's preparation, with particular reference to the economic accounts of agriculture (Sec95).

Course contents

1. The Data Base (total teaching unit: 6 hours)

1.1 Definition of data base

1.2 Structure and objects of the database

1.3 The SQL language

1.4 Database management

Unit 1: acquired skills

The student is able to:

a) Apply the data management tools

b) Use the main software for managing a database.

c) Structure a database for the subsequent statistical analysis.

 

2. Descriptive statistics (total teaching unit: 10 hours)

2.1 Univariate statistical analysis

2.1.1 Numerical and graphical representation of distributions

2.1.2 Average values, variability and concentration measures

2.1.3 Density curves, normal and standard normal distributions

2.2 Bivariate statistical analysis

2.2.1 Double entry tables

2.2.2 Diagrams for dispersion, correlation and simple linear regression (overview of multiple regression)

Unit 2: acquired skills

The student is able to:

a) Know the main average values and dispersion measures of the distributions.

b) Include some graphs useful for representing statistical distributions.

c) support with appropriate theoretical-applicative tools the type of relationship existing between two quantitative variables.

 

3. From data analysis to statistical inference (total teaching unit: 10 hours)

3.1 Probability and sampling

3.1.1 General rules of probability and random variables

3.1.2 Sampling and central limit theorem

3.1.3 Confidence intervals

3.1.4 Significance test

3.2 Inference on the variables

3.2.1 Inference for the average and for the proportion of a population

3.2.2 Comparison between two averages

3.3 Inference on relationships

3.3.1 The chi-square test

3.3.2 Comparison between more than two averages: one-way analysis of variance

3.3.3 Inferences on the regression

Unit 3: acquired skills

The student is able to:

a) Know the concepts concerning the sample distributions necessary to understand the inference.

b) Include the distribution of the sample mean and the central limit theorem.

c) support, with appropriate theoretical-applicative arguments, the conceptual structure that exists in the dependence between two variables.

 

4. Statistics of the agro-industrial system (total teaching unit: 10 hours)

4.1 Structure of agro-industrial enterprises

4.1.1 Survey on the structure of farms 2013

4.1.2 Structure of the food industry and distribution

4.2 Business trend of the sector

4.2.1 Economic accounts of agriculture

4.2.2 Other components of the agro-industrial system

4.2.3 Food consumption of households and foreign trade in Italy

4.2.4 Price index numbers

Unit 4: acquired skills

The student is able to:

a) Know the main statistical sources of the sector

b) analyze the results of economic censuses

c) support the composition of the agro-industrial system with valid theoretical and applicative arguments, as well as the dynamics of food consumption and prices.

 

5. Practical practical exercises and seminars (total 24 hours unit)

5.1 Practical applications (also with the use of the PC) concerning the structuring and management of a database

5.2 Practical applications (also with the use of the PC) and problem solving concerning the descriptive statistics

5.3 Practical applications and problem solving concerning sampling and statistical inference

5.4 Seminars for the study and analysis of statistical sources concerning the agro-industrial sector.

Unit 5: acquired skills

a) Correct approach to the statistical analysis (univariate and bivariate) and to the graphical representation of the data.

b) Acquisition and mastery of the main indicators of descriptive statistics and precautions on the use of significance tests.

c) Ability to critically apply the methodologies studied to find solutions to socio-economic problems highlighted in specific case studies.

Formative prerequisites: even if there are no compulsory propaedeutical activities of the single training activities, it is useful that the student, before tackling this discipline, has followed the teachings of mathematics and principles of economics..

Readings/Bibliography

Moore D.S. “ Statistica di base", Apogeo, 2a edizione 2013, Milano.

Handouts and material made available by the teacher.

Specific didactic papers.

Teaching methods

The course is divided into 5 teaching units of which the first 3 are theoretical and 4.a and 5.a of applicative nature. The first 3 units consist of lectures, while the last two include supplemental exercises and / or seminars on the topics covered in the course or specific case studies. In particular, the first three theoretical units, of a methodological nature, are proposed as preparatory, 4.a focuses on the statistical-economic analysis of the agro-industrial system, 5.a on practical applications.

Assessment methods

The exam can be done in two ways, chosen by the student.

The first consists of a practical technical test (also with the use of the PC) aimed at verifying the level of knowledge acquired. This mode is reserved for students who work in structured work groups during the lessons.

The second one takes place through a written test, with an indicative duration of 40-45 minutes.

Both methods tend to ascertain not only the skills acquired in the 5 teaching units, but also the achievement of an organic vision of the topics developed in the lessons.

Teaching tools

Blackboard, computer with video projector, internet access.

Office hours

See the website of Giuseppe Palladino