STATISTICS

Course Sheet

Academic Year of enrolment:

2021/2022

Divisions:

Cognomi N-Z

Type of Course:

Characterising

Optional/Mandatory:

Compulsory

Degree:

Bachelor's Degree Programme in ECONOMICS AND MANAGEMENT

Location:

SASSARI

Disciplinary Sector:

Statistics

Is part of:

STATISTICS

Credits:

Programme Year:

Professor and Collaborators:

POZZI Lucia

ESPOSITO Massimo

Hours of classroom activity:

Prerequisites:

No prerequisites are required

AF code:

40000537

Assessment Methods

Verification of learning:

The exam aims to evaluate the achievement of the educational objectives related to the knowledge of basic statistical tools.
There are two forms of examination:

1) Full written examination focused on all course topics; 2) Two partial written examinations.

Exam methods in presence and at a distance

1 Full Exam lasting 80 minutes includes 4 exercises corresponding to the same overall score; 2 exercises focus on the topics of the first part of the course, 2 exercises on the second part of the course.
Each exercise includes a variable number of points and includes the application of properties or comments on the results obtained, with a more explicit reference to elements of theory.

2) Two partial tests lasting 60 minutes each. The first partial test, relating to the topics of the first part of the course, includes 3 exercises corresponding to the same overall score. This test can only be taken at the end of the first part of the course.

The subsequent partial test, sustainable only by students who demonstrate an acceptable knowledge of the basic notions in the first test, includes 3 exercises on the second part of the course, each with the same overall score. The structure and duration of the final partial test are identical to that of the first partial test. The second part can only be taken in the three exam sessions at the end of the lessons.

The final mark is given by an average of the two marks obtained.

The minimum overall grade (in the two forms of examination) requires the demonstration of an elementary knowledge of the basic notions relevant to the issues addressed while the maximum grade requires a full mastery of the analytical tools used during the lessons.

The remote exam tests will be administered through the use of Microsoft Teams or Google Meet and using the MOODLE platform. Detailed technical information will be posted on the Moodle Statistics course page.

Teaching period

Second semester

Objectives

The aim of the course is to provide the student with the basic concepts and tools for conducting a statistical analysis.

At the end of the course, the student will have:

(1)Knowledge and understanding skills.
The student will have the knowledge and the ability to use the main methods of descriptive statistics, mastering, in particular, the main concepts and techniques for the collection, processing, summary description and study of data to conduct a statistical analysis. The student also obtains a basic knowledge of probability theory and methods of statistical inference.

(2)Ability to apply knowledge and understanding
The student will be able to: a) represent the phenomena studied, graphically and through appropriate synthesis indicators; b) perform probabilistic assessments in basic applications; c) perform interval estimates and perform simple hypothesis tests in the case of random sampling from normal statistical populations.

(3) Autonomy of judgment
Ability to interpret social economic phenomena, analyzed through the use of statistical indicators, framing them in the appropriate reference context and conducting correct comparisons over time and space.

(4)Communication skills
Although the course does not provide specific activities aimed at developing communication skills, the student will be able to operate within diverse working groups, using a quantitative analysis perspective and a statistical approach. The student will also be able to produce summary statistical reports on the main economic and social phenomena.

5) Learning skills
At the end of the course, the student will be able to use the pivotal concepts of the descriptive statistics and the basic elements of the inferential statistics to deepen the related themes in the context of advanced specialized courses.

Contents

Part 1 - DESCRIPTIVE STATISTICS
Introduction. Statistical surveys, phases of the survey. The data matrix.
Summary of the distribution of a character. Absolute and relative frequencies. Cumulative frequency distribution. Graphic representations.
Position and dispersion indices. Concentration.
Relationships between two characters. Double entry tables. Joint, marginal and conditional distributions. Independence in distribution and connection indexes. Linear dependence: covariance, correlation.
Part 2 - Linear Regression. Calculation of probabilities and Inference
The simple linear regression model.
Introduction to probability. Random phenomena and uncertainty. Events and sample space. Axioms of probability. Conditional probability.
Random variables: distribution and probability / density function. Expected value and variance. Normal distribution and standard normal distribution.
Population model and parameters. Random sampling: estimators and their sample distributions. Estimates and confidence intervals for the average of a normal distribution. Hypothesis testing: errors of I and II type, level of significance. Hypothesis testing in the case of random sampling from normal statistical populations.

Recommended Bibliography

S. Borra e A. Di Ciaccio (2021) Statistica. Metodologie per le Scienze Economiche e Sociali (III ed.), McGraw-Hill.

Teaching Methods

The course is held with online lectures and tutorials. The lessons are devoted to the exposition of the methodological concepts contained in the program, but also include examples and exercises. The tutorials include a synthetic presentation of the methodological concepts, but essentially have an applicative character (solution of exercises).

Contacts/More Information

1) Educational material containing examples, notes and exercises done during lessons and exercises, is available on the course web page on https://edisea.uniss.it/

2) Availability to provide a tutoring service (eg tutorials, explanations in the classroom at the end of the lessons) in English for Erasmus or foreign students.

For information relative to student tutorials, see:
www.edisea.uniss.it

email: lpozzi@uniss.it
phone: +39 079213002

STATISTICS

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