ECONOMIC STATISTICS

Single discipline educational activity

Course Sheet

Course code:

000100L

Academic Year of enrolment:

2017

Year of supply:

2018/2019

Type of Course:

Characterising

Degree:

Course of 2nd Cycle Degree in ECONOMICS AND COMMERCE

Disciplinary Sector:

Statistics for Economics

Language:

Italian

Location:

PESCARA

Credits:

Programme Year:

Cycle:

Second semester

Hours of classroom activity:

Individual study hours:

153

Prerequisites:

The knowledge of Basic Statistics is requested.

Borrowed from

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DATA SCIENCE IN ECONOMICS

ECONOMICS AND BUSINESS ANALYTICS

Objectives

The course of data science in economics aims to contribute to the student's learning process by providing methodologies for quantitative analyses that are useful for economic and business decisions.

In particular, these learning objectives can be associated with the following expected learning outcomes:

Knowledge and understanding
The course aims to provide the basic methodological and application knowledge of data science. Furthermore, we want to provide useful tools for the statistical analysis of certain types of economic and business data. Finally, great attention will be given to the open source statistical package R.

Applying knowledge and understanding
At the end of the course, the student will also be able to analyze data- bases, including large ones, with modern statistical techniques, with the help of case studies carried out with the statistical software R. The acquired knowledge will allow him/her to critically interpret the economic and / or business dynamics.

Contents

1. Introduction to R statistical software. Explorative analysis with R. 2. Statistical methods for the analysis of economic data:
2.1 Multiple linear regression with R.
2.2 Multiple linear regression for spatial data with R.
2.3 Logistic regression model with R.
2.4 Regression trees with R.
2.5 Principal Component Analysis with R.

Extended Syllabus

2.3 Logistic regression model with R.
Qualitative response variable. Estimate and interpretation of the results. Using R for logistic regression.
2.4 Regression trees with R.
Definition of the problem. Division criteria. Pruning. Using R for regression trees.
2.5 Principal Component Analysis with R.
Definition and derivation of principal components. Interpreting Principal Components. Using R for Principal Component Analysis.

Recommended Bibliography

Course slides.
Further readings:
Giudici P, Figini S (2009). Applied Data Mining for Business and Industry. Wiley
Ledolter J. (2013). Data Mining and Business Analytics With R. Wiley Shmueli G, Bruce PC, Yahav I, Patel NR, Lichtendahl KC, Jr. (2018). Data Mining for Business Analytics. Wiley

Methods of Provision

Conventional

Teaching Methods

Lectures.
R practice and exercises.

Evaluation methods

Verification of learning:

Knowledge and understanding
The verification of the learning outcomes will be carried out through a written and oral examination. The written exam will cover the whole program with particular attention to the use of R software. Students will also have to prepare and discuss a statistical analysis, carried out with R, concerning a real case study (data sets can be found on the internet ). This document must be sent to the Professor at least one week before the exam date.
The score of the exam is assigned by a vote expressed in 30.

Applying knowledge and understanding
During the exam and the development of the applied work, students' ability to apply the knowledge of data science

Contacts/More Information

E-mail: postigli@unich.it
Professor's website: http://www.unich.it/~postigli/Home.html
In the first semester the Professor sees students only by appointment (postigli@unich.it). In the second semester, the office hours for students is scheduled for Wednesday from 2 pm to 4 pm, studio DEC 2 ° floor, Viale della Pineta, 4.

Università degli Studi
"G. d'Annunzio"

Chieti - Pescara

Aree