BUSINESS STATISTICS

Single discipline educational activity

Course Sheet

Course code:

00099L

Academic Year of enrolment:

2019

Year of supply:

2019/2020

Type of Course:

Characterising

Degree:

Course of 2nd Cycle Degree in BUSINESS ADMINISTRATION

Disciplinary Sector:

Statistics for Economics

Language:

Italian

Location:

PESCARA

Credits:

Programme Year:

Professor and Collaborators:

POSTIGLIONE PAOLO

Cycle:

Second semester

Hours of classroom activity:

Individual study hours:

102

Prerequisites:

The knowledge of Basic Statistics is requested.

Objectives

The course of business statistics aims to contribute to the student's learning process by providing the knowledge of some statistical methodologies useful for corporate governance.

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

Knowledge and understanding
The course aims to provide the methodological and application knowledge of regression models. Furthermore, we want to provide useful tools for the statistical analysis through the open source statistical package R.

Applying knowledge and understanding
At the end of the course, the student will also be able to perform regression analyses, 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. Basic concepts of probability and statistical inference.
2. R statistical software.
3. Basic concepts of linear algebra.
4. Linear regression model
5. Linear regression model and R.
6. Logistic regression model.
7. Logistic regression model and R.

Extended Syllabus

1. Basic concepts of probability and statistical inference.
Discrete and continuous random variables. Expected values, variability. Point estimation. Confidence intervals. Hypothesis test.
2. R statistical software. 3. Basic concepts of linear algebra: vectors and matrices; Determinant and inverse of a matrix.
4. Linear regression model
Simple linear regression. Multiple linear regression. Inference. Eteroschedasticity and Autocorrelation. Dummy variables.
5. Linear regression model and R.
Study cases.
6. Logistic regression model.
Definitions. Parameter Estimation and Interpretation.
7. Logistic regression model and R.

Recommended Bibliography

- Course slides.
- PICCOLO D. (2010). Statistica. Edizioni Il Mulino. (Cap. 14 pagg.491-501; 520-526; Cap. 15 pagg. 548-557, 560-561, 567-577, Cap. 16 pagg. 585-591; Cap. 17 pagg. 607-620; Cap. 18 pagg. 669-673, 677-678; Cap. 19 pagg. 731-737, Cap. 22; Cap. 23.).
-Stock JH,‎ Watson MW,
Introduzione all'econometria (2016). Pearson
(Cap. 4,5,6,7,17,18)

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 regression models is verified to be able to face concrete analysis situations.

Contacts/More Information

E-mail: postigli@unich.it
For further details and for downloading the slides: fad.unich.it, page of Statistica Aziendale - CLEA/M
In the first semester the Professor receives students only by appointment (postigli@unich.it).
In the second semester, the office hours for students is scheduled for Friday from 11:00 to 13:00, Viale della Pineta, 4, floor 2.

Università degli Studi
"G. d'Annunzio"

Chieti - Pescara

Aree