STATISTICS FOR DATA ANALYSIS

Single discipline educational activity

Course Sheet

Course code:

STANDA18

Academic Year of enrolment:

2018

Year of supply:

2018/2019

Type of Course:

Characterising

Degree:

Course of 2nd Cycle Degree in MANAGEMENT AND ECONOMICS

Disciplinary Sector:

Statistics

Language:

Italian

Location:

PESCARA

Credits:

Programme Year:

Professor and Collaborators:

ZA STEFANO

Cycle:

Second four months

Hours of classroom activity:

Individual study hours:

171

Prerequisites:

Although no prerequisites are required, it is recommended to know the essential concepts of basic statistics.

Objectives

Educational goals:
Considering the general objectives of the master degree, the course pursues the aim of providing students with advanced knowledge and skills also in the quantitative field. In fact, one of the main purposes of the master degree is to train professionals with mathematical-statistical skills for understanding information systems for the processing of statistical data as well as
for risk analysis. Therefore, the specific objective of the course is to allow students to become familiar with the statistical-informatics methods for data processing, analysis, forecasting, presentation and interpretation of the results of some advanced statistical methodologies.

Expected learning outcomes:
The course aims to provide students with theoretical knowledge and techniques necessary for an in-depth understanding of business phenomena through the analysis of available data.
With respect to the professional profile that the
course of study aims to train, teaching is aimed at
develop the following skills:

1) Knowledge and understanding:
- in-depth and specialized knowledge necessary for the collection and organization of business data;
- in-depth and specialized knowledge necessary for the analysis of business data through the statistical software R;
- in-depth and specialized knowledge necessary for the presentation of the results obtained from the analysis of business phenomena.

2) Autonomy of judgment:
- ability to independently choose the most suitable type of analysis based on the reference context and the type of data available;
- ability to interpret the results obtained autonomously through an advanced statistical analysis.

3) Communication / application skills:
- to use the appropriate statistical terminology with respect to the type of analysis conducted;
- to apply the acquired knowledge for the diagnosis and understanding of business phenomena;
- cleverly communicate the results of a statistical analysis based on the objectives to be pursued and the recipient of the report.

Contents

1. The R Programming Environment.
2. Data organization: Theory and practice with the statistical software R.
3. Matrices and vectors for data analysis: Theory and practice with the statistical software R.
4. The exploratory analysis of information: Theory and practice with the statistical software R.
5. Analysis of variance: Theory and practice with the statistical software R.
6. The multiple linear regression model: Theory and practice with the statistical software R.
7. The logistic regression model: Theory and practice with the statistical software R.
8. The statistical analysis with three variables: Theory and practice with the statistical software R.
9. Factor analysis: Theory and practice with the statistical software R.
10. Cluster analysis: Theory and practice with the statistical software R.
11. Customer satisfaction: Theory and practice with the statistical software R.
12. Analysis of business data using real datasets.

Extended Syllabus

1. The R environment. Arithmetic and logical operators. Working with objects. Vectors. Matrices. Array. Data Frame. Lists. Functions.
2. Data organization: Theory and practice with the statistical software R. Extraction and modification of elements from objects. Subsetting. Missing data. Import and export of data. R packages.
3. Matrices and vectors for the analysis of data: Theory and practice with the statistical software R. Fundamental concepts. Operations between vectors and matrix calculation with R.
4. Exploratory analysis of information: Theory and practice with the statistical software R. Univariate analysis. Bivariate analysis. Outliers. Transformations of variables. Coding of variables. Graphical representations.
5. Analysis of variance: Theory and practice with the statistical software R. Parametric hypothesis testing. ANOVA. Multiple comparisons. Overview of Non-parametric hypothesis testing.
6. The multiple linear regression model: Theory and practice with the statistical software R. Recall of theory. Estimation of the model parameters. Graphic analysis of residues. Heteroskedasticity. Instrumental variables. Multicollinearity.
7. The logistic regression model: Theory and practice with the statistical software R.
8. The statistical analysis of three variables: Theory and practice with the statistical software R. Mediation effect. Moderation effect.
9. Factor analysis: Theory and practice with the statistical software R.
10. Group analysis: Theory and practice with statistical software R. Hierarchical and non-hierarchical methods. Aggregative and divisive methods. k-means.
11. Customer satisfaction: Theory and practice with the statistical software R.
12. Analysis of business data using real datasets. Exercises on datasets provided during the course.

Recommended Bibliography

Main books:
1. Tonio DI Battista, 2014. Metodi statistici per la valutazione. Franco Angeli.
2. James H. Stock and Mark W. Watson, 2009. Introduzione all’econometria. Pearson Addison Wesley. Edizione Italiana a cura di Franco Peracchi.
3. Il materiale didattico integrativo per le esercitazioni con R sarà pubblicato dal docente sulla piattaforma online www.elearning.unich.it
Further books:
1. Domenico Piccolo, 2010. Statistica. Il Mulino.
2. Domenico Piccolo, 2010. Statistica per le decisioni. Il Mulino.
3. Raoul Coccarca e Fabio Frascati, 2015. Manuale interattivo di statistica con R. Pearson.

Methods of Provision

Conventional

Teaching Methods

Teaching is structured in 54 hours of frontal teaching, divided into theoretical lessons, workshops with R, and seminars.

Evaluation methods

Verification of learning:

The assessment of students' learning level will be carried out with the use of a written test (using a personal computer) and a subsequent mandatory oral test.
The written test consists of exercises related to the methods that will be illustrated during the course and some questions about the theory.
The duration of this test will depend on the degree of difficulty of the proposed questions and will be communicated during the course.
The main objective of the practical test (written) is to probe "knowledge" and "know-how". This represents 80% of the overall evaluation (expressed in thirtieths) articulated in relation to the training objectives to be evaluated.
The oral exam (20% of the overall assessment) is aimed at probing communication skills, mastering the specific technical language of the discipline dealt with, clarity of exposition and the ability to interpret.
The exam methods are the same for attending and non-attending students.

Contacts/More Information

ERASMUS students are invited to contact the teacher for their program. ERASMUS students can discuss the exam in English.

Università degli Studi
"G. d'Annunzio"

Chieti - Pescara

Aree