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STATISTICS FOR DATA ANALYSIS Single discipline educational activity
Course Sheet Academic Year of enrolment:
Professor and Collaborators:
Hours of classroom activity:
Prerequisites:
Although no prerequisites are required, knowledge of the basic concepts of both descriptive and inferential statistics is recommended.
Objectives
Contents The course is divided into three main modules.
A) Data sources and statistical information for business decisions. Organisation of the data. Data matrix. Descriptive statistics for analysing business data.
B) Multivariate statistical analysis for business data and company business performances.
C) Measures of firm efficiency. Frontier analysis methods.
Extended Syllabus A) Data sources and statistical information for business decisions:
- primary and secondary data;
- internal and external sources of data.
Organisation of the data.
Data matrix.
Descriptive statistics for analysing business data:
- univariate exploratory analysis on accounting data;
- bivariate exploratory analysis of quantitative data. Correlation between business variables.
Simple linear regression model.
B) Multivariate statistical analysis for business data and company business performances:
- multiple linear regression model;
- case study: multiple linear regression model for analysing business performances;
- logistic regression;
- case study: logistic regression for predicting firm financial distress;
- principal components analysis (PCA);
- case study: principal components analysis on accounting ratios.
C) Measures of firm efficiency. Frontier analysis methods:
- different approaches to efficiency measurement;
- parametric methods: Stochastic Frontier Analysis (SFA);
- non-parametric methods: Data Envelopment Analysis (DEA);
- Firms businesses performances evaluation: case studies.
Recommended Bibliography Bracalente B., Cossignani M., Mulas A. (2009). Statistica aziendale. Mc-Graw-Hill, Milano.
Other supporting material provided by teacher will be available on the e-learning page.
Methods of Provision
Teaching Methods The course includes 54 hours of lessons divided into 3 weekly lessons (one lesson of 2 hours and two lessons of 2 hours) and will be organized in three modules of frontal lessons, exercises, case studies.
Frequency is recommended.
Evaluation methods Verification of learning:
The assessment of the learning level will be carried out with the use of a written test.
The written examination consists of: i) both open and multiple choice questions about topics covered during the course; ii) exercises in which students are asked to solve real case-studies using statistical methods presented during the course.
This test must be solved in one hour and a half and is aimed at probing above all "the knowledge" and the "know-how" (Dublin Descriptors 1 and 2). In particular, the exam is aimed at verifying the understanding of the topics covered, the student’s ability to apply statistical methods for business data analysis and to interpret the results obtained.
The exam score is on a 30-point scale.
The examination procedures are the same for attending and non-attending students.
Contacts/More Information All information concerning the course and other learning materials will be available on the e-learning page.
Erasmus students must contact the teacher for their program. They can take their exam in English or in French (written and oral exam).