Learning objectives
Aim of the course is the deepening of some statistical models which are of particular relevance in the firm: the design of experiments, the discriminant analysis, Trees with machine learning.
During the course it is also developed a comparison between Machine learning and some statistical models.
Prerequisites
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Course unit content
1. Analysis of variance and design of experiments.
Different designs of experiments and corrispondent models of analysis of variance..
Response Surfaces of first and second order.
Method of the steepest ascent.
Covariance analysis.
Analysis of variance with stochastic factors.
Part 2. Evaluation of firm with discriminant models.
Part 3. Machine learning.
Trees for explorative analysis.
Trees and regression.
Full programme
1. Analysis of variance and design of experiments.
Models with one, two, three factors.
Different designs of experiments.
Response Surfaces of first and second order. Method of the steepest ascent.
Covariance analysis.
Analisys of variance with stochastic factors.
2. Valuation of companies.
Istat and firms.
Flows of funds.
Linear discriminant analysis.
Logistic discriminant analysis.
3.Machine learning.
Explorative analysis with Trees.
Trees and regression.
Valuation of companies using Trees.
Bibliography
Montgomery D. C.
Design and Analysis of experiments.
2006 McGraw-Hill. (Chapters indicated during the course).
B. Fleury
A first course in multivariate statistics, Springer, 1997, New York. (Chapters indicated during the course).
Lantz B. Machine learning with R. Packt Publishing, Birminghan. Open source.(Chapters indicated during the course).
Teaching methods
Lectures and laboratory with R.
Assessment methods and criteria
Oral exam.
In the exam the student has to show knowledge of the different models indicated in the programme and developed during the course.
Other information
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