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Syllabus

EN IT

Learning Objectives

LEARNING OUTCOMES:
The course in Business Statistics provides an introduction to the modelling of economic and management variables using regression and multivariate methods, both in a parametric than a nonparametric framework; the emphasis is on business, marketing and industrial applications. The program will cover models for the analysis of dependence (linear regression, ANOVA, autoregressive model, logit and probit models) and exploratory techniques for data reduction (principal component analysis and clustering analysis).

KNOWLEDGE AND UNDERSTANDING:
Knowledge and understanding of parametric and nonparametric statistical techniques applied to marketing, sales and financial problems. At the end of the course students should be able to understand: (i) how to apply statistical models in a supervised and unsupervised approach; (ii) perfectly know the model’s assumptions and understanding of the tools needed to verify these hypotheses; (iii) understand the model selection techniques and measures of the model prediction capability. In particular, students will manage:
• Multiple Linear regression model
• Logit and Probit model
• Analysis of Variance (ANOVA)
• Autoregressive model AR(1)
• Cluster Analysis
• Principal Component Analysis

APPLYING KNOWLEDGE AND UNDERSTANDING:
Practical evidence of the concepts will be given with examples using statistical software such as STATA and SAS applied on real datasets. The students will have to practice both in class that with homeworks on the use of specific software so to be able to comment and understand the output.

MAKING JUDGEMENTS:
Students will be able to choose the more appropriate statistical techniques and to select the right set of explanatory variables. On the basis of results obtained, they will be able to give an interpretation about the relationship between the variables under study.

COMMUNICATION SKILLS:
Students will be able to prepare statistical reports using graphs, tables, figures and commenting them.

LEARNING SKILLS:
Students will have access to reading and understanding scientific articles using the multivariate methods considered in the course program. They will be able to identify the most appropriate methods to answer specific research questions.

Prerequisites

In order to understand the contents of the lessons and achieve the educational objectives, it is important for the student to have a basic knowledge of statistics, probability theory and time series analysis.

Program

1. Basic statistics, linearity, and non-linearity
2. Analysis of time dependent data (AR,MA,ARIMA,ARIMAX,SARIMAX models)
3. Introduction to machine learning
4. Linear Regression
5. Classification
6. Resampling methods
7. Model selection and regularization
8. Non-linear models: Splines, Local regression, and GAMs
9. Tree based models: Bagging, Boosting and Random Forests
10. Support vector machines
11. Unsupervised learning
12. Artificial neural networks

Books

- Gareth James - Daniela Witten - Trevor Hastie - Robert Tibshirani
An Introduction to Statistical Learning with Applications in R (2021)
Freely available at
https://hastie.su.domains/ISLR2/ISLRv2_website.pdf

- Brett Lantz (2013) Machine Learning with R
Freely available at https://www.packtpub.com/product/machine-learning-with-r/9781782162148

- Any introductory textbooks on time series

Teaching methods

Remote lecture mode and interactive (the students will be required participate actively in the class discussions and group work).

Exam Rules

The exam is the same for attending and non-attending students and evaluates the overall preparation of the student, the ability to integrate the knowledge of the different parts of the program, the consequentiality of the reasoning, the analytical ability and the autonomy of judgment.
Furthermore, language properties and clarity of presentation are evaluated, in compliance with the Dublin descriptors (1. Knowledge and understanding) 2. Ability to apply knowledge and understanding; 3. Making judgments; 4. Learning skills; 5: Communication skills.

The exam includes an oral exam and a written test, submitted during the oral exam;
The overall grade is as follows: oral exam (55% of the total marks), written test (45% of the total marks).
If the written exam is passed with at least 18/30, you can refuse the mark and come back to the next exam date. The mark obtained at the next exam date cancels the previous mark

The exam will be assessed according to the following criteria:
Not suitable: important deficiencies and / or inaccuracies in the knowledge and understanding of the topics; limited capacity for analysis and synthesis, frequent generalizations and limited critical and judgment skills, the arguments are presented in an inconsistent way and with inappropriate language;
18-20: just sufficient knowledge and understanding of the topics with possible generalizations and imperfections; sufficient capacity for analysis, synthesis and autonomy of judgment, the topics are frequently exposed in an inconsistent way and with inappropriate / technical language;
21-23: Routine knowledge and understanding of topics; Ability to correct analysis and synthesis with sufficiently coherent logical argument and appropriate / technical language
24-26: Fair knowledge and understanding of the topics; good analysis and synthesis skills with rigorously expressed arguments but with a language that is not always appropriate / technical.
27-29: Complete knowledge and understanding of the topics; remarkable abilities of analysis and synthesis. Good autonomy of judgment. Topics exposed rigorously and with appropriate / technical language
30-30L: Excellent level of knowledge and in-depth understanding of the topics. Excellent skills of analysis, synthesis and autonomy of judgment. Arguments expressed in an original way and with appropriate technical language.