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Program

EN IT

Updated A.Y. 2019-2020

Overview

The course 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).

Pre-requisites

Basic knowledge of descriptive statistics, elements of probability, random variables and statistical inference.

Learning objectives

   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:

  • Linear regression model
  • Logit and Probit model
  • Analysis of Variance
  • 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

Analyzing in a critical way concrete situations and case studies, working in team and comply with mandatory deadlines.

Program a.y. 2019-2020

Lecture 1-2

linear regression model

  • Statistical model
  • Linear regression model
  • Estimation and goodness of fit
  • Diagnostics for residuals
  • Applications and Examples with Stata/SAS

Lecture 3-4

Multiple linear regression model

  • matrix approach to linear regression
  • Estimation of regression coefficient
  • Inferences about regression parameters
  • Diagnostics for residuals
  • Multicollinearity
  • Model Selection
  • Applications and Examples with Stata/SAS

Lecture 5

Analysis of Variance

  • Definition
  • Effects Estimation
  • Applications and Examples with Stata/SAS

Lecture 6

  • Introduction to Multivariate Statistics
  • Principal components Analysis (PCA): Introduction and Motivation (Data dimension reduction, linear combination of variables)
  • PCA: Presentation of  Method (eigenvalues and vectors, loadings, scores)

Lecture 7

·         Graphical Methods (Biplots)

·         PCA: Applications and Examples with Stata/SAS

Lecture 8

  • Cluster Analysis: Overview, Partitional clustering: K-means
  • Cluster Analysis: Applications and Examples with Stata/SAS

Lecture 9

  • Agglomerative Hierarchical clustering (single linkage, complete linkage, group average)
  • Cluster evaluation (unsupervised, supervised, relative)
  • Cluster Analysis: Applications and Examples with Stata/SAS

Lecture 10

Logit and probit

  • Statistical model
  • Estimation and goodness of fit
  • Classification matrix
  • ROC curve

Lecture 11

Forecasting

  • Autoregressive model
  • Estimation and inference

Lecture 12

  • Applications with SAS on real data sets

 

Teaching methods

Classroom teaching, exercises, discussion of case studies, 12 hours of practical exercising by using statistical software.

References

Slides and other teaching material will be available on the course website.

- J. Neter, M. Kutner, C. Nachtsheim, W. Wasserman, 1996, Applied Linear Regression Models, Irwi

- J. Lattin, J. Carroll, P. Green, 2003, Analyzing Multivariate Data, Thomson

- M. Mazzocchi, Statistics for Marketing and Consumer Research, 2008, Sage (chapters: 7.1, 7.3, 8, 10.3.1, 10.3.2, 10.4.2, 12.1, 12.2.1, 12.2.2, 12.2.3, 12.2.4, 12.2.5, 12.2.6, 12.3.2)

- Press M. Mann, Introductory Statistics, 2010, Wiley,  (chapters: 12, 13, 14)