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Metodi statistici per l'impresa - eng

STATISTICAL METHODS FOR THE ENTERPRISE

Prof. Caterina May

Course Code: EC0024

Subject code: SECS-S/01

6 ECTS – 48 hours

Location: Novara

 

Language

Italian.

Contents

Probability theory and its applications for business and finance. Theory of sampling. Statistical inference methods: point estimation, confidence intervals, tests of hypothesis. Applications. Linear regression: significance and applications. Statistical quality control.

References

Giuseppe Cicchitelli. Statistica Principi e Metodi

Pearson 2/ed (2012)

 

Newbold, Carlson, Thorne. Statistica 2/ed.

Pearson (2010)

 

Douglas C. Montgomery. Controllo statistico della qualità 2/ed
McGraw-Hill (2006)

Giuseppe Cicchitelli. Probabilità e statistica

Maggioli Editore (2001)

 

Further teaching material prepared by the professor will be published on D.I.R. (https://eco.dir.unipmn.it/)

Educational aims

The goal of the course is the study of statistical inference and its applications to enterprise, business and finance.

Prerequisites

Contents of the following courses: Mathematical Methods I and II and Statistics.

Teaching methods

Lectures including both theory and exercises.

Further informations

Will be published during the course on D.I.R. (https://eco.dir.unipmn.it/)

Examination

Compulsory written examination plus optional oral examination.

Extended program

  1. Elements of probability:

The random experiments and the probability space. Probability. Conditional probability and independence.

Discrete and continuous random variables. Cumulative distribution function, mean, variance and moments.

Models for distributions of discrete and continuous random variables.

Random vectors: joint distribution, conditional distributions. Conditional mean and variance.

Gaussian vectors.

 

  1. Sampling and sampling distributions.

Central limit theorem.

 

  1. Statistical inference:

Estimators and properties.

Point estimation and confidence intervals.

Parametric tests of hypothesis.

Empirical distribution function.

QQ-plot.

Testing the normality of a distribution.

 

  1. Applications to statistical process control for quality improvement.

Control charts for variables and for attributes.

 

  1. Linear regression model:

inference and applications.

Dummy variables.