Metodi quantitativi - II modulo - eng

QUANTITATIVE METHODS II
Prof. Caterina May

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.

2.    Sampling and sampling distributions.
Central limit theorem.

3.    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.

4.    Applications to statistical process control for quality improvement.
Control charts for variables and for attributes.

5.    Linear regression model:
inference and applications.
Dummy variables.