Learning objectives
Aim of the course is to supply a robust basis of Data Analysis and Probability through which to tackle the main problems of firm in a statistical framework.
Prerequisites
- - -
Course unit content
English.
Univariate and bivariate descriptive Statistics.
Casual variables, sample distributions, estimation.
Bivariate and multivariate regression.
Design of experiments and analisys of variance.
Distances and similarity.
Principal Components.
Full programme
First Part
Statistical distributions and their graphical representation.
Means. Conditions of equivalence. Power means.Means of position. Mean of a continuous distribution.Non existence of the mean.
Indexes of absolute and relative Variability. Their general properties.Variance Covariance matrix. Correlation matrix.
Concentration. Absolute mean difference. Lorenz Curve and Gini.
Number Indexes.
properties of elementary indexes. Synthesis of elementay indexes.
Method of Least Squares and its properties.
Second Part: Probability and Inference.
Most used casual Variables.
Bernoulli. Binomiale. Poisson. Discrete Rectangular. Normal. Exponential. Continous Rectangular. t Student. Chi square. F. Erlang. Cauchi. Pareto.
Theorem of Central Limit.
Space of events, theorem of total probability.
Sample distributions.
Interval estimation.
Third Part: Rregression with Linear Model.
The hyphothes of the model. Estimation of the parameters of the models and distributions of the estimates of the same parameters.
The forecast. The linear multivariate model.
Fouth Part: Double distributions, Sum of Variables and Mixture of Variables.
Fith Part: Analysis of Variance and the design of experiments.
One factor model.Its hyphotheses. The parameters and their estimators.
The RBC. Latin Squares. The two and three factors model with interaction.
Surfaces Responses.
First and Second Order Surfaces Responses.
Sixt Part: Distances.
Seventh Part: Principal Components.
Bibliography
Cicchitelli G., D'Urso P., Minozzo M.
Statistica: Principi e metodi. Pearson 2017. (Chapters indicated during the course).
Montgomery D. C. Progettazione e analisi degli esperimenti 2006 McGraw-Hill).(Chapters indicated during the course)
Zani S. Cerioli S. Analisi dei dati e mining per le decisoni aziendali.2007 Giuffrè. (Chapters indicated during the course).
Teaching methods
Lectures.
Use of R.
Assessment methods and criteria
Written and oral exam.
In the written exam the student has to solve some applied exercises concerning the different parts of the course.
In ther oral exam the student has to show knowledge of the the models developed
during the course and indicated in the programme.
Other information
In Elly it is possible to downlòoad a book of exercises.
