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.
Distances and similarity.
Design of experiments and Anaysis of variance: Completely Random Design and One Factor Model, Randomized Block Designs and two Factors Model, Latin Squares Design and its model, the Factorial Factors Design and Two Factors with and without interaction. Contrasts. Response Surfaces Methods.
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. Cauchi. Pareto.
Theorem of Central Limit.
Space of events, theorem of total probability.
Sample distributions.
Interval estimation.
Third Part: Regression 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.
THe Completely Randomized Design and One Factor Model. (Hyphotheses, Parameters and their Estimators).
Contrasts.
The RBCD and the two factors model without interaction. Latin Squares and its model. Factorial design and the two factors without and with interaction model.
First and Second Order Surfaces Responses.
Sixt Part: Distances.
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
Digital lessons. Lessons are also recorded.
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 download a book of exercises.
