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Statistica A e B

STATISTICA

 

Prof. Aldo Goia (corso A)

Prof. Enea Giuseppe Bongiorno (corso B)

 

Codice Insegnamento: EA007

SSD Insegnamento: SECS-S/01

8 CFU – 64 ore

Sede: Novara

  • Lingua insegnamento

Italian

 

  • Contenuti

Presentation of statistical methodology for the analysis of one-dimensional and two-dimensional data. Introduction to probability theory and to sample statistics with special attention to inference for large samples.

  • Testi di riferimento

Lecture notes and exercises are available in the web page of the course at the URL: https://eco.dir.unipmn.it/

  • Obiettivi formativi

The goal of the course is to introduce some basic techniques of univariate and bivariate data analysis and some elements of inferential statistics.

  • Prerequisiti

Basic elements of mathematics and calculus.

  • Metodi didattici

Lectures and exercises.

  • Altre informazioni

Some on-line supports are available on the web page of the course.

  • Modalità di verifica dell’apprendimento

A compulsory written proof and an optional oral test.

  • Programma esteso

Part I – Univariate descriptive statistics

The population and the variables.Frequency distributions and graphics. Analysis of quantitative variables: the cumulative distribution functions and the quantiles. Measures of the central tendency and variability. Measures of shape. Analysis of qualitative variables: the heterogeneity.

Part II – Bivariate descriptive statistics

The joint distributions and the conditional distributions. The study of the dependence. The Chi square index. The correlation and the decomposition of variance. The linear correlation and the covariance. The linear regression. The OLS method. Goodness of fit.

 

Part III – Elements of Probability and Inference

The random experiments and the probability space. Probability measure on finite sample spaces. Conditional probability and independence. The random variables. Moments. Some useful probability distributions. Linear combination of random variables. The populations and the samples. Asymptotic statistics: consistency, the Central Limit Theorem and the plug-in principle. The point estimation and the asymptotic confidence intervals for mean, proportion, variance and regression coefficient. Introducing hypothesis testing.