AIMS AND LEARNING OUTCOMES

The expected learning outcomes require the student to be able to handle the basic definitions of statistics and probability, to understand the difference between a deterministic and statistical approach, to have acquired the notion of a random variable and to be able to use probability to pass from descriptive statistics to analysis. data through inferential statistics. The student must be able to build simple statistical-probabilistic models (possibly adapting classical schemes) and discuss the results given by the models.

TEACHING METHODS

Lectures and frontal exercises, exercise sheets, guided exercises, in itinere self-assessment tests.

SYLLABUS/CONTENT

Probability Definitions classical, a posteriori, axiomatic; conditional probability, independence; Bayes theorem, factorization theorem, law of total probability. Discrete and continuous random variables, distribution and density functions, function of random variable. Expected values, moments and theoretical variances. Joint distributions and conditional laws, covariance and correlation.

Descriptive Statistics Qualitative variables: categorical, ordinal; univariate descriptive: percentages and tables, bar and pie charts, Pareto chart, fashion; bivariate descriptive: row and column profiles. Quantitative variables: position indices (mode, median, mean, percentiles and quartiles), cumulative empirical distributions, boxplot; dispersion indices: range, IQR, variances and standard deviation, coefficient of variation; relationship between two quantitative variables: covariance and correlation, Simpson's paradox; linear regression, regression line, R2 coefficient and residual analysis.

Estimates and estimators. Principles of randomness, distortion, mean square error, efficiency.

Confidence intervals By mean (known / unknown variance, small and large sample sizes), by variance, by the difference of means (independent samples and paired samples). Funnel plot (weather permitting).

Statistical hypothesis tests Introduction, type I and II errors, p-value, level of significance, power. Test for the difference of means: independent samples and paired samples. Test for variance. Chi-squared test for categorical variables (comparison between a known distribution and an observed univariate, comparison between two observed univariate).

RECOMMENDED READING/BIBLIOGRAPHY

Sheldon M. Ross, Introduzione alla statistica, Apogeo Formulario e Soluzioni ad esercizi dispari in http://www.apogeoeducation.com/9788891602671-introduzione-alla-statisti…

Jay L. Devore, Probability and Statistics for Engineering and the sciences,

Duxbury P. Newbold, W.L. Carlson, B. Thorne, Statistica, Pearson