The course provides basic concepts of descriptive and inferential statistics. Particular attention is devoted to the concepts of hypothesis tests for one and two samples and one way analysis of variance.
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- Exploratory statistic.
- Sampling theory: population and sample definition. Probabilistic samplings: random sampling with and without reimission. Stratified sampling. Cluster sampling. Non probabilistic sampling.
- Probability theory. Discrete and continuous random variables. Bernoulli and normal distributions.
- Statistical inference: the estimation. Point and interval estimation. Confidence interval for the proportion. Confidence interval for the mean. Sampling size.
- Statistical inferenca: the significativity test. The system of hypotesis and the test. The first and second type errors. The significativity test for the mean. The significativity test for the proportion. P-value.
- Comparison between two groups. Comparison between two proportions. Comparison between two means. Comparison for dependent data.
- Contingency tables and the chi-square test of indipendence.
- Linear regression and correlation. The simple linear regression model. Parameter estimation of the regression model. Correlation coefficient estimation. Test for the regression coefficient and for the correlation coefficient. R-square index.
- Analysis of variance.

Core Documentation

Agresti, A., Finlay B. (2012). Metodi Statistici di Base e Avanzati per le Scienze Sociali, Pearson, Milano. (Capp. 1-9, 12)

Type of delivery of the course

Frontal lessons. Should the Covid19 emergency persist, the lessons will be conducted online on the formonline and Microsoft Teams platforms.

Type of evaluation

Written exam