The aim of the course is to introduce the student to the techniques of statistical inference mainly used in the social sciences, giving greater emphasis to the understanding of concepts rather than mathematical formalization.
At the end of the course the student:
- has become familiar with the basic concepts of Statistical Inference
- is able to perform analysis of inferential data and use the main statistical mode
At the end of the course the student:
- has become familiar with the basic concepts of Statistical Inference
- is able to perform analysis of inferential data and use the main statistical mode
teacher profile teaching materials
- 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.
Programme
- 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)Reference Bibliography
.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.Attendance
.Type of evaluation
written test