This course presents the student with some of the basic concepts of inferential statistic. It discusses the main sampling techniques, the probability theory, the estimation theory and the significativity tests. Among the statistical models, the regression linear model and the analysis of varianza are considered.
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
- Explanatory 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. (2009). Statistical Methods for the Social Science, 4th edition, Prentice Hall, New York.Type of delivery of the course
tradizionalAttendance
optionalType of evaluation
Multiple choice test.