Study-unit STATISTICAL METHODS FOR FINANCE
| Course name | Finance and quantitative methods for economics |
|---|---|
| Study-unit Code | A000204 |
| Curriculum | Finanza ed assicurazione |
| Lecturer | Silvia Bacci |
| Lecturers |
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| Hours |
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| CFU | 6 |
| Course Regulation | Coorte 2018 |
| Supplied | 2018/19 |
| Learning activities | Caratterizzante |
| Area | Matematico, statistico, informatico |
| Sector | SECS-S/01 |
| Type of study-unit | Obbligatorio (Required) |
| Type of learning activities | Attività formativa monodisciplinare |
| Language of instruction | English |
| Contents | STATISTICAL INFERENCE: multivariate random variables and hypothesis testing LINEAR REGRESSION: model assumptions, parameter estimation, parameter interpretation, goodness of fit, prediction, residual analysis, violations of basic assumptions |
| Reference texts | R. A. Johnson e D. W. Wichern, Applied Multivariate Statistical Analysis, 6th edition, Prentice Hall, New Jersey, 2007. S. J. Sheather, A Modern Approach to Regression with R, Springer Texts in Statistics, 2009. |
| Educational objectives | Knowledge and ability to apply methods of statistical inference and multiple linear regression to real data through the use of software R |
| Prerequisites | Fundamentals of statistics (descriptive statistics and statistical inference) |
| Teaching methods | Lectures and laboratory sections with R software |
| Learning verification modality | The exam consists in a practical test with software R and in a written test with open questions about theoretical topics. |
| Extended program | Probability distributions: multivariate random variables, multivariate normal distribution, Chi-squared, Student’s t, Fisher’s F. Statistical inference: maximum likelihood approach, hypothesis testing on one or more parameters. Simple linear regression: model definition, parameter estimation: least squares method and maximum likelihood method, hypothesis testing, residual analysis. Multiple linear regression: model assumptions, parameter interpretation, parameter estimation: least squares method and maximum likelihood method, interval estimation, hypothesis testing, categorical explicative variables, analysis of variance, goodness of fit, model selection methods, residual analysis, multicollinearity. Logit regression: model assumptions, parameter interpretation, parameter estimation. |