This course deals with statistical models for the analysis of quantitative and qualitative data. The statistical methods studied are the general linear model for quantitative responses (including multiple regression, analysis of variance and analysis of covariance), binomial regression models for binary data (logistic regression ).
Marchetti, G. (2013). Introduzione ai modelli statistici, Dipartimento di Statistica, Firenze. Dispense a cura di G. Marchetti, scaricabili in formato PDF dalla piattaforma Moodle (e-learning)
Learning Objectives
Estimation and hypothesis testing. Linear regression models. Logit models for binary response. Dummy variables and Interactions. Goodness of fit. Specification issues.
At the end of the course, the student should be able to model a real phenomenon and to fit the model on sample data in order to analyse and interpret variables relationships. The students should be able to give a correct interpretation of model parameters, to predict new values for the response variable giving the appropriate uncertainty measures.
Prerequisites
Statistics, calculus
Teaching Methods
Lectures and lab sessions
Further information
During Lab session we will use STATA.
Type of Assessment
Written and oral examination.
Course program
Probability and random variables, estimation, test of hypothesis and sample distributions, models for the comparison of two groups, simple linear regression, likelihood based inference, the logistic linear model, the general linear regression model, model specification and goodness of fit, general linear logistic model.