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Docente
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FARCOMENI ALESSIO BARONE ROSARIO
(programma)
• Introduction to the R software • Categorical data: multi-way tables, distributions, the generalized linear model • Principles of causal analysis: selection, bias, confounding. Confounders, colliders, mediators. Simpson’s Paradox. • Logistic regression, Poisson regression • Analysis of clustered and panel data: mixed models • Principles of supervised learning. Classification trees and random forests
 • Cameron, A. C. and Trivedi, P. K. (2005) Microeconometrics: Methods and Applications. Cambridge University Press. (More technical) • Alan Agresti (2018) An Introduction to Categorical Data Analysis, 3rd Edition. Wiley (More introductive) • Everitt, B. S. and Hothorn, T. (2006) A Handbook of Statistical Analyses Using R. CRC Press. Available for free at: http://www.ecostat. unical.it/tarsitano/Didattica/LabStat2/Everitt.pdf (For practicing and learning a bit more about R)
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