10596373 - Statistics for health economics 2020/2021

Google Classroom

Students should join the course using Google Classroom (code: i7ao6lr). Materials and announcements about the course will be posted exclusively on Google Classroom.

Schedule (Orario lezioni)

  • Monday 14-16 (Laboratorio Informatico Ecodir)
  • Tuesday 9-11 (Laboratorio Informatico Ecodir)
  • Thursday 16-18 (Aula 1D)

Language (Lingua)


Final grade (Modalità di valutazione)

The final grade will be based on a written exam (which will weigh 50% of the final grade) and a practical application (which will weigh 50% of the final grade). The former will require solving theoretical problems. The latter will include a data analysis exercise using R.

The calendar of the exam sessions is available on the course webpage from the courses catalogue.

The exam will take place online via Exam.net. For the time being, this modality applies to exams held in June and July only. Students are encouraged to check the instructions available at this link. The Exam.net code necessary to access the exam will be provided only to students who are registered for the specific exam session.

Teaching delivery modality (Modalità di svolgimento)

For this course, class sessions will be streamed live on Zoom. Classes will be online only and will take place as scheduled. The link for attending online classes is available on the School's webpage. For students who wish to attend lectures from within the designated classrooms, lectures will be streamed live on the classroom's screen.  

Prerequisites (Prerequisiti)

Fundamental prerequites: calculus (limits, integrals, derivatives, matrices/vectors) and introductory statistics (mean, variance, chi-squared test, linear correlation, linear regression). Knowledge of probability theory is not required but recommended.

Topics (Programma)

1.    Probability
2.    Random variables and distributions
3.    Moments of random variables and their properties
4.    Joint and conditional distributions
5.    Principles of statistical inference and important results
6.    Maximum likelihood estimation
7.    Delta method
8.    Odds, odds ratios and relative risk
9.    Contingency tables
10.    Linear regression
11.    Logistic and Poisson regression
12.    Zero excess, over- and under-dispersion
13.    Multinomial regression
14.    Model selection
15.    Predictive modelling

Bibliography (Bibliografia di riferimento)

  • Course slides
  • Mood, Graybill, Boes. Introduction to the theory of statistics. McGraw-Hill
  • Agresti A. Categorical Data Analysis. Wiley
  • Fleiss JL, Levin B, Paik MC. Statistical methods for rates and proportions. Wiley
  • Crawley MJ. The R book. Wiley
  • Venables WN, Smith DM, and the R Core Team. An introduction to R. Available at: https://cran.r-project.org/manuals.html

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