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Single discipline educational activity
Course Sheet Academic Year of enrolment:
Professor and Collaborators:
Hours of classroom activity:
Prerequisites:
The knowledge of Basic Statistics is requested.
Objectives Contents Content: 1. Basic concepts of probability and statistical inference. Discrete and continuous random variables. Expected values, variability. Point estimation. Confidence intervals. Hypothesis test. 2. R statistical software. 3. Basic concepts of linear algebra: vectors and matrices; Determinant and inverse of a matrix. 4. The linear regression model Simple linear regression. Multiple linear regression. Inference. Eteroschedasticity and Autocorrelation. Dummy variables. Instrumental variables. 5. The linear regression model and R. Study cases. 6. The logistic regression model. Definitions. Parameter Estimation and Interpretation. 7. The logistic regression model and R.
Recommended Bibliography - Course slides. - PICCOLO D. (2010). Statistica. Edizioni Il Mulino. (Cap. 14 pagg.491-501; 520-526; Cap. 15 pagg. 548-557, 560-561, 567-577, Cap. 16 pagg. 585-591; Cap. 17 pagg. 607-620; Cap. 18 pagg. 669-673, 677-678; Cap. 19 pagg. 731-737, Cap. 22; Cap. 23.).
Methods of Provision Evaluation methods Verification of learning:
The exam is written and oral. The written exam will consist of theoretical questions and empirical exercises. The exam will cover the entire program, with particular attention to the use of the R software. Students will also have to prepare and discuss a regression (linear or logistic) analysis, carried out with R, concerning a real case study (data sets can be found on internet). This report must be sent to the Professor at least one week before the examination date. Further information can be found on the Professor's website: http://www.unich.it/~postigli/Home.html
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