First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Science
STATISTICS ECONOMICS AND FINANCE
Course unit
STATISTICAL MODELS 1
SSL1001481, A.A. 2014/15

Information concerning the students who enrolled in A.Y. 2013/14

Information on the course unit
Degree course First cycle degree in
STATISTICS ECONOMICS AND FINANCE
SS1449, Degree course structure A.Y. 2009/10, A.Y. 2014/15
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Degree course track Common track
Number of ECTS credits allocated 8.0
Type of assessment Mark
Course unit English denomination STATISTICAL MODELS 1
Website of the academic structure http://scienzestatistiche.scienze.unipd.it/2014/laurea_statisticaeconomiafinanza_2009
Department of reference Department of Statistical Sciences
Mandatory attendance No
Language of instruction Italian
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge NICOLA SARTORI SECS-S/01

Mutuating
Course unit code Course unit name Teacher in charge Degree course code
SSL1001481 STATISTICAL MODELS 1 NICOLA SARTORI SS1451

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses SECS-S/01 Statistics 8.0

Course unit organization
Period Second semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Laboratory 3.0 20 55.0 2
Lecture 5.0 36 89.0 No turn

Calendar
Start of activities 02/03/2015
End of activities 12/06/2015
Show course schedule 2015/16 Reg.2009 weekly timetable
2015/16 Reg.2009 single teaching timetable

Examination board
Board From To Members of the board
6 Commissione a.a. 2014/2015 01/10/2016 30/09/2020 ROVERATO ALBERTO (Presidente)
CATTELAN MANUELA (Membro Effettivo)
GRIGOLETTO MATTEO (Membro Effettivo)
SARTORI NICOLA (Membro Effettivo)
5 Commissione a.a. 2014/2015 01/10/2014 30/03/2016 SARTORI NICOLA (Presidente)
CELANT GIORGIO (Membro Effettivo)
CHIOGNA MONICA (Membro Effettivo)
KENNE PAGUI EULOGE CLOVIS (Membro Effettivo)
SALVAN ALESSANDRA (Membro Effettivo)
VENTURA LAURA (Membro Effettivo)

Syllabus
Prerequisites: Calculus
Linear algebra
Probability
Statistical inference
Target skills and knowledge: - Learning statistical analysis using linear regression models and their generalisation
- Learning how to use regression models in R
Examination methods: Written exam
Assessment criteria: Evaluation is based on the written exam
Course unit contents: - The linear regression model. Second order assumptions and assumption of
normality.
- Parameter estimation: least squares estimator, Gauss-Markov theorem.
- Likelihood based inference: point estimation, confidence intervals,
testing linear hypotheses on regression coefficients.
- Use of dummy variables. Analysis of variance and analysis of covariance.
- Model building and model criticism: diagnostic methods (analysis of
residuals, outliers and leverage points), variable selection methods.
- Limits of linear model and reasons for its generalization.
- Logistic and Poisson regression.
Planned learning activities and teaching methods: Front lectures (4 hours per week) and Computer Lab classes (4 hours per week, for the last 5 weeks of the course)
Additional notes about suggested reading: Teaching material available in the course website
Textbooks (and optional supplementary readings)
  • Pace, L., Salvan, A., Introduzione alla Statistica - II. Inferenza, Verosimiglianza.. Padova: Cedam, 2001. Cerca nel catalogo
  • Azzalini, A., Inferenza Statistica: una Presentazione Basata sul Concetto di Verosimiglianza.. Milano: Springer-Italia, 2004. Cerca nel catalogo
  • Bortot, P., Ventura, L. Salvan, A., Inferenza Statistica: Applicazioni con S-Plus e R. Padova: Cedam, 2000. Cerca nel catalogo