Advanced data analysis
A.A. 2024/2025
Obiettivi formativi
The purpose of this course is to introduce students to basic programming in Stata and to provide guidance on data management strategies for socio-economics data. The course will focus on command-based programming for modifying and managing data and performing statistical analysis in Stata.
Risultati apprendimento attesi
By the end of the course students will be able to comfortably navigate the Stata environment, create simple datasets, access existing datasets, create variables, use graphing functions, run commands to calculate summary statistics as well as inferential statistics, including simple and multiple regression.
Periodo: Secondo trimestre
Modalità di valutazione: Giudizio di approvazione
Giudizio di valutazione: superato/non superato
Corso singolo
Questo insegnamento non può essere seguito come corso singolo. Puoi trovare gli insegnamenti disponibili consultando il catalogo corsi singoli.
Programma e organizzazione didattica
Edizione unica
Responsabile
Periodo
Secondo trimestre
Programma
Introduction to Stata
· Data management
· Working with Data
· Bivariate Analysis and Hypothesis testing
· Graphics
· Simple Reression
· Multiple Regression
· Regression Diagnostics
· Non linear Regression
· Robust Regression
· Data management
· Working with Data
· Bivariate Analysis and Hypothesis testing
· Graphics
· Simple Reression
· Multiple Regression
· Regression Diagnostics
· Non linear Regression
· Robust Regression
Prerequisiti
Mathematics and Statistics
Metodi didattici
The students will use a computer during the lectures. Every session will intermix the presentation of syllabus topics followed by examples and in class exercises. Optional group work will be offered to get familiar with the software and increase practical skills.
Materiale di riferimento
- Hamilton, L. C., Statistics with STATA: Version 12, 8th Edition, Cengage, 2012 (Chapter 1,2,3,5,6,7,8)
- Stock J., Watson M. (2010) Introduction to Econometrics, 3rd Edition, Addison-Wesley, Pearson (Chapters 6,7,8,9)
- Additional materials (slides, exercises, datasets, scripts, examples) in the ARIEL website
- Stock J., Watson M. (2010) Introduction to Econometrics, 3rd Edition, Addison-Wesley, Pearson (Chapters 6,7,8,9)
- Additional materials (slides, exercises, datasets, scripts, examples) in the ARIEL website
Modalità di verifica dell’apprendimento e criteri di valutazione
The exam consists in a project assignment and brief oral discussion.
The project will involve identifying a dataset, developing research questions, and using the skills learned in the class to answer the research questions. It will include a brief introduction, a methods section, a section on results, graphic representations of the sample and/or results, and a brief discussion. All assignments must be submitted via email (dataset, script, and project in pdf) 5 days before the exam, they will be checked for plagiarism via Compilatio.net. During the oral discussion students must present the project and discuss the results.
The project will involve identifying a dataset, developing research questions, and using the skills learned in the class to answer the research questions. It will include a brief introduction, a methods section, a section on results, graphic representations of the sample and/or results, and a brief discussion. All assignments must be submitted via email (dataset, script, and project in pdf) 5 days before the exam, they will be checked for plagiarism via Compilatio.net. During the oral discussion students must present the project and discuss the results.
INF/01 - INFORMATICA - CFU: 3
Informatica di base: 20 ore
Docente:
Salini Silvia
Turni:
Turno
Docente:
Salini SilviaDocente/i
Ricevimento:
Il ricevimento studenti è in presenza, per appuntamento, il martedì dalle 9.30 alle 11.00 e via Teams, per appuntamento, il lunedì dalle 15.00 alle 16.30.
DEMM, stanza 30, 3° p