Advanced data analysis
A.A. 2020/2021
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
In case the Covid emergency prevents lectures from being given in class, these will be delivered live via the Microsoft Teams according to the regular schedule. Each live lecture will be video recorded and immediately made available to all students via the platform. The teaching modality (in class vs. online) and the instructions for attending classes will be advertised on the course web page.
The syllabus and the reference material will not change.
The methods of assessment and the evaluation criteria will not change. However, exams may take place via Zoom or Teams depending on the rules being enforced at the time of the exam session.
The syllabus and the reference material will not change.
The methods of assessment and the evaluation criteria will not change. However, exams may take place via Zoom or Teams depending on the rules being enforced at the time of the exam session.
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.
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, example) 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, example) 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 pdf) and via Compilatio.net to review for plagiarism (only pdf). 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 pdf) and via Compilatio.net to review for plagiarism (only pdf). During the oral discussion students must present the project and discuss the results.
Docente/i
Ricevimento:
Il ricevimento studenti è il martedì dalle 10.00 alle 13.00 o in presenza o via Teams (meglio fissare un appuntamento) - Il ricevimento di martedì prossimo, per altri impegni accademici, non sarà svolto. Contattare il docente per un altro appuntamento.
DEMM, stanza 30, 3° p oppure su Teams