Advanced Data Analysis
A.Y. 2024/2025
Learning objectives
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.
Expected learning outcomes
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.
Lesson period: Second trimester
Assessment methods: Giudizio di approvazione
Assessment result: superato/non superato
Single course
This course can be attended as a single course.
Course syllabus and organization
Single session
Responsible
Lesson period
Second trimester
Course syllabus
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
Prerequisites for admission
Mathematics and Statistics
Teaching methods
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.
Teaching Resources
- 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
Assessment methods and Criteria
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 - INFORMATICS - University credits: 3
Basic computer skills: 20 hours
Professor:
Salini Silvia
Shifts:
Turno
Professor:
Salini SilviaProfessor(s)
Reception:
The student reception is in attendance, by appointment, on Tuesday from 09.30 to 11.00 and via Teams, by appointment, on Monday from 15.00 to 16.30.
DEMM, room 30, 3° floor