ITA | ENG
B030725 - Stata Lab II: models and applications
Main information
Teaching Language
Course Content
Suggested readings
Learning Objectives
Prerequisites
Teaching Methods
Type of Assessment
Course program
Academic Year 2021-22
Coorte 2021 - Second Cycle Degree in ECONOMICS AND DEVELOPMENT
Course year
First year - Second Semester
Belonging Department
Economics and Management (DISEI)
Course Type
Single education field course
Scientific Area
-
Credits
3
Teaching Hours
24
Teaching Term
21/02/2022 ⇒ 31/05/2022
Attendance required
No
Type of Evaluation
Giudizio Finale
Course Content
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Course program
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Lectureship
Teaching Language
English
Course Content
The course covers the basics of statistical modelling with the software Stata. Much emphasis is placed on the linear regression model, tackling issues in specification (non-linear functions, interactions, selection of covariates) and model checking (collinearity, heteroscedasticity, outliers). The course also covers logit/probit models for binary and ordinal responses and multilevel linear models. The ideas are illustrated through the analysis of real data using Stata.
Suggested readings (Search our library's catalogue)
Slides of the teacher and online resources on
https://www.stata.com/
https://stats.idre.ucla.edu/stata/
https://www.stata.com/
https://stats.idre.ucla.edu/stata/
Learning Objectives
The student will be able to carry out a statistical analysis based on simple regression models using Stata, understanding and checking the assumptions and interpreting the results in an effective way.
Prerequisites
Statistical inference.
Teaching Methods
Lessons, possibly in lab.
Type of Assessment
Homeworks assigned during the course.
Course program
The course covers the basics of statistical modelling with the software Stata. Much emphasis is placed on the linear regression model, tackling issues in specification (non-linear functions, interactions, selection of covariates) and model checking (collinearity, heteroscedasticity, outliers). The course also covers logit/probit models for binary and ordinal responses and multilevel linear models. The ideas are illustrated through the analysis of real data using Stata.