At the end of the course, students will be able to: use R to import, transform, visualize, and analyze economic data; write readable and reproducible R scripts; apply fundamental statistical tools; perform and interpret simple, multiple, and nonlinear linear regressions; produce reproducible reports and outputs.
Course Prerequisites
Basic knowledge of statistics.
Teaching Methods
Lectures in which course content is presented through the use of slides and the chosen programming software. Lectures are regularly supplemented by in-class exercises, where theoretical concepts are deepened through exercises presented and solved by the instructor. Teaching material used during lectures and exercises is made available through the KIRO platform.
Assessment Methods
The written exam consists of theoretical questions and practical programming exercises based on the topics covered throughout the course. The exercises will be similar in structure and content to those discussed during lectures and made available on the KIRO e-learning platform. The detailed exam structure will be communicated on the course e-learning page along with a mock exam containing the instructions for the test. The grading scale ranges from 0 to 30 cum laude, with a passing grade of 18/30. Exam results are communicated via the ESSE3 portal. Acceptance or rejection of the grade must follow the current University academic regulations.
Texts
External handouts: Introduzione a R, by Claudio Zandonella Callegher and Filippo Gambarota, Psicostat, University of Padua, 202; Rappresentazione Analitica delle Distribuzioni Statistiche con R, by Vito Ricci, version 0.4-21 February 2005. Introduzione all’Econometria 5ed, di J.H. Stock M.W Watson, Pearson, 2020, ISBN: 8891906190. Fourth and third editions are consistent with the goals of this module.
Contents
Introduction to R and RStudio: environment, objects, functions. Data structures: vectors, matrices, factors, data frames. Data import, cleaning, and manipulation. Visualization and representation of statistical distributions in R. Control flow (if, else) and loops (for, while). Functions in R: definition, default arguments, best practices. Descriptive statistics. Application of simple linear regression: part one. Application of simple linear regression: part two. Application of multiple linear regression
Course Language
Italian
More information
For all organizational details and teaching materials, please refer to the course e-learning page.
