77953 - Data Analysis

Learning outcomes

The course provides an overview of the foundations of data analysis for social sciences and economics, using statistical techniques. These include descriptive and data visualization methods and inferential statistics (estimation and test and hypothesis). The course is applied in nature and students learn how to use the R programming language.

Course contents

PLEASE NOTE: The updated syllabus is available here:

https://sites.google.com/view/quantitativemethods/syllabus-lab

 

The purpose of this short crash-course is to learn some of the foundations of statistics and, while doing that, to start familiarizing oneself with the R programming language.

Course contents

• Descriptive statistics;
• Probability theory
• Random variables and probability distributions
• Sample analysis,
• Estimation
• Test of hypotheses [SW] chapter 2, 3.

[PP]: Chapter 1: All ; Chapter 2: All, with the exlusion of 2.10; Chapter 3: 3.1, 3.2, 3.4; Chapter 4: All, with the exclusion of 4.9 (Bayes' Theorem); Chapter 5. All, except 5.6 (Binomial distribution), 5.7 (Ipergeometric distribution) ; Chapter 6. All, except 6.6 (Sample distribution of a relative frequency); Chapter 7. All, except 7.6 (Interval estimation of a relative frequency); Chapter 8. All, except 8.4 (Test of hypothesis for a relative frequency) and 8.6 (Other cases)

R labs

• R Lab 1: Introduction to R.

Variety of programs available.

How to do a project

The "file system"

Basic commands, data management, use of ".R files".

• R Lab 2: Introduction to R.

Inputing data, data transformation, first elements of data analysis.

file: /R/lectures/r_files/REcon_Ch2_[date].r

(From: Sunstrom & Kevane, "Guide to R - Data analysis for Economics" )

• R Lab 3: Exploratory data analysis.

Descriptive statistics. Data visualization.

file: /R/IMF-WEO/r_files/imf_weo_[...].r

file: /R/graphs/r_files/world_map_[...].r

file: /R/graphs/r_files/chord_diagram[...].R

• R Lab 4: Exploratory data analysis.

Descriptive statistics. Data visualization.

file: /R/lectures/r_files/lectures/exploratory_analysis[..].R

• R Lab 5: Exploratory data analysis.

Descriptive statistics. Data visualization.

file: /R/lectures/r_files/CLT_LP_[date].r

file: /R/lectures/r_files/REcon_Ch3-4_[date].r

Readings/Bibliography

• Handouts
• Students who can read Italian may want to consider: Pacini, B. and Picci, L. 2001. Introduzione alla Statistica, Clueb (indicated in what follows as [PP]). A limited number of copies of this textbook may be borrowed from the library.
• Hanck, Christoph; Arnold, Martin; Gerber Alexander, and Martin Schmelzer Introduction to Econometrics with R. Online:
• Sundstrom, Williams. Guide to R for SCU Economics Students (contains replications of some of Stock-Watson problems)

Teaching methods

Lectures, partly online.

Assessment methods

To pass the exam (with a "pass/fail" grade) students will need to complete a project using R. Detailed instructions will be provided in due time.

 

Teaching tools

R, R Studio.

Links to further information

https://sites.google.com/view/quantitativemethods/syllabus-lab

Office hours

See the website of Lucio Picci