STATISTICS LABORATORY FOR ECONOMIC APPLICATIONS

The course is an activity designed to enhance the skills required for statistical data management in an Economics degree program, utilizing specialized software for reporting. The objective of the course is to impart knowledge in data analysis and management, aiming to improve the ability to analyze data and present information using statistical software (R).

1. KNOWLEDGE AND UNDERSTANDING
1.1 Familiarize yourself with the R software.
1.2 Grasp the fundamental basic statistical techniques in R.
1.3 Acquire the knowledge of how to prepare a report.
2. APPLYING KNOWLEDGE AND UNDERSTANDING
2.1 Import data effectively.
2.2 Analyze data both graphically/descriptively and inferentially, particularly using models.
2.3 Develop R language code to execute the necessary statistical analyses.
3. MAKING JUDGMENTS
3.1 Recognize the most suitable statistical techniques for the given dataset and implement them in R.
3.2 Interpret the statistical results with proficiency.
3.3 Summarize the results obtained using R in a comprehensive report.

To have attained the learning outcomes of Statistics, the student should particularly excel in understanding the concepts and methods related to basic inference and descriptive statistics. These include p-value, confidence intervals, frequency distributions, main indices of a distribution, and graphical representation.

1) Introduction to statistical software (R)
Students will learn how to use the software R, with a specific focus on loading data and handling variables.
2) Descriptive statistics
Beginning with real data, students will learn how to construct graphical and tabular representations to obtain a meaningful synthesis of the examined phenomenon.
3) Statistical inference
Using real data, the course will demonstrate the implementation of fundamental inferential tools, including point estimation, confidence intervals, and tests.
4) Linear regression (simple and multiple)
5) More advanced regression analysis (e.g., logistic regression)

Material provided by the professor on the Moodle platform with links from the course's website
Data Analysis with RStudio (2021) by Kronthaler F, Zöllner S. Springer

The assessment of learning occurs through a written test. Students will be required to analyze a set of data to assess their mastery of a minimal set of basic knowledge and the skills acquired in processing, interpreting, analyzing, and communicating the available information. Regarding the grading, the exam will be marked on a scale ranging from 0 to 30. The minimum passing grade is 18. Honors ("lode") will be granted only for exceptional capacity of judgment and excellent knowledge of the topics under evaluation.

The course comprises face-to-face lessons, utilizing statistical software for data analysis.

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