Computational Statistics Course Materials

Welcome to the repository of the Computational Statistics course, a part of the Applied Computer Science and Data Analysis program. This course is designed to equip students with advanced statistical analysis skills, utilizing the power of the R programming language.

Course Objectives

• Advanced Learning: Master advanced statistical concepts and data analysis techniques.
• Economic and Social Applications: Apply statistical methods to real-world scenarios in economic and social domains.
• Progressive Difficulty: Transition from basic to advanced statistical inference.
• Hands-On Experience: Engage with practical exercises using R to solidify understanding.

Prerequisites

• A foundational knowledge of descriptive and inferential statistics.

Course Content

The course is divided into a series of Jupyter notebooks, each focusing on a specific area of computational statistics. Jupyter notebooks have been created using R scripts provided in the dedicated folder. Below is the complete list of notebooks included in this repository:

1. 01 Introduzione a R.ipynb - An introductory guide to the basics of R programming.
2. 02 Univariate Analysis.ipynb - Techniques and methods for analyzing single-variable datasets.
3. 03 Bivariate Analysis.ipynb - Exploratory data analysis techniques for understanding two-variable relationships.
4. 03e Bivariate Analysis (Esercizio).ipynb - Exercises related to bivariate analysis.
5. 04 Multivariate Analysis.ipynb - Methods for analyzing datasets with more than two variables.
6. 05 Random Variable Creation.ipynb - How to create and work with random variables in R.
7. 08 Discrete and Continuous Random Variables.ipynb - Detailed exploration of discrete and continuous random variables.
8. 09 Theorem of the Central Limit.ipynb - Understanding and applying the Central Limit Theorem.
9. 10 Intervalli di Confidenza.ipynb - Confidence interval construction and interpretation.
10. 10 Sample Distributions.ipynb - Investigating the distribution of sample data.
11. 11 Properties of estimators.ipynb - Examining the key properties of statistical estimators.
12. 12 Hypothesis Tests (Statistical Tests).ipynb - Frameworks and methods for conducting hypothesis testing.
13. 13 Intervals of Acceptance.ipynb - Defining and calculating acceptance intervals in hypothesis testing.
14. 13b Breadth of the sample.ipynb - Analyzing the sample size and its effects on statistical results.
15. 14 Power of the Test.ipynb - Studying the power of statistical tests and their implications.
16. 15 Tests Not Parametric.ipynb - Non-parametric testing methods for various data types.
17. 16 Linear Regression.ipynb - Linear regression analysis and its applications.

Each notebook is self-contained and includes theoretical background, practical examples, and exercises to facilitate hands-on learning and application of the concepts discussed.

Installation and Usage

To get started with these materials, clone the repository using the following command:

git clone <https://github.com/Marco-Sau/Computational-Statistics.git)https://github.com/Marco-Sau/Computational-Statistics.git>