Welcome to the "Popular Statistical Tests in R" repository! This repository is a comprehensive collection of popular statistical tests implemented in the R programming language. It provides both the R code for performing the tests and detailed reports with explanations of each test's concept, usage, assumptions, and interpretation of results.
Statistical tests are crucial tools for analyzing data and making informed decisions. This repository aims to provide clear and concise implementations of popular statistical tests using the R programming language. Each test comes with an associated report that explains the theory behind the test, its assumptions, appropriate use cases, and how to interpret the results.
The t-test is used to determine if there is a significant difference between the means of two groups. The repository includes examples of both independent and paired t-tests, along with explanations of their applications and interpretation.
Analysis of Variance (ANOVA) is employed to test the equality of means among multiple groups. The repository covers one-way and two-way ANOVA, with detailed explanations of how to set up and interpret the tests.
The Chi-Square test is used to assess the independence between categorical variables. It is often used in contingency table analysis. This repository provides examples of Chi-Square tests and guides you through the process of interpreting the results.
Correlation analysis is used to quantify the strength and direction of the relationship between two continuous variables. The repository covers Pearson correlation, Spearman rank correlation, and provides insights into understanding and interpreting correlation coefficients.
Regression analysis is used to model the relationship between one or more independent variables and a dependent variable. The repository includes examples of linear regression, logistic regression, and guides you through model interpretation and diagnostics.
Each statistical test is organized in its own directory, containing the R code for conducting the test and generating sample data if necessary. The associated reports provide theoretical explanations, assumptions, step-by-step instructions, and guidance on interpreting the results.
To use the code and reports, follow these steps:
- Clone or download the repository to your local machine.
- Navigate to the directory of the specific test you're interested in.
- Read the associated report to understand the test's context and assumptions.
- Run the provided R code using your preferred R environment.
Contributions to this repository are welcome! If you'd like to add more statistical tests, improve existing code, or enhance the explanations, please feel free to submit pull requests. Make sure to follow the established format and guidelines.
This repository is licensed under the MIT License. You are free to use, modify, and distribute the code and reports for your own purposes. Please refer to the license file for more information.
We hope this repository helps you better understand and apply popular statistical tests using the R programming language. Happy analyzing!
Disclaimer: The information provided in this repository is for educational and informational purposes only. Always consult with a qualified statistician or data analyst before making important decisions based on statistical analysis.