Statistical Analysis with SAS

A collection of SAS programs, examples, and projects covering statistical analysis, data exploration, hypothesis testing, regression, ANOVA, and other applied analytics techniques.

About This Repository

This repository provides practical examples of statistical analysis using SAS. It includes reusable SAS programs, sample analyses, and step-by-step demonstrations of commonly used statistical methods.

The repository is suitable for students, analysts, researchers, and data professionals who want to strengthen their SAS programming and statistical analysis skills.

Topics Covered

• SAS programming fundamentals
• Data import and preparation
• Data exploration
• Descriptive statistics
• Data visualization
• Probability distributions
• Hypothesis testing
• Confidence intervals
• Correlation analysis
• Simple linear regression
• Multiple linear regression
• Analysis of variance
• Categorical data analysis
• Model diagnostics
• Statistical result interpretation

Repository Structure

Statistical-Analysis-With-SAS/
│
├── data/                 # Sample datasets
├── programs/             # SAS program files
├── assets/               # Images and supporting resources
└── README.md

The repository structure may evolve as additional topics and projects are added.

Requirements

The SAS programs can be run using one of the following environments:

• SAS Studio
• SAS OnDemand for Academics
• SAS 9.4
• SAS Viya
• Another compatible SAS environment

Getting Started

Clone the repository:

git clone https://github.com/theanalyticschool/Statistical-Analysis-With-SAS.git

Navigate to the project directory:

cd Statistical-Analysis-With-SAS

Open the required .sas file in your SAS environment and run the program.

Example SAS Program

proc import datafile="data/sample_data.csv"
    out=work.sample_data
    dbms=csv
    replace;
    guessingrows=max;
run;

proc contents data=work.sample_data;
run;

proc means data=work.sample_data
    mean median std min max;
run;

Learning Objectives

By working through the examples in this repository, you will learn how to:

• Prepare and validate data in SAS
• Generate descriptive summaries
• Select an appropriate statistical method
• Test statistical assumptions
• Build and evaluate statistical models
• Interpret SAS output
• Communicate analytical findings clearly

Contributing

Contributions, corrections, and suggestions are welcome.

To contribute:

1. Fork the repository.
2. Create a new branch.
3. Make your changes.
4. Commit your changes.
5. Push the branch to your fork.
6. Submit a pull request.

Disclaimer

The datasets and examples in this repository are intended for educational purposes. Results should be independently validated before applying the code to production, academic research, or business decisions.

Author

Analytics School

GitHub: theanalyticschool