Statistical Programming languages

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Statistical programming languages are programming languages specifically designed for statistical analysis and data manipulation. They are used by statisticians, data scientists, and researchers for tasks such as data visualization, data cleaning, statistical modeling, and machine learning. Some examples of popular statistical programming languages include:

• R: R is an open-source programming language that is widely used for statistical analysis and data visualization. It is particularly popular in academia and has a large community of users who contribute to its development and share packages and libraries. R has a wide range of libraries and packages that can be used for tasks such as data manipulation, statistical modeling, and machine learning.
• SAS: SAS (Statistical Analysis System) is a proprietary programming language that is widely used in business and industry. It is known for its powerful data management capabilities and has a wide range of statistical procedures and functions. SAS is often used for tasks such as data mining, predictive modeling, and business intelligence.
• Python: Python is a general-purpose programming language that is widely used in data science and machine learning. It has a wide range of libraries and packages such as NumPy, Pandas, and Scikit-learn that can be used for tasks such as data manipulation…