A review of software and tools commonly used for statistical analysis in the mining and mineral industry.

Statistical analysis is a crucial aspect of the mining and mineral industry, helping professionals make informed decisions, optimise processes, and ensure safety and environmental compliance. Here is a review of some commonly used software and tools for statistical analysis in the mining and mineral industry:

R is an open-source statistical programming language and environment. It offers a wide range of statistical and data analysis tools, making it a popular choice for data scientists and statisticians in the mining industry.
Key Features: Extensive libraries for data manipulation, visualisation, and statistical modeling; strong community support; customisable through packages.

Python:

Python is a versatile programming language widely used for data analysis, including statistical analysis in mining and mineral exploration.
Key Features: Rich ecosystem of libraries (e.g., NumPy, pandas, SciPy, scikit-learn) for data manipulation, statistical analysis, and machine learning; readability and ease of use.

MATLAB:

MATLAB is a proprietary programming environment often used for numerical computing and data analysis in engineering and scientific applications, including mining.
Key Features: Extensive built-in functions for data analysis, statistics, and mathematical modeling; excellent visualisation capabilities.

SPSS (Statistical Package for the Social Sciences):

SPSS is a widely used commercial statistical software package. While originally designed for social sciences, it is also employed in the mining industry for data analysis.
Key Features: User-friendly interface, extensive statistical procedures, data visualisation, and reporting tools.

SAS (Statistical Analysis System):

SAS is a powerful commercial software suite used for advanced analytics and statistical analysis in various industries, including mining.
Key Features: Comprehensive statistical and data analysis capabilities, data management, and reporting; robust scripting language.

JMP:

JMP is a user-friendly, desktop-based statistical software application that is particularly useful for data exploration and visualisation.
Key Features: Interactive and intuitive interface, extensive graphics and visualisation tools, and statistical analysis capabilities.

Minitab:

Minitab is a statistical software package often used for quality improvement and process optimisation in mining and manufacturing.
Key Features: User-friendly interface, statistical analysis, and graphical visualisation tools; ideal for Six Sigma and quality control applications.

Statistica:

Statistica is a comprehensive data analysis and visualisation software suite that supports advanced statistical modeling and data mining.
Key Features: User-friendly interface, extensive statistical capabilities, and integration with data mining techniques.

Weka:

Weka is an open-source data mining software with a graphical user interface. It is used for machine learning and predictive modeling in mining applications.
Key Features: User-friendly interface, extensive machine learning algorithms, and data preprocessing tools.

Microsoft Excel:

While not a dedicated statistical software, Microsoft Excel is often used for basic statistical analysis and reporting in the mining industry due to its widespread availability and familiarity.

Key Features: Built-in statistical functions and data analysis tools; easy data manipulation and charting capabilities.

Selecting the right software or tool for statistical analysis in the mining and mineral industry depends on the specific needs of the project, the level of expertise of the users, and budget considerations. Many professionals in the field use a combination of these tools to address various aspects of their work, from exploration and resource estimation to process optimisation and environmental compliance.