Linear Regression as a Method to Prioritize Humanitarian Efforts in Stability Operations

Area of Science:

• Operational Research
• Military Science
• Data Analysis

Background:

• Special Operations Forces (SOF) require effective methods for humanitarian efforts to access populations and understand the human terrain.
• Shifting Rules of Engagement (ROE) necessitate non-lethal influence strategies.
• Resource constraints challenge the selection of impactful humanitarian projects in complex environments.

Purpose of the Study:

• To demonstrate how linear regression can aid SOF commanders in selecting optimal humanitarian projects.
• To present a data-driven methodology for resource allocation in stability operations.
• To highlight accessible data analysis techniques for tactical environments.

Main Methods:

• Utilizing linear regression, a statistical tool available in Microsoft Excel.
• Analyzing publicly available information (PAI) to model predictive outcomes.
• Identifying significant independent variables influencing project selection.

Main Results:

• Linear regression enables the determination of variable significance and effect for project recommendations.
• Data-driven assessments using PAI facilitate mutually beneficial shaping efforts.
• The methodology allows for analysis without specialized statistical software.

Conclusions:

• Linear regression offers a practical solution for SOF to make informed humanitarian project selections.
• Accessible data analysis can enhance operational effectiveness in stability frameworks.
• This approach supports commanders in navigating resource limitations and complex operational needs.