The Experimental Design Optimizer is an interactive and data-driven tool designed to help researchers, analysts, and decision-makers design efficient and statistically sound experiments. This tool allows users to specify key parameters such as the number of factors, levels, desired statistical power, and effect size to generate optimal experimental designs (e.g., factorial, fractional factorial, or response surface designs). It provides actionable insights into the trade-offs between sample size, cost, and statistical rigor, helping stakeholders make informed decisions about resource allocation and study design.

Key Features:

1. Dynamic Input Fields: Users can input parameters such as the number of factors, levels, desired power, significance level, and budget constraints.
2. Design Optimization: Automatically generate optimal experimental designs based on user inputs using algorithms like factorial design, fractional factorial design, or D-optimal design.
3. Interactive Charts: Visualize experimental designs and their properties (e.g., aliasing, resolution) using tables, heatmaps, or 3D plots for enhanced clarity.
4. Scenario Testing: Allow users to adjust inputs dynamically to explore how changes in parameters (e.g., budget, power) affect the design.
5. PDF Export Functionality: Generate downloadable reports summarizing the experimental design and optimization results for presentations or sharing with stakeholders.
6. Modern and Stylish Design: A sleek interface with vibrant colors, animations, and clear typography to enhance user engagement.
7. Fully Responsive: Optimized for all devices, ensuring seamless functionality on desktops, tablets, and mobiles.
8. Self-Contained Container: The tool operates within its own container, ensuring no interference with the page header or footer.

Use Cases:

• Researchers designing experiments to maximize efficiency while minimizing costs.
• Industrial engineers optimizing process parameters in manufacturing.
• Academics teaching experimental design principles and techniques.

How It Works:
Users input parameters such as the number of factors, levels, desired power, significance level, and budget constraints. The tool generates an optimal experimental design (e.g., full factorial, fractional factorial, or response surface design) and calculates key metrics such as resolution, aliasing, and required sample size. Results are displayed in tables and visualizations, allowing users to interpret the design’s properties. Users can customize inputs dynamically, analyze scenarios, and download detailed reports in PDF format for further analysis or sharing with stakeholders.