The Distribution Fitting Tool is an interactive and data-driven solution designed to help analysts, researchers, and decision-makers identify the best-fit probability distribution for a given dataset. This tool allows users to input data and fit it to various statistical distributions (e.g., Normal, Exponential, Weibull, Lognormal) to determine which distribution most accurately represents the data. It provides actionable insights into the underlying data structure and helps stakeholders make informed decisions.

Key Features:

1. Dynamic Input Fields: Users can input datasets with up to five variables or upload a CSV file containing their data.
2. Distribution Fitting: Automatically fit the data to multiple distributions and calculate goodness-of-fit metrics such as Kolmogorov-Smirnov (KS) test, Anderson-Darling (AD) test, and Chi-Square test.
3. Interactive Charts: Visualize the fitted distributions using probability density functions (PDFs), cumulative distribution functions (CDFs), and Q-Q plots for enhanced clarity.
4. Scenario Testing: Allow users to adjust inputs dynamically to explore how changes in the dataset affect the best-fit distribution.
5. PDF Export Functionality: Generate downloadable reports summarizing distribution fitting 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 identifying the best-fit distribution for experimental or survey data.
• Engineers modeling failure times or reliability data using statistical distributions.
• Business analysts assessing customer behavior patterns for predictive modeling.

How It Works:
Users input a dataset or upload a CSV file containing their data. The tool fits the data to multiple distributions (e.g., Normal, Exponential, Weibull, Lognormal) and calculates goodness-of-fit metrics. Results are displayed in tables and visualizations, allowing users to compare the fitted distributions. Users can customize inputs dynamically, analyze scenarios, and download detailed reports in PDF format for further analysis or sharing with stakeholders.