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    This study introduces a method to analyze large datasets by estimating overall structure from smaller, randomly selected samples. This approach addresses challenges in V-analysis and 0-analysis for big data. Keywords: data analysis, sample estimation, V-analysis, 0-analysis.

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    Area of Science:

    • Multivariate statistical analysis
    • Data science methodologies

    Background:

    • Traditional V-analysis and 0-analysis face challenges with large numbers of variables and subjects, respectively.
    • Handling extensive datasets requires efficient and scalable analytical techniques.

    Purpose of the Study:

    • To develop a method for estimating the structure of a total dataset from analyses of manageable random samples.
    • To overcome computational limitations associated with large-scale V-analysis and 0-analysis.

    Main Methods:

    • Randomly sampling the total dataset.
    • Computing the structure of each sample using existing BC TRY programs.
    • Combining the structures of the random samples using new BC TRY programs.

    Main Results:

    • Successfully estimated the overall structure of large datasets by integrating results from smaller samples.
    • Demonstrated the applicability of the method in two distinct study examples.
    • Provided a scalable solution for complex multivariate analyses.

    Conclusions:

    • The proposed sampling and combining strategy effectively addresses the problem of large numbers of variables and subjects in V-analysis and 0-analysis.
    • This method offers a practical approach for analyzing large datasets where complete analysis is computationally prohibitive.