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A BASIC program package for weighted least squares solutions using a microprocessor with disc memory

H Tyson, M A Fields

    Computer Programs in Biomedicine
    |February 1, 1982
    PubMed
    Summary
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    This study presents a BASIC program for fitting linear additive models using weighted least squares. The method optimizes memory usage on microprocessors, enabling analysis of larger datasets and complex models.

    Area of Science:

    • Computer Science
    • Statistics
    • Scientific Computing

    Background:

    • Linear additive models are widely used for data analysis.
    • Weighted least squares is a common method for fitting these models.
    • Microprocessor memory limitations can restrict the size of matrices and complexity of models that can be analyzed.

    Purpose of the Study:

    • To develop a BASIC program for fitting linear additive models using weighted least squares.
    • To optimize the program for reduced Random Access Memory (RAM) usage on microprocessors.
    • To enable the analysis of larger matrices and more complex models within limited memory constraints.

    Main Methods:

    • The matrix solution was split into several steps to reduce RAM requirements.
    • Intermediate products and subsequent programs were stored on a mini floppy dual-disc drive.

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  • An extended BASIC interpreter allowing multiple open files was utilized to balance computing time and problem size.

    • Main Results:

    • The program successfully reduced the RAM memory space needed in a microprocessor.
    • This optimization allowed for the handling of larger matrices.
    • Problems involving 1600-element matrices were run on a micro with 32000 bytes of memory, fitting a model with 22 parameters to 72 observations.

    Conclusions:

    • The developed BASIC program effectively fits linear additive models using weighted least squares on microprocessors with limited memory.
    • The memory optimization strategy allows for the analysis of larger and more complex statistical models than previously feasible.
    • This approach enhances the utility of microcomputers for advanced statistical computations in scientific research.