Regression Analysis Significance Evaluation via Distribution Comparison
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Solution Overview
Problem
Existing prediction devices do not evaluate the significance of regression models, particularly in cases where regression analysis is performed for various combinations of explanatory variables, limiting the assessment of regression analysis results.
Innovation Solution
An information processing device that performs regression analysis for multiple types of material samples, evaluates errors for each combination of explanatory variables, generates distributions showing the frequency of variable combinations resulting in errors, and compares these distributions to determine the significance of regression analysis results, allowing for the visualization of regression coefficients for significant combinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If regression analysis is performed for multiple combinations of explanatory variables, then the comprehensiveness of analysis is improved, but the complexity of evaluation increases
Solution Approach 1:
The patent segments the evaluation process into two independent parts: (1) evaluating regression analysis results using original data, and (2) evaluating regression analysis results using modified data with swapped objective variable values. This segmentation allows comprehensive evaluation across multiple explanatory variable combinations while managing complexity through structured, modular assessment steps.
Solution Approach 2:
The patent implements feedback by comparing distribution characteristics between results from original data and modified data. The comparison provides feedback on whether regression analysis results are statistically significant, enabling systematic evaluation across multiple variable combinations while controlling complexity through iterative refinement.
2Reliability
If significance evaluation is added to regression analysis, then the reliability of results is improved, but the processing time increases
Solution Approach 1:
The patent performs preliminary actions by generating distributions of regression analysis results before conducting significance evaluation. By pre-computing distributions from both original and modified data, the system prepares all necessary statistical foundations in advance, enabling efficient significance testing without excessive processing time during the evaluation phase.
Solution Approach 2:
The patent creates a copied version of the data by modifying objective variable values (swapping between positive and negative). This copying approach allows significance evaluation through comparison without requiring entirely separate analysis processes, reducing overall processing time while maintaining reliability.
3Measurement precision
If distributions are generated for multiple error combinations, then the precision of evaluation is improved, but the computational load increases
Solution Approach 1:
The patent changes parameters by generating distributions across multiple error combinations rather than evaluating single error values. This parameter change approach improves precision by capturing the variability and statistical significance of regression results, while managing computational load through efficient distribution generation and comparison methods.
Data Source
AI summary
An information processing device includes a first evaluation section that performs regression analysis for plural types of material sample based on first data including plural explanatory variables that are feature values and an objective variable that is a performance by regression analysis for respective combinations explanatory variables and that also evaluates error with respect to the regression analysis result, a second evaluation section that performs regression analysis for respective combinations of explanatory variables based on second data resulting from modifying a value of the objective variable in the first data and that also evaluates error with respect to a result of the regression analysis on the combination, a generation section that generates a distribution expressing a frequency of combinations of the explanatory variables with respect to the regression analysis result with the first data and that generates a distribution expressing a frequency of combinations of the explanatory variables resulting in respective errors for each of the errors with respect to the regression analysis result with the second data, and an output section that outputs a result of comparing the distributions.