Regression Model Reliability Quantification in Data Analysis
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Solution Overview
Problem
Current data analysis systems lack the ability to provide users with reliable information about the reliability of regression models used to predict relationships between analysis conditions and responses in liquid chromatography, making it difficult for users to trust the accuracy of the results.
Innovation Solution
A data analysis system that includes a data storage component for storing analysis results, a data processor for creating regression models using statistical algorithms, and a display for presenting reliability information, allowing users to quantify and visualize the reliability of the regression models based on the variation in response data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a regression model is created using measurement data with large variation, then the model can be created with available data, but the reliability of the regression model is low
Solution Approach 1:
The system calculates reliability metrics (such as R-squared values, standard errors, or confidence intervals) from the regression model and feeds this information back to the user through the display interface. This allows users to see the reliability of predictions and understand the quality of the regression model, enabling them to make informed decisions about whether to trust the model's predictions for their specific analysis conditions.
2Reliability
If the structure of the model expression is not appropriate, then the regression model cannot be accurately created, but determining the appropriate model structure increases system complexity
Solution Approach 1:
The system dynamically adjusts the model expression structure based on the characteristics of the input measurement data. It can automatically select from multiple predefined model types (linear, quadratic, interaction terms) or allow users to specify different model structures. The system then evaluates which model structure provides the best fit for the specific dataset, creating an optimized regression model tailored to the data characteristics rather than using a fixed model structure.
3Measurement precision
If all coordinate points are experimentally measured, then the most accurate response data is obtained, but the number of experiments becomes unmanageably large
Solution Approach 1:
Instead of performing experiments at all coordinate points, the system creates a regression model based on a limited set of actual measurement data. This model serves as a mathematical copy or surrogate that predicts responses for unmeasured coordinate points. The model captures the underlying relationships between analysis conditions and responses, allowing accurate prediction without the need for exhaustive experimental measurement of every possible condition combination.
Data Source
AI summary
A data storage part (2) that stores a plurality of analysis results obtained by a plurality of analyses performed under a plurality of analysis conditions and a plurality of parameters included in the analysis conditions, wherein each analysis result is factor and each analysis condition is response, and the response and the factor are associated with each other, a data processor (4) configured to perform operation using data stored in the data storage part (2), and a display (8) electrically connected to the data processor (4) are included. The data processor (4) is configured to create a regression model indicating a relationship of a variable with the response, by determining coefficients of each of terms constituting a predetermined model expression, in which the factor is the variable, based on the model expression by using a predetermined statistical analysis algorithm, and the data processor (4) is configured to create reliability information, which is able to be referred by a user on the display (8), by quantifying reliability of the regression model based on a relationship with the response.