Experiment Design Comparison Tool for Regression Analysis
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Solution Overview
Problem
The derivation of models that explain complex systems to identify linkages between factors and responses is often a time-consuming process due to the need for iterative selection of model types and experiment designs, which can be wasteful and inefficient, especially under practical limitations of cost and time.
Innovation Solution
An apparatus and method that compares and analyzes multiple experiment designs to identify matching factors and responses, using a processor to receive input selections, identify matching terms and responses, and present corrections, while generating statistical power graphs and correlation graphs to support the selection of an appropriate experiment design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If iterative selection of model types and experiment designs is performed to derive models that explain complex systems, then the understanding of system linkages between factors and responses is improved, but the time and cost required for the process increases significantly
Solution Approach 1:
The patent performs preliminary actions by automatically generating multiple candidate experiment designs and their associated models before the user commits to a single design. The system pre-computes statistical power analyses, model comparisons, and factor-response linkages for multiple designs simultaneously, allowing the user to select from pre-evaluated options rather than iteratively developing each design from scratch.
Solution Approach 2:
The patent creates copies of experiment designs with varying parameters (number of runs, factors, responses) and automatically generates corresponding model versions for each copy. This allows parallel evaluation of multiple design alternatives without repeating the entire model derivation process for each one, significantly reducing the time required to compare different experimental approaches.
2Measurement precision
If multiple iterations of model selection and experiment design are performed to achieve sufficient understanding of system linkages, then the quality of the derived model is improved, but the productivity of the research process deteriorates
Solution Approach 1:
The patent systematically varies key parameters of experiment designs (number of runs, number of factors, number of responses, replication levels) to generate multiple candidate designs. For each parameter combination, the system automatically computes statistical power, model fit quality, and resource requirements, enabling rapid comparison of how different parameter settings affect both model quality and research efficiency.
Solution Approach 2:
The patent creates a universal framework that handles multiple types of experiment designs (factorial, fractional factorial, response surface, mixture designs) and multiple model types (linear, quadratic, interaction models) through a single automated comparison system. This multi-functional approach allows the user to evaluate diverse design options within one unified process rather than separately analyzing each design type.
3Ease of manufacture
If practical limitations of cost, availability of materials and time are taken into account in deriving the model, then the feasibility of the experiment design is improved, but the ideal technical performance of the model may be compromised
Solution Approach 1:
The patent incorporates practical constraints by allowing the user to specify budget limits, available time, and material availability as input parameters. The automated comparison system then evaluates experiment designs against these constraints, identifying designs that achieve acceptable model quality within the given resource limitations. The system can adjust design parameters (reducing number of runs, factors, or replications) to meet feasibility requirements while maintaining the best possible model performance under the constraints.
Data Source
AI summary
An apparatus may include a processor caused to: receive indications of selection of an experiment design for regression analysis, of a type of distribution for a simulation of random data in the regression analysis, and of selection of a number of iterations of the simulation of random data; generate executable instructions in a pre-selected programming language to be executable by the processor to perform the regression analysis with the selected number of iterations of simulation of random data and with the selected type of distribution; generate a human readable form of a portion of the first executable instructions that includes the coefficients and terms in mathematical notation, and that specifies the selected number of iterations and the selected type of distribution for the simulation of random data; and present, on a display communicatively coupled to the processor, the human readable form of the portion of the first executable instructions.