Information processing device, information processing method, and storage medium storing program

An information processing method in which a computer:

• performs regression analysis for a plurality of types of a material sample based on first data including a plurality of explanatory variables that are feature values of the material sample and an objective variable that is a performance of the material sample, by performing regression analysis for respective combinations of at least one explanatory variable from among the plurality of explanatory variables, and evaluates error with respect to a result of the regression analysis on the combination;
• performs regression analysis for respective combinations of at least one explanatory variable from among the plurality of explanatory variables based on second data that results from modifying a value of the objective variable in the first data for the plurality of types of material sample, and evaluates error with respect to a result of regression analysis on the combination;
• generates a distribution expressing a frequency of combinations of the explanatory variables resulting in respective errors for each of the errors, based on an evaluation result of the error with respect to a regression analysis result with the first data;
• generates a distribution expressing a frequency of combinations of the explanatory variables resulting in respective errors for each of the errors, based on an evaluation result of the error with respect to a regression analysis result with the second data;
• and
• outputs a result of comparing the distributions.

A non-transitory storage medium storing a program that is executable by a computer to perform processing, the processing comprising:

• performing regression analysis for a plurality of types of a material sample based on first data including a plurality of explanatory variables that are feature values of the material sample and an objective variable that is a performance of the material sample, by performing regression analysis for respective combinations of at least one explanatory variable from among the plurality of explanatory variables, and evaluating error with respect to a result of the regression analysis on the combination;
• performing regression analysis for respective combinations of at least one explanatory variable from among the plurality of explanatory variables based on second data that results from modifying a value of the objective variable in the first data for the plurality of types of material sample, and evaluating error with respect to a result of regression analysis on the combination;
• generating a distribution expressing a frequency of combinations of the explanatory variables resulting in respective errors for each of the errors, based on an evaluation result of the error with respect to a regression analysis result with the first data;
• generating a distribution expressing a frequency of combinations of the explanatory variables resulting in respective errors for each of the errors, based on an evaluation result of the error with respect to a regression analysis result with the second data;
• and
• outputting a result of comparing the distributions.