Abstract: In more complex industrial problems, data are not necessarily one dimensional. Often, we get into analyzing data that have many possibilities. For example, automobiles are built with several features targeting different consumer demographics. Different demographic groups may react differently because the features and preferences vary. Younger consumers may pay more attention to design style, while older consumers place greater emphasis on stability and safety of the design. Similarly, there may be preference discrepancies between men and women in terms of color and comfort. To analyze this data, the traditional seven quality tools may not be adequate. The matrix data analysis method can be used to analyze the data arranged in matrix format. For example, you may want to analyze the customer responses to several attributes of a new product to form a smaller number of uncorrelated variables that are easier to interpret.
Keywords: Principal component analysis - Multivariate analysis - Correlation coefficient matrix - Quality tools - Matrix data analysis
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