**3.3 Results of variable selection**

Here we select a subset of variables from 35 variables of Expectation data focusing on the loss of proportion *P*. Suppose we want to keep it under 1%, *q* ¼ 25 which is assigned from **Table 3** and **Figure 1**. The selected variables are marked by � in the right column of **Table 1**. As far as looking at the variables deleted from each block, two variables {8, 9} from 11 variables in "fashion" block, three variables {12, 16, 20} from 12 variables in "brand" block, and five variables {25, 27, 30, 32, 34} from 12 variables in "shop staff" block are deleted. That is, nine variables are selected from the first two blocks and seven from the third block. It can be stated that the proposed method selects a reasonable subset of variables. Comparing the number of deleted variables in the three blocks, a slightly larger number of variables are removed from the third block, so it is thought that questions on "shop staff" have little information rather than those in the other two blocks and some of them have less significance on the prediction efficiency. From this point of view, we can evaluate the usefulness of each question in the questionnaire.

To evaluate the significance of variables, we observe how many times each variable is selected through the selection for *q* ¼ 35, … , 5. Extracting the variables selected over 2*=*3 times (24 or more), for example, in the "fashion" block, variables {1, 6, 10, 11} were selected. Given the fact that the close-up questions are located close to each other (1 to 3, recognition on fashion, 4 to 7—consciousness on fashion, 8 to 11 activity on fashion), it is generally clear that NL.M.PCA using the proportion *P* selects variables well-balanced from the close-up questions. Similarly, if the most frequently selected variables (such as the above four items) are considered as the most important questions, they should be involved in future surveys. If variables are selected a few times, they should not be involved in the future. In such a way, there is a possibility to use the selection results to evaluate the questionnaire itself.
