By Michael Greenacre

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SUMMARY: Principal Component Analysis Biplots . . . . . . . . . . . . . . . . . . . . 59 61 61 61 63 63 64 64 65 66 67 The last section of Chapter 5 defined a generalized form of PCA where rows and columns were weighted. 3, there is no need to differentially weight the rows or the columns: on the one hand, the countries should be treated equally, while on the other hand, the variables are all on the same 1 to 9 scale, and so there is no need to up- or downweight any variable with respect to the others (if variables were on different scales, the usual way to equalize out their roles in the analysis is to standardize them).

Multidimensional scaling biplot—data set “attributes” . . . . . . . . . . . . . . . . . . Chi-square distance biplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . SUMMARY: Multidimensional Scaling Biplots . . . . . . . . . . . . . . . . . . . . . . 43 44 45 46 48 50 In the first class that I give in my postgraduate course “Methods of Marketing Research”, I ask the students, who usually come from a number of different countries, to evaluate the similarities and differences between these countries on a scale from 1 (most similar) to 9 (most different).

SUMMARY: Principal Component Analysis Biplots . . . . . . . . . . . . . . . . . . . . 59 61 61 61 63 63 64 64 65 66 67 The last section of Chapter 5 defined a generalized form of PCA where rows and columns were weighted. 3, there is no need to differentially weight the rows or the columns: on the one hand, the countries should be treated equally, while on the other hand, the variables are all on the same 1 to 9 scale, and so there is no need to up- or downweight any variable with respect to the others (if variables were on different scales, the usual way to equalize out their roles in the analysis is to standardize them).

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