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Scaling the Data

When you were only making comparisons based on people’s ages, it was fine to keep the data as it was and to use averages and distances, since it makes sense to compare variables that mean the same thing. However, now you’ve introduced some new variables that aren’t really comparable to age, since their values are much smaller. Having differing opinions about children—a gap of 2, between 1 and −1—may be much more significant in reality than having an age gap of six years, but if you used the data as is, the age difference would count for three times as much.

To resolve this issue, it’s a good idea to put all the data on a common scale so that differences are comparable in every variable. You can do this by determining the lowest and highest values for every variable, and scaling the data so that the lowest value is now 0 and the highest value is now 1, with all the other values somewhere between 0 and 1.


  

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