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PeopleSize 2020

There are two versions of PeopleSize 2020 - Easy and Professional.

Both versions use the same dataset.  You would choose the Pro version to have 3 power facilities:

In general, this version is for when you are designing or specifying something of commercial value, or for teaching the concepts involved.

1. Mixed User Groups

You can define the users of your design, by mixing proportions of the measured populations and gender or age groups.
For example you might apply sales demographics data to define the user group.
You set the proportions, and save them with your own choice of meaningful name, such as 'ipod buyers' or according to whatever you are designing.
Then PeopleSize always calculates percentiles of that group.
It does this iteratively, which is more accurate than a composite distribution. It's important for addressing the issue of exactly who will be using your design - it's unlikely to be 'everyone' or an 'average person'.

2.  Design Percentiles: fit the percentage of People that you intend

  PeopleSize Pro has a 'Monte Carlo Modelling' engine, which calculates the proportion of users who will fit a design in several different dimensions at once.
Because people's proportions vary, every extra dimension that your design has to fit increases the percentage of 'excluded' users. For example, setting 98th percentile stature excludes 2% of users; they stay excluded, whatever else you specify. 
Then 98th buttock-leg length excludes some extra people, because some people with 98th %ile stature are more than 98th %ile in buttock-leg length.  The exclusions accumulate with each dimension.
It's a difficult calculation, because some dimensions, but only some, tend to be big or small in the same person - like stature and leg length.  In that case, the leg length limit doesn't add many exclusions.
Other dimensions vary more, like stature and waist circumference, in which case the waist criterion adds quite a lot of exclusions. 
Consequently, working out the overall percentile for a number of dimensions is very difficult, and needs Monte Carlo Modelling.

3.  Connected Dimension: when the start of one dimension depends on the size of another

  This feature is for situations where the start point of one dimension depends on the size of another dimension. Often this happens when a design is adjustable in some way, for example if a car seat is adjusted to suit a long leg length, what is the range of arm lengths for the gearlever?

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