Many work only on specific scale factors: 2x is the most common, with 3x and 4x also present.

In this case, the transform is more than a simple scale factor.

However it certainly may be possible to pick a better scaling factor.

For this reason, the use of a scale factor has been introduced to expand the data point power range.

Note that the scale factor depends on the distribution in question.

Alternatively, a number may be followed immediately by any scale factor.

The resolution of the problem is to find a better scaling factor.

It is seen that The scale factors and are the same as in the two-dimensional case.

Since the size field cannot be expanded, a scaling factor is used.

Scale factors for many other quantities can be derived from the above relationships.

Each of these is modified by a scale factor to adjust the agreement between the model and the real world.

The graph shows the variation of the scale factors for the above three examples.

In a lesson on "How does scale factor affect surface area and volume?"

An example above illustrated how certain scale factors can cause unnecessary precision loss.

Actually increasing the scale factor provides a pretty convincing demonstration of why this is so.

The graph shows the variation of the scale factor with latitude.

At other times, that scale factor differs from 1.

In fact, it may be determined only up to a signed scale factor and an arbitrary time delay.

The necessary condition is that the magnitude of scale factor must not vary too much over the region concerned.

And because we'll have to make quite a few jumps, let us write down the scale factor as a reminder."

Finally, here's what a checkbox looks like in Leopard at various scale factors.

More general is scaling with a separate scale factor for each axis direction.

Such a transformation can be called an enlargement if the scale factor exceeds 1.

A scale factor is a number which scales, or multiplies, some quantity.

As it turns out, the problem was our scale factor, 11, which introduced unnecessary precision requirements.