Now, there are an infinite number of mathematical transformations out there.

Which one should I do?

That's where an underlying, a basic,

knowledge of the key math functions that we discussed in another module really come

into play, and those basic functions are the linear.

They are the power.

They are the exponential, and they are the log function.

So those are the ones that we most frequently use when we're thinking

about transforming data, and given the relationship isn't a straight line then of

those functions the one that we will find used most in practice is the log function.

Doesn't mean that it's always going to work for you, but

it certainly can provide for some flexible models.

And what I've done is taken the data in this particular example, and by the way,

the product is a pet food, and what we're looking at is the price that the pet food

is sold for and the quantities cases sold, in this case.

And what we've done is take the price and

the quantities sold, and applied the log transform to them.

And in this case, I'm using the natural logarithm.

And so we've taken the log transform and when we look at the data on

the log scale, you can see that the relationship appears

much more linear than it did on the original scale of the data.

So one of our basic approaches to seeing curvature in data

is to consider transforming the data and

if you ask me, well what transformation should I do, my answer generically is

going to be do a transformation that achieves linearity on the transform scale.

How do I know I achieve linearity?

Well, the answer is going to be always, always plot your data.

Have a look at it on the transform scale.

And when I look at this data on the transform scale, and that's the plot at

the bottom left-hand side here, you see it's approximately linear.

And putting a line through the data on the transform scale

seems to make much more sense.