HP 10413 Datasheet download pdf (Page 141)

141

There are two methods of dealing with this. The first is to use another

measure of goodness of fit. The second is to ‘linearize’ the data (discussed

below). The hp 39g+ provides an alternative measure of goodness of fit via

the RelErr value in the view.

RelErr

as a measure of non-linear fit

RelErr is defined as the measure of the relative error

in predicted values when compared to data values,

and has the formula shown right. The

values are

obtained using the PREDY function internally. The only drawback to RelErr

is that there is no upper limit its value of as there is for the correlation

coefficient. The interpretation placed on it is that the closer it is to zero the

better the model fits the data. This value is available for any of the data

models, including the user defined model.

Alternatively, when data is non-linear in nature you can

transform the data mathematically so that it is linear. Let's

illustrate this briefly with exponential data.

As you can see, I chose a very simple rule for

the data: 2

y =

If you set up a linear fit for the data in S1, and then view the bivariate stats,

you will find that the correlation for a linear fit is 0.9058

As you can easily see from the graph left, a

linear fit is not a very good choice.

If we change now to the SYMB SETUP view

and choose an Exponential fit rather than a

linear fit then the results are far better.

1 2

2 4

3 8

4 16

5 32

6 64

()

RelErr

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HP 10413 Datasheet Page 141