Non-linear Components Regression - Quick Tab

Select the Quick tab of the Non-linear Components Regression dialog box to access the transformation functions. You can use these options to specify nonlinear transformations of the previously selected variables. Selected transformations are marked by selecting the check box against each function.

For each selected variable, new temporary variables (in memory, the actual data file is not changed) are created, containing the results of the respective transformations (a new variable is created for each selected transformation for each selected variable). The new variable names is a combination of the respective original variable number and the chosen transformation, for example, V42, 10V7.

Following are the available transformations and their valid ranges. You can select more than one transformation.

Non-Linear Transformation Functions Valid range
X2 (X-squared) -5.0E+08 to 5.0E+08
X3 (X-cubed) -5.0E+05 to 5.0E+05
X4 (X to the fourth power) -5.0E+04 to 5.0E+04
X5 (X to the fifth power) -5.0E+03 to 5.0E+03
Sqrt(X) (Square root of X) X³0
LN(X) (Natural log of X) X>0
LOG(X) (Log base 10 of X) X>0
ex (Euler [e] = 2.71...) -40 to +40
10x (10 to the power X) -18 to +18
1/X (Inverse of X) X ¹ 0
The default missing value code (-999999998) is assigned to the new transformed variables for a case if:
  • The original untransformed variable contains a missing value for the same case
  • The value for the case does not fall within the valid range

Fitting Centered Polynomial Models

The fitting of higher-order polynomials of an independent variable with a mean not equal to zero can create numerical problems. Specifically, the polynomials are highly correlated due to the mean of the primary independent variable. With large numbers, for example, Julian dates this problem is very serious. This can give wrong results if proper protections are not put in place. You must center the independent variable by subtracting the mean, and then computing the polynomials. For more information and analyses with polynomial models in general, see the classic text by Neter, Wasserman, & Kutner. Statistica automatically checks for very large numbers created in the process of computing the polynomials, and issues a warning message of potential multicollinearity problems.