Two-Series Transformation (x=f(x,y))

Creates two-series transformations of the kind x=f(x,y) for selected continuous variables; computes a summary graph and spreadsheet of the series after computing the transformation; autocorrelation functions can also be computed.

 Available transformations include:
 Differencing ( x=x-y) with or without lag
 Residualizing ( x=x-(a+b*y(lag))).

Element Name Description
General
Type of transformation Select the desired type of transformation; available transformations include:

 Differencing ( x=x-y)
 with or without lag

 Residualizing (x=x-(a+b*y(lag)))
Lag Specifies the Lag to be used for the selected transformation.
Estimate parameters from data Estimate the parameters (intercept and slope) for the Residualizing transformation from the data via least-squares regression; if set to False, STATISTICA will use user-defined parameter values for this transformation.
Intercept for residualizing Specifies a user-defined value for the intercept parameter (a), for the residualizing transformation x=x-(a+b*y(lag)); if you select the Estimate parameters from data option (for the Residualizing transformation), the intercept (a) and slope (b) for the transformation is computed from the data (via least squares regression), and this value will be ignored.
Slope for residualizing Specifies a user-defined value for the slope parameter (b), for the residualizing transformation x=x-(a+b*y(lag)); if you select the Estimate parameters from data option (for the Residualizing transformation), the intercept (a) and slope (b) for the transformation is computed from the data (via least squares regression), and this value will be ignored.
Generates data source, if N for input less than Generates a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations.
Autocorrelations and Plots
Labels plots with case names Labels the horizontal axis in plots with case numbers or case names (if available).
Creates autocorrelations Creates the autocorrelation function for the differenced series.
Number of lags Enter a value in the Number of lags box to determine the maximum number of lags for which the autocorrelations are to be computed.
Missing Data
Replace missing data Specifies how missing data is to be replaced. Missing data can be replaced by the overall mean, interpolated from adjacent points, replaced by the mean or median of N adjacent points (on both sides of the hole), or estimated (predicted) from linear trend regression.

 Note that as long as the missing data are at the end of the series (trailing missing data) or the beginning of the series (leading missing data), the missing data will simply be ignored.
Number of adjacent points Applicable if missing data are replaced by the mean or median of N adjacent points; specify N.

 The missing data are replaced by the mean or median computed from the N adjacent points on both sides of the hole of missing data.