Ribbon
bar. Select the Data tab.
In the Transformations group,
click Filter/Recode and select
MD Imputation from the menu to
display the *MD Imputations* dialog box.

Classic
menus. From the *Data - Data Filtering/Recoding* submenu, select
*MD Imputation* to display the *MD Imputations* dialog box.

Use the options in this dialog box to specify
the parameters for the k-nearest
neighbor algorithm and then perform missing data replacement using that
algorithm. For more information on this algorithm, see __K-Nearest Neighbors Introductory Overview__.

**Input/Output.**
Use the options in the *Input/Output *group box to specify the criteria
for the k-nearest neighbor algorithm.
Statistica uses the information you give here to determine what values
to input to the missing data. Note that all nearest neighbor calculations
will be based on standardized Euclidean distances.

**Variables.**
Click the *Variables* button to display a variable selection dialog
box. Select one or more continuous or categorical target variables, and
a list of input variables (which can be continuous, categorical, or a
mixture of both). Note that the variables you select here will be used
in the algorithm and included in the resulting (output) spreadsheet.

**Cases.**
Click the *Cases* button to display the __ Spreadsheet
Case Selection Conditions__ dialog box, which contains
options to select only specified observations or cases for the data filtering
operation.

**Use Caseweights.**
Select this check box to use the currently assigned spreadsheet case weights
before applying the k-nearest
neighbor algorithm. When this check box is selected, values of the case
weight variable specified in the Spreadsheet Case Weights
dialog box will be used as case multipliers before the algorithm is applied.
If the check box is cleared, the assigned case weight will be disregarded
for this analysis. Note that when case weights have not been assigned,
this check box will be dimmed.

**K-value.**
In this field, specify the number of nearest neighbors *K*. This
option may significantly influence the quality of inference. By default,
this value is set to 3; however, you can specify any k between 1 and 500.

**No. of exemplars.** Enter the number
of cases to use in the prototype (exemplar) data set. Statistica *K-Nearest
Neighbors* divides the data into prototype and test samples. The testing
sample is used to create a set of new query points for which the outcomes
are estimated from the known values of the *K* nearest cases in the
prototype sample.

**OK.** Click *OK* to accept the options specified
here and use the k-nearest neighbor
algorithm on the current spreadsheet.

**Cancel.** Click *Cancel *to close this dialog
box without using the k-nearest
neighbor algorithm on the current spreadsheet.