dt = Open( "$SAMPLE_DATA/Wafer Stacked.jmp" );

`Arc Finder(`

Group( :Lot, :Wafer ),

X( :X_Die ),

Y( :Y_Die ),

Min Distance( 12 ), // minimum distance among 3 points to seed an arc

Min Radius( 15 ), // minimum radius of the acceptable arc

Max Radius( 2000 ), // maximum radius of acceptable arc

Max Radius Error( 2 ), // how close a point needs to be added

Min Arc Points( 5 ), // how many points to define an arc

Number of Searches( 500 ), // how many random probes of data

Max Number Arcs( 3 ) // number of arcs searched for

);

dt << Color or Mark by Column( :Arc Number );

dt << Graph Builder(

Size( 1539, 921 ),

Variables( X( :X_Die ), Y( :Y_Die ), Wrap( :Lot_Wafer Label ), Color( :Arc Number ) ),

Elements( Points( X, Y, Legend( 6 ) ) )

);

A list of named values that matches the messages sent to ARIMA Forecast(). If you perform an ARIMA Forecast and save the script, the estimates are part of the script.

Define the range of values. Typically, from is between 1 and to, inclusive. If from is less than or equal to 0, and if from is less than or equal to to, the results include filtered predictions.

Returns the cumulative sum for the current row. Col Cumulative Sum supports By columns, which do not need to be sorted.

Optional: A By variable to compute statistics across groups of rows. Use the By variable in a column formula or in a For Each Row() function.

Optional: A By variable to compute statistics across groups of rows. Use the By variable in a column formula or in a For Each Row() function.

If a data value is assigned by a column property (such as Missing Value Codes), use Col Stored Value() to base the calculation on the value stored in the column instead. See Col Stored Value(<dt>, col, <row>).

Optional: A By variable to compute statistics across groups of rows. Use the By variable in a column formula or in a For Each Row() function.

If a data value is assigned by a column property (such as Missing Value Codes), use Col Stored Value() to base the calculation on the value stored in the column instead. See Col Stored Value(<dt>, col, <row>).

If a data value is assigned by a column property (such as Missing Value Codes), use Col Stored Value() to base the calculation on the value stored in the column instead. See Col Stored Value(<dt>, col, <row>).

Returns the moving average over a given interval based at the current row. Col Moving Average supports By columns.

Col Moving Average( x, 1, 4 );

`// equal weighting of a five-item lagging range`

Col Moving Average( x, 0 );

`// ramp weighting of all preceding items`

Col Moving Average( x, 0, 2, 2 );

`// triangle weighting of a five-item centered range`

Col Moving Average( x, 0.25 );

`// exponential weighting of all preceding items`

Optional: A By variable to compute statistics across groups of rows.Use the By variable in a column formula or in a For Each Row() function.

Calculates the specified quantile p across all rows of the specified column. The result is internally cached to speed up multiple evaluations.

a specified quantile p between 0 and 1.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );

Col Quantile( :height, .5 );

63

Determines how the tie is broken. A tie occurs when the values being ranked are the same. For the data [33 55 77 55], 33 has rank 1 and 77 has rank 4, and the question is how to assign ranking for the 55s. average reports the average of the possible rankings, 2.5, for both 55s. arbitrary matches JMP 12 behavior by assigning the possible rankings in an unspecified order, which could be 2 and 3 or 3 and 2. row assigns the ranks in the order that they originally appear. (The first 55 would be 2 and the second 55 would be 3.)

minimum gives both values the lowest possible rank, 2.

dt = Open( "$SAMPLE_DATA/Big Class.jmp" );

dt << New Column( "stdht", Formula( Col Standardize( height ) ) );

dt << New Column( "stdht2",

Formula( (height - Col Mean( height )) / Col Std Dev( height ) )

);

Calculates the sum across rows in a column. Calculating all missing values (Col Sum(.,.)) returns missing. The result is internally cached to speed up multiple evaluations.

Fit Censored(Distribution("name"), YLow(vector) | Y(Vector), <YHigh(vector)>, <Weight(vector)>, <X(matrix)>, <Z(matrix)>, <HoldParm(vector)>, <Use random sample to compute initial values(percent)>, <Use first N observations to compute initial values(nobs)>)

If you do not have censoring, then use Y and an array of your data, and do not specify YHigh. If you do have censoring, then specify YLow and YHigh as the lower and upper censoring values, respectively.

{Xcenter, yCenter, radius, SSE} = Fit Circle(Xvec, Yvec)

Must be a vector of the same length as vector, and can contain any nonnegative real numbers. Weights represents frequencies, counts, or similar concepts.

n must be 0, 1, 2, or 3, corresponding to Sheather and Jones, Normal Reference, Silverman rule of thumb, or Oversmoother, respectively.

n must be 0, 1, 2, 3, or 4, corresponding to Gaussian, Epanechnikov, Biweight, Triangular, or Rectangular, respectively.

Returns the pth quantile of the arguments. The first argument can be a scalar or a matrix of values between 0 and 1. The remaining arguments can also be specified as values within a single matrix or list argument.

Rowwise sum of the variables specified. Calculating all missing values (Sum(.,.))returns missing.

A data table of p-values and LogWorth values for each Y and X combination. See The PValues Data Table in Predictive and Specialized Modeling.

Performs the same function as the Response Screening platform. See Response Screening in the Predictive and Specialized Modeling book for details.