Screening

Example 1

Summary: Perform a screening analysis with the OCV as the response variable and A1, A2, A3, A4, C1, and C2 as the factors.

Code:

// Open data table
dt = Open("$Sample_Data/Design Experiment/Battery Data.jmp");
// Screening
Screening(
    Y( :OCV ),
    X( :A1, :A2, :A3, :A4, :C1, :C2 )
);

Example 2

Summary: Perform screening analysis to identify significant factors affecting taste in a cake experiment using the Screening function.

Code:

// Open data table
dt = Open("$Sample_Data/Design Experiment/Cake Data.jmp");
// Screening
Screening(
    Y( :Taste ),
    X(
        :Cocoa, :Sugar, :Flour, :Butter,
        :Milk, :Eggs
    )
);

Example 3

Summary: Perform screening analysis on the response variable Y using predictors X1, X2, and X3 from a specific dataset.

Code:

// Open data table
dt = Open("$Sample_Data/Design Experiment/Custom RSM.jmp");
// Screening
Screening( Y( :Y ), X( :X1, :X2, :X3 ) );

Example 4

Summary: Conduct a screening analysis on a design experiment dataset to evaluate the influence of multiple predictor variables on a response variable.

Code:

// Open data table
dt = Open("$Sample_Data/Design Experiment/Piepel.jmp");
// Screening
Screening( Y( :Y ), X( :X1, :X2, :X3 ) );

Example 5

Summary: Perform screening analysis using the Screening function to identify significant factors affecting the Percent Reacted.

Code:

// Open data table
dt = Open("$Sample_Data/Design Experiment/Plackett-Burman.jmp");
// Screening
Screening(
    Y( :Percent Reacted ),
    X(
        :Feed Rate, :Catalyst, :Stir Rate,
        :Temperature, :Concentration
    )
);

Example 6

Summary: Perform a screening analysis for the response variables 'Number Popped' and 'Total Kernels' against the predictor variables 'Brand', 'Time', and 'Power'.

Code:

// Open data table
dt = Open("$Sample_Data/Design Experiment/Popcorn DOE Results.jmp");
// Screening
Screening(
    Y( :Number Popped, :Total Kernels ),
    X( :Brand, :Time, :Power )
);

Example 7

Summary: Perform a screening analysis with Percent Reacted as the response variable and Feed Rate, Catalyst, Stir Rate, Temperature, and Concentration as the explanatory variables.

Code:

// Open data table
dt = Open("$Sample_Data/Design Experiment/Reactor 20 Custom.jmp");
// Screening
Screening(
    Y( :Percent Reacted ),
    X(
        :Feed Rate, :Catalyst, :Stir Rate,
        :Temperature, :Concentration
    )
);

Example 8

Summary: Perform screening analysis using the Screening function with Odor as the response variable and temp, gl ratio, and ht as the covariates.

Code:

// Open data table
dt = Open("$Sample_Data/Odor JSS.jmp");
// Screening
Screening(
    Y( :Odor ),
    X( :temp, :gl ratio, :ht )
);

Example 9

Summary: Screen continuous variables for a response using the Screening function.

Code:

// Open data table
dt = Open("$Sample_Data/Reactor Half Fraction.jmp");
// Screening
Screening(
    Y( :Percent Reacted ),
    X(
        :Feed Rate, :Catalyst, :Stir Rate,
        :Temperature, :Concentration
    )
);

Example 10

Summary: Perform variable screening using the Screening platform on a dataset containing 18 predictors and one response variable.

Code:

// Open data table
dt = Open("$Sample_Data/Supersaturated.jmp");
// Screening
Screening(
    Y( :Y ),
    X(
        :X1, :X2, :X3, :X4, :X5, :X6, :X7,
        :X8, :X9, :X10, :X11, :X12, :X13,
        :X14, :X15, :X16, :X17, :X18
    )
);

Example 11

Summary: Perform a screening analysis to identify significant factors affecting log life in the Weld-Repaired Castings dataset using the Screening platform.

Code:

// Open data table
dt = Open("$Sample_Data/Weld-Repaired Castings.jmp");
// Screening
Screening(
    Y( :"Log Life (×100)"n ),
    X(
        :Initial Structure, :Bead Size,
        :Pressure Treatment,
        :Heat Treatment, :Cooling Rate,
        :Polish, :Final Treatment, :ε1,
        :ε2, :ε3, :ε4
    )
);