Bivariate

Example 1

Summary: Perform bivariate analysis

Code:

// Open data table
dt = Open("$Sample_Data/AdverseR.jmp");
// Bivariate
Bivariate(
    Y( :DAY ON DRUG ),
    X( :WEIGHT )
);

Example 2

Summary: Build a custom 2x2 display

Code:

// Open data table
dt = Open("$Sample_Data/Anscombe.jmp");
// The Quartet
New Window( "The Quartet",
    H List Box(
        V List Box(
            Bivariate(
                x( :x1 ),
                y( :y1 ),
                Show Points( 0 ),
                Fit Line
            ),
            Bivariate(
                x( :x2 ),
                y( :y2 ),
                Show Points( 0 ),
                Fit Line
            )
        ),
        V List Box(
            Bivariate(
                x( :x3 ),
                y( :y3 ),
                Show Points( 0 ),
                Fit Line
            ),
            Bivariate(
                x( :x4 ),
                y( :y4 ),
                Show Points( 0 ),
                Fit Line
            )
        )
    )
);

Example 3

Summary: Perform a paired t-test using the bivariate platform

Code:

// Open data table
dt = Open("$Sample_Data/BabySleep.jmp");
// bivariate
Bivariate(
    x( Awake ),
    Y( Asleep ),
    Paired T Test
);

Example 4

Summary: Perform bivariate analsysis with a fit line

Code:

// Open data table
dt = Open("$Sample_Data/Big Class.jmp");
// Bivariate
Bivariate(
    Y( :weight ),
    X( :height ),
    Fit Line
);

Example 5

Summary: Perform bivariate analysis with a second order polynomial fit

Code:

// Open data table
dt = Open("$Sample_Data/Birth Death.jmp");
// Bivariate
Bivariate(
    Y( :death ),
    X( :birth ),
    Fit Polynomial( 2 )
);

Example 6

Summary: Perform bivariate analysis with a t test

Code:

// Open data table
dt = Open("$Sample_Data/Blood Pressure by Time.jmp");
// bivariate
Bivariate(
    x( BP AM ),
    Y( BP PM ),
    Paired T Test
);

Example 7

Summary: Builld a custom display window with four analysis placed horizontally

Code:

// Open data table
dt = Open("$Sample_Data/Cola Heart Rate.jmp");
// Fit Y by X Group
New Window(
    "Cola Heart Rate- Fit Y by X of Heart Rate",
    H List Box(
        Oneway(
            Y( :Heart Rate ),
            X( :Drink ),
            Box Plots( 0 ),
            Mean Diamonds( 0 ),
            SendToReport(
                Dispatch( {}, "",
                    NomAxisBox,
                    Rotated Tick Labels(
                        1
                    )
                )
            )
        ),
        Oneway(
            Y( :Heart Rate ),
            X( :Testers ),
            Box Plots( 0 ),
            Mean Diamonds( 0 )
        ),
        Oneway(
            Y( :Heart Rate ),
            X( :"Time (Raw)"n ),
            Box Plots( 0 ),
            Mean Diamonds( 0 )
        ),
        Bivariate(
            Y( :Heart Rate ),
            X( :"Time (Numeric)"n )
        )
    )
);

Example 8

Summary: Generate a bivariate analsys with discrete values of hue and shade

Code:

// Open data table
dt = Open("$Sample_Data/Color Palette.jmp");
// Bivariate
Bivariate( Y( :hue axis ), X( :shade ) );

Example 9

Summary: Perform bivariate analysis with nonparametric density contours

Code:

// Open data table
dt = Open("$Sample_Data/Cytometry.jmp");
// Quantile Density Contours
Bivariate(
    Y( :CD8 ),
    X( :CD3 ),
    Nonpar Density
);

Example 10

Summary: Perform a Bivariate Analysis to Examine the Relationship Between Difference and Mean.

Code:

// Open data table
dt = Open("$Sample_Data/Dogs.jmp");
// Fit diff by mean
Bivariate( Y( :diff ), X( :mean ) );

Example 11

Summary: Perform a bivariate analysis fitting LogHist1 as the response variable and LogHist0 as the explanatory variable using the Bivariate platform.

Code:

// Open data table
dt = Open("$Sample_Data/Dogs.jmp");
// Fit LogHist1 By LogHist0
Bivariate(
    Y( :LogHist1 ),
    X( :LogHist0 )
);

Example 12

Summary: Generate a bivariate analysis of speed and weight for different football positions, overlaying 50% density ellipses.

Code:

// Open data table
dt = Open("$Sample_Data/Football.jmp");
// Bivariate by Position
Bivariate(
    Y( :Speed2 ),
    X( :Weight ),
    Group By( "Position2" ),
    Density Ellipse( 0.5 )
);

Example 13

Summary: Create a bivariate analysis with a fit line, quadratic fit, cubic fit, and cubic spline in the Bivariate platform.

Code:

// Open data table
dt = Open("$Sample_Data/Growth.jmp");
// Bivariate
Bivariate(
    Y( :ratio ),
    X( :age ),
    Fit Line,
    Fit Polynomial( 2 ),
    Fit Polynomial( 3 ),
    Fit Spline( 1000 )
);

Example 14

Summary: Generate Passing Bablok regression plots for multiple methods comparing them to a standard measurement.

Code:

// Open data table
dt = Open("$Sample_Data/Method Comparison.jmp");
// Passing Bablok
Fit Group(
    Bivariate(
        Y( :Method 1 ),
        X( :Standard ),
        Fit Passing Bablok
    ),
    Bivariate(
        Y( :Method 2 ),
        X( :Standard ),
        Fit Passing Bablok
    ),
    Bivariate(
        Y( :Method 3 ),
        X( :Standard ),
        Fit Passing Bablok
    ),
    Bivariate(
        Y( :Method 4 ),
        X( :Standard ),
        Fit Passing Bablok
    ),
    <<{Arrange in Rows( 1 )}
);

Example 15

Summary: Imperative sentence: Create a bivariate plot of the edge variable against the nub variable using the Bivariate platform.

Code:

// Open data table
dt = Open("$Sample_Data/Pollen.jmp");
// Bivariate
Bivariate( Y( :edge ), X( :nub ) );

Example 16

Summary: Create a bivariate plot to visualize the relationship between F Rate 60+ and F Rate 0-19 from the PopAgeGroup data table.

Code:

// Open data table
dt = Open("$Sample_Data/PopAgeGroup.jmp");
// Gender Portion
Bivariate(
    Y( :"F Rate 60+"n ),
    X( :"F Rate 0-19"n ),
    SendToReport(
        Dispatch( {}, "Bivar Plot",
            FrameBox,
            {Frame Size( 706, 434 ),
            Marker Drawing Mode( "Fast" )
            }
        )
    )
);

Example 17

Summary: Perform a bivariate analysis with jittering and label offset settings for maximum January temperature points.

Code:

// Open data table
dt = Open("$Sample_Data/Pollutants Map.jmp");
// Bivariate
Bivariate(
    Y( :Y ),
    X( :X ),
    SendToReport(
        Dispatch( {},
            "Bivariate Fit of Y By X",
            OutlineBox,
            {
            Set Title(
                "Highlight Maximum January Temperature Points and Label Them"
            )}
        ),
        Dispatch( {}, "Bivar Plot",
            FrameBox,
            {Frame Size( 476, 313 ),
            DispatchSeg(
                Marker Seg( 1 ),
                label offset(
                    {503, 25, 5},
                    {566, -15, 29}
                )
            )}
        )
    )
);

Example 18

Summary: Create a Bivariate plot with a Linear Fit to examine the relationship between OZONE and POP.

Code:

// Open data table
dt = Open("$Sample_Data/Polycity.jmp");
// Bivariate with Linear Fit
Bivariate(
    Y( :OZONE ),
    X( :POP ),
    Fit Line(
        {Line Color( {213, 72, 87} )}
    )
);

Example 19

Summary: Create a bivariate plot to visualize the relationship between the portion of the population aged 60 and above and the portion aged 0-19, adjusting the frame size and marker drawing mode.

Code:

// Open data table
dt = Open("$Sample_Data/PopAgeGroup.jmp");
// Age Portion
Bivariate(
    Y( :"Portion60+"n ),
    X( :"Portion 0-19"n ),
    SendToReport(
        Dispatch( {}, "Bivar Plot",
            FrameBox,
            {Frame Size( 532, 339 ),
            Marker Drawing Mode( "Fast" )
            }
        )
    )
);

Example 20

Summary: Perform a bivariate analysis on the Hours and Temp variables using the Bivariate platform.

Code:

// Open data table
dt = Open("$Sample_Data/Reliability/Devalt.jmp");
// Bivariate
Bivariate( Y( :Hours ), X( :Temp ) );

Example 21

Summary: Generate and customize a bivariate plot of weight vs. height, fitting a linear regression line.

Code:

// Open data table
dt = Open("$Sample_Data/World Class.jmp");
// Bivariate
Bivariate(
    Y( :"weight (lb.)"n ),
    X( :"height (in.)"n ),
    Fit Line
);

Example 22

Summary: Perform Bivariate Analysis on Weight and Height Data with Fit Line and Fit Robust Models

Code:

// Open data table
dt = Open("$Sample_Data/Weight Measurements.jmp");
// Bivariate
Bivariate(
    Y( :weight ),
    X( :height ),
    Fit Line(
        {Line Color( {213, 72, 87} )}
    ),
    Fit Robust(
        {Line Color( {57, 177, 67} )}
    )
);

Example 23

Summary: Perform a bivariate analysis on infant mortality rate against crude birth rate using the World Demographics dataset.

Code:

// Open data table
dt = Open("$Sample_Data/World Demographics.jmp");
// Bivariate: Infant Mortality by Birth Rate
Bivariate(
    Y( :Infant Mortality Rate ),
    X( :"Crude Birth Rate (1000)"n ),
    SendToReport(
        Dispatch( {}, "Bivar Plot",
            FrameBox,
            {Frame Size( 264, 205 ),
            Marker Size( 2 ),
            DispatchSeg(
                Marker Seg( 1 ),
                label offset(
                    {1, -104, 22},
                    {7, 8, -4},
                    {126, -2, 13},
                    {160, -15, -26},
                    {198, -103, -15}
                )
            )}
        )
    )
);