Logistic

Example 1

Summary: Fit a logistic regression model

Code:

// Open data table
dt = Open("$Sample_Data/Big Class.jmp");
// Logistic
Logistic( Y( :age ), X( :weight ) );

Example 2

Summary: Fit a logistic regression model to predict the response based on the dose, and perform inverse prediction at the 0.5 response level.

Code:

// Open data table
dt = Open("$Sample_Data/Dose Response.jmp");
// Logistic
Logistic(
    Y( response ),
    X( dose ),
    Inverse Prediction( Response( 0.5 ) )
);

Example 3

Summary: Perform logistic regression on the O-Ring Failure data table, predicting the Response variable using Temperature as the predictor, and calculate the inverse prediction for a Response value of 0.00063.

Code:

// Open data table
dt = Open("$Sample_Data/O-Ring Failure.jmp");
// Logistic
Logistic(
    Y( Response ),
    X( Temperature ),
    Inverse Prediction(
        Response( 0.00063 )
    )
);

Example 4

Summary: Fit a logistic regression model with a response variable of interest and a predictor variable, including a frequency column.

Code:

// Open data table
dt = Open("$Sample_Data/Penicillin.jmp");
// Logistic
Logistic(
    Y( Response ),
    X( "ln(dose)"n ),
    Freq( count )
);

Example 5

Summary: Perform a logistic regression analysis with age as the dependent variable and weight in pounds as the independent variable.

Code:

// Open data table
dt = Open("$Sample_Data/World Class.jmp");
// Logistic
Logistic(
    Y( :age ),
    X( :"weight (lb.)"n )
);