Quick normality test on many parameters

SophieCuvillier

Hello,

 

I have lots of parameters (100 ~ 2000) each with 200 to 2500 values in the sample. I want to do for each one a statistical test to check if the distribution is normal or not.

 

My problem is that it takes me a long time (about 6 minutes): I have to run the Distribution > Fit Normal > Goodness of fit platform for each parameter and retrieve the p-value from the Anderson test, for example, to make my conclusion.

 

Isn't there a faster way? I'd like to avoid using Python as much as possible, because we've got JMP17 and we're afraid it'll cost a lot to migrate the code when we switch to MP19 in 6 months' time, because it changes a lot.

 

Best regards,

 

Ben_BarrIngh

Hi @SophieCuvillier ,

 

Got you, I've had a try at putting something together for you that will run the make combined data table in a script - this was a fun challenge to get the platform to run all the columns into one window instead of individual windows per column.

 

Let me know how that works for you.

 

//Random Column Generator v1 - Inspired by https://www.linkedin.com/feed/update/urn:li:activity:7241062805292929025/ 

//Get current data table, clear previous selections
dt = CurrentDataTable();
Names Default to Here(1);
dt << Clear Column Selection();

//Launch dialog for columns - adapted from https://community.jmp.com/t5/JMPer-Cable/Prompting-for-columns-totally-modally/ba-p/189647
nw = New Window( "Launch Dialog",
    <<Modal,
    V List Box( Align( "right" ),
        H List Box(
            Panel Box( "Select Columns",
            //Filter for continuous data only
                clb = Filter Col Selector( dt, All,<<continuous(1), <<ordinal(0), <<nominal(0))
            ),
            Panel Box( "Cast Selected Columns for Distribution",
                Lineup Box( N Col( 2 ), Spacing( 5 ),
                    Button Box( "Columns",
                        clbY << Append( clb << Get Selected )
                    ),
                    clbY = Col List Box(
                        "Numeric",
                        MinItems( 1 ),
                        MaxItems( 20 ),
                        nlines( 5 )
                    ),
                    Button Box( "Remove",
                        clbY << Remove Selected
                    )
                )
            )
        ),
        H List Box(
            Button Box( "OK",
                cols = clby << Get Items( "Column Reference" );
            ),
            Button Box( "Cancel" )
        )
    )
);

//Test for cols names
Print(cols);


N = N Items(cols); //count the number of cols

distList = (""); //create a storage location to compile the script commands for each distribution


for(i = 1, i <= N, i++,
    Insert Into(distList,
            (
                "Continuous Distribution(column(" || char(cols[i]) || "), Fit Normal(Goodness of Fit(1))),"
            )
        )
    );

finalScript = "Distribution(" || distList || ");"; //combine the distlist with distribution cols with the distribution command
distribrep=eval(parse(finalscript));

//Target the first Anderson wilkes table
Firsttable=Report(distribrep)[char(cols[1]),"Fitted Normal Distribution","Goodness-of-Fit Test",TableBox(2)]
//Make a combined data table with the results
firsttable<<make combined data table;