How to calculate UCL from Mahalanobis Distance

[#1]

Hi all,
Currently, I am using JMP 12 version
Do you know how to calculate UCL distance from Mahalanobis Distance chart?

If you guy know how to get it, could you please show me the formula?

Sincerely

[#2]

Click the "?" from JMP toolbar and then click on Mahalanobis Distances, this will open Help page Outlier Analysis . From here you can find Mahalanobis Distance and find its documentation Mahalanobis Distance Measures and finally

[#3]

Hi Jthi,

got it -- I already find the document due to your instruction.

However, do you know whether we can get syntax / code to calculate UCL

Thanks

[#4]

This example illustrates how to calculate UCL with JSL.

Names Default to Here( 1 );

alpha = 0.05;	// typical decision level for type I error risk
n = 1600;		// about the number in your plot
p = 5; 			// guess of the number of variables used to compute distance

ucl = Sqrt( (((n-1)^2)/n) * Beta Distribution( 1-alpha, p/2, (n-p-1)/2 ) );

[#5]

Hi,
Thank for your instruction
I try to follow your instruction but the result is not correct.
I will display the script below and image

Based on your instruction, does p =2 and n = 85

Multivariate(
	Y(:DELTAMINIZ, :CARRIER_X),
	Estimation Method("Row-wise"),
	Matrix Format("Square"),
	Scatterplot Matrix(Density Ellipses(1), Shaded Ellipses(0), Ellipse Color(3)),
	Jackknife Distances(1)
);

[#6]

What is the UCL shown if you click the red triangle next to Multivariate and select Outlier Analysis > Mahalanobis Distance?

My apologies! I used the wrong function. I also added the calculation of the jack-knife UCL. Please use this modified script instead of the one that I posted earlier.

Names Default to Here( 1 );

alpha = 0.05;	// typical decision level for type I error risk
n = 31;		// about the number in your plot
p = 7; 			// guess of the number of variables used to compute distance

m ucl = Sqrt( (((n-1)^2)/n) * Beta Quantile( 1-alpha, p/2, (n-p-1)/2 ) );

j ucl = Sqrt( (((n-2)*(n^2))/((n-1)^3))*((m ucl^2)/(1-((n*(m ucl^2))/((n-1)^2)))) );

Show( m ucl, j ucl );

[#7]

It works like a gem. Appreciate !

[#8]

Hi Mark_Bailey,

I use Jackknife method to detect outlier, but I found 1 case that I really need your advice.
You can refer to the images below.
Based on the right chart, all the data points on 8th carrier_x should be outliers. However, when I use Jackknife method with default settings, this method only points out 1 outlier instead 4.

I believe that not enough data points leads to the result

Do you have any suggestion / method that I can catch outliers more efficiently? 

[#9]

Maybe the four episode outliers blog post series can help you or at least give some ideas

Outliers Episode 1: The elusive outlier described, visually identified, and judged 

Outliers Episode 2: Detecting outliers using quantile ranges 

Outliers Episode 3: Detecting outliers using the Mahalanobis distance (and T2) 

Outliers Episode 4: Detecting outliers using jackknife distance 

[#10]

Appreciate Jthi for your recommendation