how do I write a script to compute confidence intervals

[#1]

Hi - I am still new at jsl scripting and am trying to write a script that creates and saves four new columns based on my linear regression fit:

- lower 95% mean confidence interval

- upper 95% mean confidence interval

- lower 95% individual confidence interval

- upper 95% individual confidence interval

obj = Fit Model(
	Y( :LOG_ECOLI_PEAR ),
	Effects( :LOG_TURB_PEAR_FNU),
	Personality( "Standard Least Squares" ),
	Emphasis( "Effect Leverage" ),
	Run(:LOG_ECOLI_PEAR 
	))	;
 
  obj << Prediction Formula;

Thank you!

[#2 · Accepted solution]

Hi @learning_JSL ,

  It's actually really easy. All you need to do is add a few lines of code to your existing JSL and you're done. While you're at it, I highly suggest using the Scripting Index under the Help menu -- it's VERY useful.

  For the mean CI, you'll add the code: obj << Mean Confidence Interval; and for the individual CIs, you'll add obj << Indiv Confidence Interval;. The default is for an alpha of 0.05 (corresponding to your +/- 95%, but you can specify any alpha you want, for example: obj << Mean Confidence Interval(0.1) would correspond to +/-90% CIs.

  Below is some code you can try out using the Diabetes.jmp file.

Hope this helps!,

DS

Names default to here(1);

dt=Open("$SAMPLE_DATA/Diabetes.jmp");

obj_fit = Fit Model(
	Validation( :Validation ),
	Y( :Y ),
	Effects(
		:Age, :Gender, :BMI, :BP, :Total Cholesterol, :LDL, :HDL, :TCH, :LTG,
		:Glucose
	),
	Personality( "Standard Least Squares" ),
	Emphasis( "Effect Leverage" ),
	Run(
		:Y << {Summary of Fit( 1 ), Analysis of Variance( 1 ),
		Parameter Estimates( 1 ), Lack of Fit( 0 ), Scaled Estimates( 0 ),
		Plot Actual by Predicted( 1 ), Plot Regression( 0 ),
		Plot Residual by Predicted( 1 ), Plot Studentized Residuals( 0 ),
		Plot Effect Leverage( 1 ), Plot Residual by Normal Quantiles( 0 ),
		Box Cox Y Transformation( 0 )}
	)
);

obj_fit << Prediction Formula;
obj_fit << Mean Confidence Interval;
obj_fit << Indiv Confidence Interval;

[#3]

Perfect.  Thank you!