turn on suggestions

Auto-suggest helps you quickly narrow down your search results by suggesting possible matches as you type.

Showing results for

- JMP User Community
- :
- Discussions
- :
- Discussions
- :
- Confidence in Std Deviation

Topic Options

- Subscribe to RSS Feed
- Mark Topic as New
- Mark Topic as Read
- Float this Topic for Current User
- Bookmark
- Subscribe
- Printer Friendly Page

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Oct 9, 2015 9:07 AM
(2176 views)

Is there a way to graph Confidence (say .9) in a bunch of Std Deviations other than digging through each of their distributions? Maybe I'm just brain-dead this morning.

*Edit* Right now I'm just doing this but I feel like there is a better way.

x = current report();

est = (x << xpath("//OutlineBox[text() = 'Confidence Intervals']/TableBox/NumberColBox[1]/NumberColBoxItem[2]/text()"));

LCI = (x << xpath("//OutlineBox[text() = 'Confidence Intervals']/TableBox/NumberColBox[2]/NumberColBoxItem[2]/text()"));

UCI = (x << xpath("//OutlineBox[text() = 'Confidence Intervals']/TableBox/NumberColBox[3]/NumberColBoxItem[2]/text()"));

New Table("Sigma GRR",

new column("Estimates", Character, SetValues(est)),

new column("LCI", Character, SetValues(LCI)),

new column("UCI", Character, SetValues(UCI)),

);

1 ACCEPTED SOLUTION

Accepted Solutions

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Oct 9, 2015 3:34 PM
(3994 views)

Solution

Hi, Vince!

Not sure this is what you're after, since there are many, many ways to do this, but you could always "roll your own"...

Names Default to Here(1);

// Open the dataset in question.

BigClass_dt = Open( "$SAMPLE_DATA/Big Class.jmp" );

// Summarize the standard deviation by age.

BigClassSummary_dt = BigClass_dt << Summary(

Group( :age ),

Std Dev( :height ),

output table name("Big Class Summary"));

// Set alpha as a table variable.

BigClassSummary_dt << Set Table Variable("alpha", 0.10);

// Calculate the Confidence Intervals

BigClassSummary_dt << New Column( "LCI",

numeric, continuous,

Formula( Root(((:N Rows - 1) * :Name( "Std Dev(height)" ) ^ 2) / ChiSquare Quantile( 1 - :alpha / 2, :N Rows - 1 ), 2 )),

eval Formula);

BigClassSummary_dt << New Column( "UCI",

numeric, continuous,

Formula( Root( ((:N Rows - 1) * :Name( "Std Dev(height)" ) ^ 2) / ChiSquare Quantile( :alpha / 2, :N Rows - 1 ), 2 ) ),

eval Formula);

// Revel in your fly statistical swerve!

5 REPLIES

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Oct 9, 2015 3:34 PM
(3995 views)

Hi, Vince!

Not sure this is what you're after, since there are many, many ways to do this, but you could always "roll your own"...

Names Default to Here(1);

// Open the dataset in question.

BigClass_dt = Open( "$SAMPLE_DATA/Big Class.jmp" );

// Summarize the standard deviation by age.

BigClassSummary_dt = BigClass_dt << Summary(

Group( :age ),

Std Dev( :height ),

output table name("Big Class Summary"));

// Set alpha as a table variable.

BigClassSummary_dt << Set Table Variable("alpha", 0.10);

// Calculate the Confidence Intervals

BigClassSummary_dt << New Column( "LCI",

numeric, continuous,

Formula( Root(((:N Rows - 1) * :Name( "Std Dev(height)" ) ^ 2) / ChiSquare Quantile( 1 - :alpha / 2, :N Rows - 1 ), 2 )),

eval Formula);

BigClassSummary_dt << New Column( "UCI",

numeric, continuous,

Formula( Root( ((:N Rows - 1) * :Name( "Std Dev(height)" ) ^ 2) / ChiSquare Quantile( :alpha / 2, :N Rows - 1 ), 2 ) ),

eval Formula);

// Revel in your fly statistical swerve!

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Oct 12, 2015 4:03 AM
(1997 views)

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Oct 12, 2015 6:55 AM
(1997 views)

Yeah, smoore2, bootstrapping is one of the "many, many ways" to do this, and it has some advantages.

I wish JMP had implemented a better approach to their bootstrap estimation. I ask Santa for this every Christmas!

I had to write an addin to get the Bias Corrected accelerated (BCa) estimations from the R "boot" package, which is a more defensible method, in my opinion.

Maybe if more individuals lifted their voice in unison, Santa would hear us??

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Oct 12, 2015 12:52 PM
(1997 views)

- Mark as New
- Bookmark
- Subscribe
- Subscribe to RSS Feed
- Get Direct Link
- Email to a Friend
- Report Inappropriate Content

Oct 13, 2015 5:52 AM
(1997 views)