Sheet: 
(0, 1)		MACROS
(0, 4)		
(0, 5)		
(0, 6)		DIALOGS
(0, 14)		
(0, 15)		
(0, 16)		STRINGS
(0, 18)		
(0, 19)		
(0, 21)		Toolbars
(0, 30)		
(0, 31)		
(0, 32)		Style Names
(0, 33)		
(0, 37)		
(0, 40)		Module
(1, 4)		
(1, 5)		
(1, 14)		
(1, 15)		
(1, 18)		
(1, 19)		
(1, 30)		
(1, 31)		
(1, 37)		
(2, 1)		labels
(2, 2)		code
(2, 3)		comments
(2, 4)		
(2, 5)		
(2, 6)		labels
(2, 7)		item
(2, 8)		x
(2, 9)		y
(2, 10)		width
(2, 11)		height
(2, 12)		text
(2, 13)		init/result
(2, 14)		
(2, 15)		
(2, 16)		labels
(2, 17)		string
(2, 18)		
(2, 19)		
(2, 20)		labels
(2, 21)		tool id
(2, 22)		macro
(2, 23)		down
(2, 24)		enabled
(2, 25)		face
(2, 26)		status bar
(2, 27)		balloon help
(2, 28)		help
(2, 30)		
(2, 31)		
(2, 33)		listbox item
(2, 34)		reference
(2, 35)		defined name
(2, 36)		type
(2, 37)		
(2, 40)		test name
(2, 41)		menu
(2, 42)		seperator
(2, 43)		insert before
(2, 44)		command
(2, 45)		macro
(2, 46)		keyboard shortcut
(2, 47)		status bar message
(2, 48)		help
(2, 49)		on/off
(2, 50)		customize macro
(2, 51)		toolbar
(3, 1)		
(4, 1)		
(4, 2)		Module 1
(4, 6)		
(4, 20)		
(5, 1)		
(5, 2)		  (c) 1994, Diagnostic Development Unit
(5, 6)		dialog.deming.regression
(5, 7)		#NAME?
(5, 8)		
(5, 9)		
(5, 10)		
(5, 11)		
(5, 12)		Deming Regression
(5, 13)		2
(5, 16)		__LongName
(5, 17)		Module 1 vsn 1.0
(5, 20)		toolbar
(5, 21)		23
(5, 22)		 'C:\DEV\STATS\XLBUILD\MODULES\MOD1.XLA'!ROCAnalysisTes
(5, 23)		FALSE
(5, 24)		TRUE
(5, 25)		Picture 14
(5, 26)		Reciever-operator characteristic (ROC) analysis
(5, 27)		
(5, 28)		#NAME?
(5, 29)		ROC
(5, 33)		Variable Names
(5, 34)		
(5, 35)		variable
(5, 36)		2
(5, 39)		module.description
(5, 40)		
(5, 41)		&Statistics
(5, 42)		TRUE
(5, 43)		
(5, 44)		Met&hod Comparison
(5, 45)		
(5, 46)		
(5, 47)		
(5, 48)		
(5, 49)		
(5, 50)		FALSE
(5, 51)		
(5, 52)		
(6, 1)		
(6, 2)		                The University of Leeds
(6, 6)		
(6, 7)		5
(6, 8)		11
(6, 9)		13
(6, 10)		
(6, 11)		
(6, 12)		Range &X:
(6, 13)		
(6, 16)		initialised
(6, 17)		0
(6, 20)		
(6, 21)		24
(6, 22)		 'C:\DEV\STATS\XLBUILD\MODULES\MOD1.XLA'!AltmanBlandTes
(6, 23)		FALSE
(6, 24)		TRUE
(6, 25)		Picture 7
(6, 26)		Altman Bland method comparison
(6, 27)		
(6, 28)		#NAME?
(6, 29)		Altman Bland
(6, 33)		Dataset
(6, 34)		
(6, 35)		dataset
(6, 36)		2x2
(6, 40)		
(6, 41)		&Statistics
(6, 42)		FALSE
(6, 43)		
(6, 44)		&Precision
(6, 45)		
(6, 46)		-
(6, 47)		
(6, 48)		
(6, 49)		
(6, 50)		FALSE
(6, 51)		
(6, 52)		Statistics 1
(7, 1)		
(7, 2)		                All Rights Reserved
(7, 6)		dialog.deming.range.1
(7, 7)		10
(7, 8)		84
(7, 9)		10
(7, 10)		160
(7, 11)		
(7, 12)		
(7, 13)		
(7, 16)		string.licence.token
(7, 17)		MOD1
(7, 20)		
(7, 21)		25
(7, 22)		 'C:\DEV\STATS\XLBUILD\MODULES\MOD1.XLA'!PassingAndBablokMethodComparisonTes
(7, 23)		FALSE
(7, 24)		TRUE
(7, 25)		Picture 8
(7, 26)		Passing and Bablok method comparison
(7, 27)		
(7, 28)		#NAME?
(7, 29)		Passing and Bablok
(7, 40)		
(7, 41)		&Statistics
(7, 42)		FALSE
(7, 43)		
(7, 44)		D&iagnostic Testing
(7, 45)		
(7, 46)		-
(7, 47)		
(7, 48)		
(7, 49)		
(7, 50)		FALSE
(7, 51)		
(7, 52)		Statistics 1
(8, 1)		
(8, 2)		                Simon Huntington
(8, 6)		
(8, 7)		5
(8, 8)		11
(8, 9)		37
(8, 10)		
(8, 11)		
(8, 12)		Range &Y:
(8, 13)		
(8, 15)		
(8, 16)		string.loading
(8, 17)		Loading add-on module 1 (vsn 1.00)...
(8, 20)		
(8, 21)		26
(8, 22)		 'C:\DEV\STATS\XLBUILD\MODULES\MOD1.XLA'!DemingRegressionTes
(8, 23)		FALSE
(8, 24)		TRUE
(8, 25)		Picture 9
(8, 26)		Deming regression
(8, 27)		
(8, 28)		#NAME?
(8, 29)		Deming
(8, 32)		styles.imprecision.analysis
(8, 33)		Statistics
(8, 34)		MOD1.XLA!AH11:AK21
(8, 39)		string.normality
(8, 40)		Normality
(8, 41)		&Descriptive Statistics
(8, 42)		TRUE
(8, 43)		
(8, 44)		&Normality
(8, 45)		NormalityTest
(8, 46)		
(8, 47)		Godness of fit for gaussian distribution and normal probability plot
(8, 48)		#NAME?
(8, 49)		
(8, 50)		FALSE
(8, 51)		29
(8, 52)		Statistics 1
(9, 1)		
(9, 6)		dialog.deming.range.2
(9, 7)		10
(9, 8)		84
(9, 9)		34
(9, 10)		160
(9, 11)		
(9, 12)		
(9, 13)		
(9, 15)		
(9, 16)		string.invalid.astute.version
(9, 17)		Module 1 requires Astute version 1.50 or later.
(9, 20)		
(9, 21)		27
(9, 22)		 'C:\DEV\STATS\XLBUILD\MODULES\MOD1.XLA'!GalenAndGambinoTes
(9, 23)		FALSE
(9, 24)		TRUE
(9, 25)		Picture 10
(9, 26)		Galen and Gambino analysis
(9, 27)		
(9, 28)		#NAME?
(9, 29)		Galen and Gambino
(9, 33)		Dataset
(9, 34)		MOD1.XLA!AH6:AK7
(9, 39)		string.survival.analysis
(9, 40)		Survival Analysis
(9, 41)		&Statistics
(9, 42)		FALSE
(9, 43)		
(9, 44)		Sur&vival Analysis
(9, 45)		SurvivalAnalysis
(9, 46)		-
(9, 47)		Survival life table analysis
(9, 48)		#NAME?
(9, 49)		
(9, 50)		FALSE
(9, 51)		30
(9, 52)		Statistics 1
(10, 1)		
(10, 2)		
(10, 6)		
(10, 7)		5
(10, 8)		10
(10, 9)		65
(10, 10)		
(10, 11)		
(10, 12)		&Imprecision X:
(10, 13)		
(10, 16)		string.module.not.initialised
(10, 17)		This module is not operational.
(10, 18)		
(10, 20)		
(10, 21)		28
(10, 22)		 'C:\DEV\STATS\XLBUILD\MODULES\MOD1.XLA'!OneRunPrecisionTes
(10, 23)		FALSE
(10, 24)		TRUE
(10, 25)		Picture 18
(10, 26)		NCCLS one run precision evaluation test
(10, 27)		
(10, 28)		#NAME?
(10, 29)		NCCLS One Run Precision
(10, 33)		<No name defined>
(10, 34)		
(10, 35)		
(10, 36)		
(10, 39)		string.altman.bland
(10, 40)		Altman Bland Method Comparison
(10, 41)		Met&hod Comparison
(10, 42)		FALSE
(10, 43)		
(10, 44)		&Altman Bland
(10, 45)		AltmanBlandTest
(10, 46)		
(10, 47)		Altman Bland method comparison
(10, 48)		#NAME?
(10, 49)		
(10, 50)		FALSE
(10, 51)		24
(10, 52)		Statistics 1
(11, 1)		
(11, 6)		dialog.deming.imprecision.x
(11, 7)		8
(11, 8)		120
(11, 9)		62
(11, 10)		94
(11, 11)		
(11, 12)		
(11, 13)		
(11, 16)		string.gng.invalid.cutoff
(11, 17)		You must specify the cut-off value.
(11, 20)		
(11, 21)		29
(11, 22)		 'C:\DEV\STATS\XLBUILD\MODULES\MOD1.XLA'!NormalityTes
(11, 23)		FALSE
(11, 24)		TRUE
(11, 25)		Picture 12
(11, 26)		Goodness of fit for gaussian (normal) distribution
(11, 27)		
(11, 28)		#NAME?
(11, 29)		Normality
(11, 33)		N Replicates
(11, 34)		
(11, 35)		n.1
(11, 36)		1
(11, 39)		string.deming.regression
(11, 40)		Deming Regression
(11, 41)		Met&hod Comparison
(11, 42)		FALSE
(11, 43)		
(11, 44)		&Deming
(11, 45)		DemingRegressionTest
(11, 46)		
(11, 47)		Deming regression
(11, 48)		#NAME?
(11, 49)		
(11, 50)		FALSE
(11, 51)		26
(11, 52)		Statistics 1
(12, 6)		
(12, 7)		5
(12, 8)		101
(12, 9)		89
(12, 10)		
(12, 11)		
(12, 12)		Y:
(12, 13)		
(12, 16)		string.invalid.sensitivity
(12, 17)		Sensitivity must be a number between 0 and 100.
(12, 20)		
(12, 21)		30
(12, 22)		 'C:\DEV\STATS\XLBUILD\MODULES\MOD1.XLA'!SurvivalAnalysi
(12, 23)		FALSE
(12, 24)		TRUE
(12, 25)		Picture 13
(12, 26)		Survival analysis life tables
(12, 27)		
(12, 28)		#NAME?
(12, 29)		Survival Analysis
(12, 33)		N Batches
(12, 34)		
(12, 35)		n.2
(12, 36)		1
(12, 39)		string.passing.Bablok.method.comparison
(12, 40)		Passing and Bablok Method Comparison
(12, 41)		Met&hod Comparison
(12, 42)		FALSE
(12, 43)		
(12, 44)		&Passing and Bablok
(12, 45)		PassingAndBablokMethodComparisonTest
(12, 46)		
(12, 47)		Passing and Bablok method comparison
(12, 48)		#NAME?
(12, 49)		
(12, 50)		FALSE
(12, 51)		25
(12, 52)		Statistics 1
(13, 6)		dialog.deming.imprecision.y
(13, 7)		8
(13, 8)		120
(13, 9)		86
(13, 10)		94
(13, 11)		
(13, 12)		
(13, 13)		
(13, 16)		string.invalid.specificity
(13, 17)		Specificity must be a number between 0 and 100.
(13, 20)		
(13, 21)		31
(13, 22)		 'C:\DEV\STATS\XLBUILD\MODULES\MOD1.XLA'!ReverseGalenAndGambinoTes
(13, 23)		FALSE
(13, 24)		TRUE
(13, 25)		Picture 15
(13, 26)		Reverse Galen and Gambino analysis
(13, 27)		
(13, 28)		#NAME?
(13, 29)		Reverse Galen and Gambino
(13, 33)		Replicate Means
(13, 34)		
(13, 35)		mean.2
(13, 36)		2
(13, 39)		string.one.run.precision.test
(13, 40)		NCCLS One Run Precision Test
(13, 41)		&Precision
(13, 42)		FALSE
(13, 43)		
(13, 44)		NCCLS &One Run
(13, 45)		OneRunPrecisionTest
(13, 46)		-
(13, 47)		NCCLS one run precision evaluation test
(13, 48)		#NAME?
(13, 49)		
(13, 50)		FALSE
(13, 51)		28
(13, 52)		Statistics 1
(14, 6)		
(14, 7)		101
(14, 8)		261
(14, 9)		6
(14, 10)		90
(14, 11)		
(14, 12)		OK
(14, 13)		
(14, 16)		string.invalid.prevalence
(14, 17)		Prevalence must be a number between 0 and 100.
(14, 20)		
(14, 21)		33
(14, 22)		 'C:\DEV\STATS\XLBUILD\MODULES\MOD1.XLA'!GalenAndGambinoCutOffTes
(14, 23)		FALSE
(14, 24)		TRUE
(14, 25)		Picture 17
(14, 26)		ROC - Galen and Gambino analysis
(14, 27)		
(14, 28)		#NAME?
(14, 29)		ROC - Galen and Gambino
(14, 30)		
(14, 33)		Mean
(14, 34)		
(14, 35)		mean
(14, 36)		1
(14, 39)		string.gng
(14, 40)		Galen and Gambino Analysis
(14, 41)		D&iagnostic Testing
(14, 42)		FALSE
(14, 43)		
(14, 44)		&Galen && Gambino
(14, 45)		GalenAndGambinoTest
(14, 46)		
(14, 47)		Galen and Gambino analysis
(14, 48)		#NAME?
(14, 49)		
(14, 50)		FALSE
(14, 51)		27
(14, 52)		Statistics 1
(15, 6)		
(15, 7)		2
(15, 8)		
(15, 9)		
(15, 10)		90
(15, 11)		
(15, 12)		Cancel
(15, 13)		
(15, 16)		string.deming.invalid.x
(15, 17)		You must specify the imprecision for the X dataset.
(15, 22)		FALSE
(15, 28)		FALSE
(15, 33)		Control Mean
(15, 34)		
(15, 35)		mean.1
(15, 36)		1
(15, 39)		string.reverse.gng
(15, 40)		Reverse Galen and Gambino Analysis
(15, 41)		D&iagnostic Testing
(15, 42)		FALSE
(15, 43)		
(15, 44)		Re&verse Galen && Gambino
(15, 45)		ReverseGalenAndGambinoTest
(15, 46)		
(15, 47)		Reverse Galen and Gambino analysis
(15, 48)		#NAME?
(15, 49)		
(15, 50)		FALSE
(15, 51)		31
(15, 52)		Statistics 1
(16, 6)		
(16, 7)		24
(16, 8)		
(16, 9)		62
(16, 10)		
(16, 11)		
(16, 12)		
(16, 13)		
(16, 16)		string.deming.invalid.y
(16, 17)		You must specify the imprecision for the Y dataset.
(16, 33)		Within-Batch SD
(16, 34)		
(16, 35)		sd.1
(16, 36)		1
(16, 39)		string.roc.analysis
(16, 40)		ROC Analysis
(16, 41)		D&iagnostic Testing
(16, 42)		FALSE
(16, 43)		
(16, 44)		&ROC Analysis
(16, 45)		ROCAnalysisTest
(16, 46)		
(16, 47)		Reciever-operator characteristic (ROC) analysis
(16, 48)		#NAME?
(16, 49)		
(16, 50)		FALSE
(16, 51)		23
(16, 52)		Statistics 1
(17, 6)		
(17, 33)		Within-Batch CV
(17, 34)		
(17, 35)		cv.1
(17, 36)		1
(17, 39)		string.gng.cutoff
(17, 40)		ROC - Galen and Gambino Analysis
(17, 41)		D&iagnostic Testing
(17, 42)		FALSE
(17, 43)		
(17, 44)		R&OC - Galen && Gambino
(17, 45)		GalenAndGambinoCutOffTest
(17, 46)		
(17, 47)		ROC - Galen and Gambino analysis
(17, 48)		#NAME?
(17, 49)		
(17, 50)		FALSE
(17, 51)		33
(17, 52)		Statistics 1
(18, 1)		
(18, 2)		Initialise
(18, 3)		initialises the module and the xla if not loaded
(18, 6)		
(18, 16)		string.significance.levels.list
(18, 17)		0.01
(18, 33)		Between-Batch SD
(18, 34)		
(18, 35)		sd.2
(18, 36)		1
(18, 48)		FALSE
(19, 1)		
(19, 2)		FALSE
(19, 3)		
(19, 6)		dialog.reverse.gng
(19, 7)		#NAME?
(19, 8)		
(19, 9)		
(19, 10)		
(19, 11)		
(19, 12)		Reverse Galen and Gambino Analysis
(19, 13)		2
(19, 17)		0.025
(19, 33)		Between-Batch CV
(19, 34)		
(19, 35)		cv.2
(19, 36)		1
(20, 2)		FALSE
(20, 3)		load astute4.xla or astute5.xla
(20, 6)		
(20, 7)		5
(20, 8)		19
(20, 9)		13
(20, 10)		
(20, 11)		
(20, 12)		&Sensitivity:
(20, 13)		
(20, 17)		0.05
(20, 33)		Total Precision SD
(20, 34)		
(20, 35)		sd.3
(20, 36)		1
(21, 1)		astute.path
(21, 2)		 C:\DEV\STATS\XLBUIL
(21, 3)		
(21, 6)		dialog.reverse.gng.sensitivity
(21, 7)		8
(21, 8)		105
(21, 9)		10
(21, 10)		94
(21, 11)		
(21, 12)		
(21, 13)		
(21, 17)		0.1
(21, 33)		Total CV
(21, 34)		
(21, 35)		cv.3
(21, 36)		1
(22, 1)		
(22, 2)		TRUE
(22, 3)		
(22, 6)		
(22, 7)		5
(22, 8)		17
(22, 9)		37
(22, 10)		
(22, 11)		
(22, 12)		Specificit&y:
(22, 13)		
(22, 17)		0.15
(23, 1)		
(23, 2)		FALSE
(23, 3)		
(23, 6)		dialog.reverse.gng.specificity
(23, 7)		8
(23, 8)		105
(23, 9)		34
(23, 10)		94
(23, 11)		
(23, 12)		
(23, 13)		
(23, 32)		styles.normality.test
(23, 33)		Statistics
(23, 34)		MOD1.XLA!AH37:AK51
(24, 1)		
(24, 2)		FALSE
(24, 3)		initialise the main astute module, we should get called back with registertests
(24, 6)		
(24, 7)		5
(24, 8)		10
(24, 9)		70
(24, 10)		
(24, 11)		
(24, 12)		&Prevalence:
(24, 13)		
(24, 16)		string.cutoff.direction
(24, 17)		less than
(24, 33)		Normal Probability Plot
(24, 34)		MOD1.XLA!AH52:AK55
(25, 1)		
(25, 2)		FALSE
(25, 3)		
(25, 6)		dialog.reverse.gng.prevalence
(25, 7)		8
(25, 8)		105
(25, 9)		67
(25, 10)		94
(25, 11)		
(25, 12)		
(25, 13)		
(25, 17)		less than equal
(25, 33)		Descriptive
(25, 34)		MOD1.XLA!AH29:AK36
(26, 1)		
(26, 2)		FALSE
(26, 3)		terminal error, module not initialised
(26, 6)		
(26, 7)		1
(26, 8)		263
(26, 9)		6
(26, 10)		90
(26, 11)		
(26, 12)		OK
(26, 13)		
(26, 17)		greater than equal
(26, 33)		Frequency
(26, 34)		MOD1.XLA!AH56:AK60
(27, 1)		
(27, 2)		FALSE
(27, 3)		
(27, 6)		
(27, 7)		2
(27, 8)		
(27, 9)		
(27, 10)		90
(27, 11)		
(27, 12)		Cancel
(27, 13)		
(27, 17)		greater than
(27, 33)		Dataset
(27, 34)		MOD1.XLA!AH6:AK7
(28, 2)		FALSE
(28, 3)		return initialised state
(28, 6)		
(28, 7)		24
(28, 8)		
(28, 9)		62
(28, 10)		
(28, 11)		
(28, 12)		
(28, 13)		
(28, 33)		N
(28, 34)		
(28, 35)		n
(28, 36)		1
(29, 6)		
(29, 7)		5
(29, 8)		201
(29, 9)		13
(29, 10)		
(29, 11)		
(29, 12)		%
(29, 13)		
(29, 33)		Sum
(29, 34)		
(29, 35)		sum
(29, 36)		1
(30, 2)		RegisterTests
(30, 3)		adds the tools to the palette and initialise help
(30, 6)		
(30, 7)		5
(30, 8)		201
(30, 9)		37
(30, 10)		
(30, 11)		
(30, 12)		%
(30, 13)		
(30, 33)		Mean
(30, 34)		
(30, 35)		mean
(30, 36)		1
(31, 2)		2
(31, 3)		disable user interaction and error reporting
(31, 6)		
(31, 7)		5
(31, 8)		201
(31, 9)		70
(31, 10)		
(31, 11)		
(31, 12)		%
(31, 13)		
(31, 33)		Variance
(31, 34)		
(31, 35)		variance
(31, 36)		1
(32, 2)		TRUE
(32, 3)		
(32, 6)		
(32, 33)		Standard Deviation
(32, 34)		
(32, 35)		sd
(32, 36)		1
(33, 2)		FALSE
(33, 3)		get the version of excel in use
(33, 6)		
(33, 33)		Standard Error
(33, 34)		
(33, 35)		se
(33, 36)		1
(34, 1)		path
(34, 2)		 C:\DEV\STATS\XLBUILD\MODULE
(34, 3)		read the pathname of the xla
(34, 6)		dialog.normality
(34, 7)		#NAME?
(34, 8)		
(34, 9)		
(34, 10)		
(34, 11)		
(34, 12)		Normality
(34, 13)		2
(34, 33)		Skewness
(34, 34)		
(34, 35)		skewness
(34, 36)		1
(35, 2)		FALSE
(35, 3)		build the filename for help
(35, 6)		
(35, 7)		5
(35, 8)		37
(35, 9)		13
(35, 10)		
(35, 11)		
(35, 12)		&Range:
(35, 13)		
(35, 33)		Kurtosis
(35, 34)		
(35, 35)		kurtosis
(35, 36)		1
(36, 2)		FALSE
(36, 3)		build the names of the macros on the astute sheet to use
(36, 6)		
(36, 7)		10
(36, 8)		95
(36, 9)		10
(36, 10)		160
(36, 11)		
(36, 12)		
(36, 13)		
(36, 33)		<No name defined>
(36, 34)		
(36, 35)		
(36, 36)		
(37, 2)		FALSE
(37, 3)		
(37, 6)		
(37, 7)		5
(37, 8)		10
(37, 9)		46
(37, 10)		
(37, 11)		
(37, 12)		&Significance Level:
(37, 13)		
(37, 33)		Coefficient of Skewness
(37, 34)		
(37, 35)		skewness.1
(37, 36)		1
(38, 2)		FALSE
(38, 3)		
(38, 6)		dialog.normality.significance.level
(38, 7)		21
(38, 8)		160
(38, 9)		43
(38, 10)		95
(38, 11)		
(38, 12)		string.significance.levels.list
(38, 13)		
(38, 33)		SD of Skewness
(38, 34)		
(38, 35)		sd.1
(38, 36)		1
(39, 2)		FALSE
(39, 3)		
(39, 6)		
(39, 7)		101
(39, 8)		268
(39, 9)		6
(39, 10)		90
(39, 11)		
(39, 12)		OK
(39, 13)		
(39, 33)		Standardized Skewnesss
(39, 34)		
(39, 35)		skewness.3
(39, 36)		1
(40, 2)		FALSE
(40, 3)		
(40, 6)		
(40, 7)		2
(40, 8)		
(40, 9)		
(40, 10)		90
(40, 11)		
(40, 12)		Cancel
(40, 13)		
(40, 33)		Gaussian Skewness
(40, 34)		
(40, 35)		gaussian.1
(40, 36)		1
(41, 2)		TRUE
(41, 3)		
(41, 6)		
(41, 7)		24
(41, 8)		
(41, 9)		62
(41, 10)		
(41, 11)		
(41, 12)		
(41, 13)		
(41, 33)		Coefficient of Kurtosis
(41, 34)		
(41, 35)		kurtosis.1
(41, 36)		1
(42, 2)		FALSE
(42, 3)		if there is no version number it must be version 1.0
(42, 6)		
(42, 33)		SD of Kurtosis
(42, 34)		
(42, 35)		sd.2
(42, 36)		1
(43, 2)		FALSE
(43, 3)		
(43, 6)		
(43, 33)		Standardized Kurtosis
(43, 34)		
(43, 35)		kurtosis.3
(43, 36)		1
(44, 2)		0
(44, 3)		astute base version less than 1.20
(44, 6)		dialog.one.run.precision.test
(44, 7)		#NAME?
(44, 8)		
(44, 9)		
(44, 10)		
(44, 11)		
(44, 12)		NCCLS One Run Precision Test
(44, 13)		2
(44, 33)		Gaussian Kurtosis
(44, 34)		
(44, 35)		gaussian.2
(44, 36)		1
(45, 2)		FALSE
(45, 3)		
(45, 6)		
(45, 7)		5
(45, 8)		37
(45, 9)		13
(45, 10)		
(45, 11)		
(45, 12)		&Range:
(45, 13)		
(45, 33)		Kolmogorov Smirnov D
(45, 34)		
(45, 35)		kolmogorov.d
(45, 36)		1
(46, 2)		FALSE
(46, 3)		
(46, 6)		
(46, 7)		10
(46, 8)		95
(46, 9)		10
(46, 10)		160
(46, 11)		
(46, 12)		
(46, 13)		
(46, 33)		Kolmogorov Smirnov *D (size stabilized)
(46, 34)		
(46, 35)		kolmogorov.d.1
(46, 36)		1
(47, 2)		FALSE
(47, 3)		
(47, 6)		
(47, 7)		5
(47, 8)		10
(47, 9)		38
(47, 10)		
(47, 11)		
(47, 12)		&Control Mean:
(47, 13)		
(47, 33)		Gaussian *D
(47, 34)		
(47, 35)		gaussian.3
(47, 36)		1
(48, 2)		FALSE
(48, 3)		initialise the help
(48, 6)		dialog.one.run.precision.control.mean
(48, 7)		8
(48, 8)		120
(48, 9)		35
(48, 10)		94
(48, 11)		
(48, 12)		
(48, 13)		
(48, 33)		Anderson Darling A2
(48, 34)		
(48, 35)		anderson.darling.a2
(48, 36)		1
(49, 2)		FALSE
(49, 3)		
(49, 6)		
(49, 7)		101
(49, 8)		268
(49, 9)		6
(49, 10)		90
(49, 11)		
(49, 12)		OK
(49, 13)		
(49, 33)		Anderson Darling *A2 (size stabilized)
(49, 34)		
(49, 35)		anderson.darling.a2.1
(49, 36)		1
(50, 2)		FALSE
(50, 3)		
(50, 6)		
(50, 7)		2
(50, 8)		
(50, 9)		
(50, 10)		90
(50, 11)		
(50, 12)		Cancel
(50, 13)		
(50, 33)		Gaussian *A2
(50, 34)		
(50, 35)		gaussian.4
(50, 36)		1
(51, 2)		FALSE
(51, 3)		
(51, 6)		
(51, 7)		24
(51, 8)		
(51, 9)		62
(51, 10)		
(51, 11)		
(51, 12)		
(51, 13)		
(51, 33)		Normal Distribution X
(51, 34)		
(51, 35)		line.x.2
(51, 36)		2
(52, 2)		FALSE
(52, 3)		add the tools to the statistics palette
(52, 6)		
(52, 33)		Normal Distribution Y
(52, 34)		
(52, 35)		line.y.2
(52, 36)		2
(53, 2)		FALSE
(53, 3)		
(53, 6)		
(53, 33)		X Datapoints
(53, 34)		
(53, 35)		line.x.1
(53, 36)		2
(54, 2)		FALSE
(54, 3)		
(54, 6)		dialog.gng
(54, 7)		#NAME?
(54, 8)		
(54, 9)		
(54, 10)		
(54, 11)		
(54, 12)		ROC - Galen and Gambino Analysis
(54, 13)		2
(54, 33)		Normal Score Y Datapoints
(54, 34)		
(54, 35)		line.y.1
(54, 36)		2
(55, 6)		
(55, 7)		5
(55, 8)		10
(55, 9)		13
(55, 10)		
(55, 11)		
(55, 12)		Range &1:
(55, 13)		
(55, 33)		Bin Minimum
(55, 34)		
(55, 35)		frequency.minimum
(55, 36)		2
(56, 6)		
(56, 7)		10
(56, 8)		83
(56, 9)		10
(56, 10)		160
(56, 11)		
(56, 12)		
(56, 13)		
(56, 33)		Bin Midpoint
(56, 34)		
(56, 35)		frequency.midpoint
(56, 36)		2
(57, 6)		
(57, 7)		5
(57, 8)		10
(57, 9)		37
(57, 10)		
(57, 11)		
(57, 12)		Range &2:
(57, 13)		
(57, 33)		Bin Maximum
(57, 34)		
(57, 35)		frequency.maximum
(57, 36)		2
(58, 1)		
(58, 2)		One sample test macros
(58, 3)		
(58, 6)		
(58, 7)		10
(58, 8)		83
(58, 9)		34
(58, 10)		160
(58, 11)		
(58, 12)		
(58, 13)		
(58, 33)		Frequency
(58, 34)		
(58, 35)		frequency
(58, 36)		2
(59, 1)		
(59, 6)		
(59, 7)		5
(59, 8)		10
(59, 9)		93
(59, 10)		
(59, 11)		
(59, 12)		Positive if result
(59, 13)		
(59, 33)		Cumulative Frequency
(59, 34)		
(59, 35)		frequency.cumulative
(59, 36)		2
(60, 2)		GalenAndGambinoTest
(60, 3)		performs galen and gambino test
(60, 6)		dialog.gng.direction
(60, 7)		21
(60, 8)		136
(60, 9)		90
(60, 10)		188
(60, 11)		
(60, 12)		string.cutoff.direction
(60, 13)		
(61, 2)		FALSE
(61, 3)		
(61, 6)		dialog.gng.cutoff
(61, 7)		8
(61, 8)		136
(61, 9)		110
(61, 10)		75
(61, 11)		
(61, 12)		
(61, 13)		
(61, 32)		styles.survival.analysis
(61, 33)		Life Table 1
(61, 34)		MOD1.XLA!AH64:AK70
(62, 2)		#NAME?
(62, 3)		
(62, 6)		
(62, 7)		101
(62, 8)		260
(62, 9)		6
(62, 10)		90
(62, 11)		
(62, 12)		OK
(62, 13)		
(62, 33)		Life Table 2
(62, 34)		MOD1.XLA!AH71:AK76
(63, 2)		TRUE
(63, 3)		
(63, 6)		
(63, 7)		2
(63, 8)		
(63, 9)		
(63, 10)		90
(63, 11)		
(63, 12)		Cancel
(63, 13)		
(63, 33)		<No name defined>
(63, 34)		
(63, 35)		
(63, 36)		
(64, 2)		FALSE
(64, 3)		
(64, 6)		
(64, 7)		24
(64, 8)		
(64, 9)		62
(64, 10)		
(64, 11)		
(64, 12)		
(64, 13)		
(64, 33)		Variable Names
(64, 34)		
(64, 35)		variable.1
(64, 36)		2
(65, 2)		FALSE
(65, 3)		
(65, 6)		
(65, 7)		FALSE
(65, 33)		Dataset
(65, 34)		
(65, 35)		dataset.1
(65, 36)		2x2
(66, 2)		FALSE
(66, 3)		
(66, 6)		
(66, 33)		Number at Risk
(66, 34)		
(66, 35)		survival.table.1
(66, 36)		2
(67, 6)		
(67, 33)		Risk of Dying
(67, 34)		
(67, 35)		survival.table.2
(67, 36)		2
(68, 6)		
(68, 33)		Chance of Surviving
(68, 34)		
(68, 35)		survival.table.3
(68, 36)		2
(69, 2)		ReverseGalenAndGambinoTest
(69, 3)		performs reverse galen and gambino test
(69, 6)		
(69, 33)		Cumulative Chance of Surviving
(69, 34)		
(69, 35)		survival.table.4
(69, 36)		2
(70, 2)		FALSE
(70, 3)		
(70, 6)		
(70, 33)		Variable Names
(70, 34)		
(70, 35)		variable.2
(70, 36)		2
(71, 2)		#NAME?
(71, 3)		
(71, 6)		
(71, 33)		Dataset
(71, 34)		
(71, 35)		dataset.2
(71, 36)		2x2
(72, 2)		FALSE
(72, 3)		
(72, 6)		
(72, 33)		Number at Risk
(72, 34)		
(72, 35)		survival.table.5
(72, 36)		2
(73, 2)		FALSE
(73, 3)		
(73, 6)		
(73, 33)		Risk of Dying
(73, 34)		
(73, 35)		survival.table.6
(73, 36)		2
(74, 2)		FALSE
(74, 3)		
(74, 6)		
(74, 33)		Chance of Surviving
(74, 34)		
(74, 35)		survival.table.7
(74, 36)		2
(75, 2)		FALSE
(75, 3)		
(75, 6)		
(75, 33)		Cumulative Chance of Surviving
(75, 34)		
(75, 35)		survival.table.8
(75, 36)		2
(76, 2)		TRUE
(76, 3)		if there are 2 or 3 cells selected, copy out the sensitivity, specificity, and prevalence
(76, 6)		
(77, 2)		TRUE
(77, 3)		
(77, 6)		
(77, 32)		styles.gng
(77, 33)		Statistics
(77, 34)		MOD1.XLA!AH80:AK86
(78, 2)		TRUE
(78, 3)		
(78, 6)		
(78, 33)		Dataset
(78, 34)		MOD1.XLA!AH87:AK89
(79, 2)		TRUE
(79, 3)		
(79, 6)		
(79, 33)		<No name defined>
(79, 34)		
(79, 35)		
(79, 36)		
(80, 2)		TRUE
(80, 3)		
(80, 6)		
(80, 33)		Sensitivity
(80, 34)		
(80, 35)		sensitivity
(80, 36)		1
(81, 2)		FALSE
(81, 3)		
(81, 6)		
(81, 33)		Specificity
(81, 34)		
(81, 35)		specificity
(81, 36)		1
(82, 2)		FALSE
(82, 3)		
(82, 6)		
(82, 33)		Predictive Positive Value
(82, 34)		
(82, 35)		predictive.positive
(82, 36)		1
(83, 2)		FALSE
(83, 3)		
(83, 6)		
(83, 33)		Predictive Negative Value
(83, 34)		
(83, 35)		predictive.negative
(83, 36)		1
(84, 2)		FALSE
(84, 3)		
(84, 6)		
(84, 33)		Efficiency
(84, 34)		
(84, 35)		efficiency
(84, 36)		1
(85, 2)		FALSE
(85, 3)		
(85, 6)		
(85, 33)		Prevalence
(85, 34)		
(85, 35)		prevalence
(85, 36)		1
(86, 2)		FALSE
(86, 3)		
(86, 6)		
(86, 33)		Variable Names
(86, 34)		
(86, 35)		variable
(86, 36)		2
(87, 2)		TRUE
(87, 3)		
(87, 6)		
(87, 33)		Range Names
(87, 34)		
(87, 35)		range
(87, 36)		2
(88, 2)		TRUE
(88, 3)		
(88, 6)		
(88, 33)		Dataset
(88, 34)		
(88, 35)		dataset
(88, 36)		2x2
(89, 2)		TRUE
(89, 3)		
(89, 6)		
(90, 2)		FALSE
(90, 3)		display the dialog box
(90, 6)		
(91, 2)		FALSE
(91, 3)		validate the sensitivity is between 0 and 100
(91, 6)		
(92, 2)		FALSE
(92, 3)		
(92, 6)		
(93, 2)		FALSE
(93, 3)		validate the specificity level is between 0 and 100
(93, 6)		
(93, 33)		Variable Name
(93, 34)		
(93, 35)		variable.1
(93, 36)		1
(94, 2)		FALSE
(94, 3)		
(94, 6)		
(94, 33)		Dataset
(94, 34)		
(94, 35)		dataset.1
(94, 36)		2
(95, 2)		FALSE
(95, 3)		validate the prevalence level is between 0 and 100
(95, 6)		
(95, 33)		Variable Name
(95, 34)		
(95, 35)		variable.2
(95, 36)		1
(96, 2)		FALSE
(96, 3)		
(96, 6)		
(96, 33)		Dataset
(96, 34)		
(96, 35)		dataset.2
(96, 36)		2
(97, 2)		TRUE
(97, 3)		
(97, 6)		
(98, 2)		#NAME?
(98, 3)		
(98, 6)		
(98, 32)		styles.altman.bland
(98, 33)		Difference Plot
(98, 34)		MOD1.XLA!AH103:AK115
(99, 2)		#NAME?
(99, 3)		
(99, 6)		
(99, 33)		Frequency
(99, 34)		MOD1.XLA!AH116:AK120
(100, 2)		FALSE
(100, 3)		
(100, 6)		
(100, 33)		Dataset 1
(100, 34)		MOD1.XLA!AH94:AK95
(101, 2)		FALSE
(101, 3)		
(101, 6)		
(101, 33)		Dataset 2
(101, 34)		MOD1.XLA!AH96:AK97
(102, 2)		FALSE
(102, 3)		
(102, 6)		
(102, 33)		<No name defined>
(102, 34)		
(102, 35)		
(102, 36)		
(103, 2)		FALSE
(103, 3)		
(103, 6)		
(103, 33)		Average
(103, 34)		
(103, 35)		line.x.1
(103, 36)		2
(104, 2)		#NAME?
(104, 3)		
(104, 6)		
(104, 33)		Difference
(104, 34)		
(104, 35)		line.y.1
(104, 36)		2
(105, 2)		FALSE
(105, 3)		end of macro close dialog box
(105, 6)		
(105, 33)		Difference %
(105, 34)		
(105, 35)		line.y.2
(105, 36)		2
(106, 2)		TRUE
(106, 3)		
(106, 6)		
(106, 33)		Lower Limit
(106, 34)		
(106, 35)		limit.1
(106, 36)		1
(107, 2)		TRUE
(107, 3)		
(107, 6)		
(107, 33)		CI of Lower Limit Minimum
(107, 34)		
(107, 35)		ci.minimum.1
(107, 36)		1
(108, 2)		TRUE
(108, 3)		end of macro close the dialog box
(108, 6)		
(108, 33)		CI of Lower Limit Maximum
(108, 34)		
(108, 35)		ci.maximum.1
(108, 36)		1
(109, 2)		FALSE
(109, 3)		
(109, 6)		
(109, 33)		Bias
(109, 34)		
(109, 35)		bias
(109, 36)		1
(110, 2)		TRUE
(110, 3)		
(110, 6)		
(110, 33)		CI of Bias Minimum
(110, 34)		
(110, 35)		ci.minimum
(110, 36)		1
(111, 2)		FALSE
(111, 3)		compute galen and gambino to compute the ppv, ppn, efficiency
(111, 6)		
(111, 20)		
(111, 23)		
(111, 33)		CI of Bias Maximum
(111, 34)		
(111, 35)		ci.maximum
(111, 36)		1
(112, 2)		FALSE
(112, 3)		
(112, 6)		
(112, 20)		
(112, 23)		
(112, 33)		Upper Limit
(112, 34)		
(112, 35)		limit.2
(112, 36)		1
(113, 2)		FALSE
(113, 3)		
(113, 6)		
(113, 33)		CI of Upper Limit Minimum
(113, 34)		
(113, 35)		ci.minimum.2
(113, 36)		1
(114, 6)		
(114, 33)		CI of Upper Limit Maximum
(114, 34)		
(114, 35)		ci.maximum.2
(114, 36)		1
(115, 1)		reverse gng table
(115, 2)		
(115, 3)		
(115, 6)		
(115, 33)		Bin Minimum
(115, 34)		
(115, 35)		frequency.minimum
(115, 36)		2
(116, 2)		
(116, 3)		
(116, 6)		
(116, 33)		Bin Midpoint
(116, 34)		
(116, 35)		frequency.midpoint
(116, 36)		2
(117, 6)		
(117, 33)		Bin Maximum
(117, 34)		
(117, 35)		frequency.maximum
(117, 36)		2
(118, 2)		OneRunPrecisionTest
(118, 3)		
(118, 6)		
(118, 33)		Frequency
(118, 34)		
(118, 35)		frequency
(118, 36)		2
(119, 2)		FALSE
(119, 3)		
(119, 6)		
(119, 33)		Cumulative Frequency
(119, 34)		
(119, 35)		frequency.cumulative
(119, 36)		2
(120, 2)		FALSE
(120, 3)		
(120, 6)		
(121, 2)		#NAME?
(121, 3)		
(121, 6)		
(121, 32)		styles.deming
(121, 33)		Statistics
(121, 34)		MOD1.XLA!AH126:AK134
(122, 2)		TRUE
(122, 3)		
(122, 6)		
(122, 33)		Fitted Graph
(122, 34)		MOD1.XLA!AH142:AK144
(123, 2)		FALSE
(123, 3)		
(123, 6)		
(123, 33)		Dataset X
(123, 34)		MOD1.XLA!AH136:AK138
(124, 2)		FALSE
(124, 3)		
(124, 6)		
(124, 33)		Dataset Y
(124, 34)		MOD1.XLA!AH139:AK141
(125, 2)		FALSE
(125, 3)		
(125, 6)		
(125, 33)		<No name defined>
(125, 34)		
(125, 35)		
(125, 36)		
(126, 2)		FALSE
(126, 3)		
(126, 6)		
(126, 33)		Lambda
(126, 34)		
(126, 35)		lambda
(126, 36)		1
(127, 6)		
(127, 33)		Intercept Estimate
(127, 34)		
(127, 35)		estimate.1
(127, 36)		1
(128, 6)		
(128, 33)		CI of Intercept Minimum
(128, 34)		
(128, 35)		ci.minimum.1
(128, 36)		1
(129, 2)		NormalityTest
(129, 3)		
(129, 6)		
(129, 33)		CI of Intercept Maximum
(129, 34)		
(129, 35)		ci.maximum.1
(129, 36)		1
(130, 2)		FALSE
(130, 3)		
(130, 6)		
(130, 33)		Intercept Standard Error
(130, 34)		
(130, 35)		se.1
(130, 36)		1
(131, 2)		#NAME?
(131, 3)		
(131, 6)		
(131, 33)		Slope Estimate
(131, 34)		
(131, 35)		estimate.2
(131, 36)		1
(132, 2)		TRUE
(132, 3)		
(132, 6)		
(132, 33)		CI of Slope Minimum
(132, 34)		
(132, 35)		ci.minimum.2
(132, 36)		1
(133, 2)		FALSE
(133, 3)		
(133, 6)		
(133, 33)		CI of Slope Maximum
(133, 34)		
(133, 35)		ci.maximum.2
(133, 36)		1
(134, 2)		FALSE
(134, 3)		
(134, 6)		
(134, 33)		Slope Standard Error
(134, 34)		
(134, 35)		se.2
(134, 36)		1
(135, 2)		FALSE
(135, 3)		
(135, 6)		
(135, 33)		Variable Name
(135, 34)		
(135, 35)		variable.1
(135, 36)		1
(136, 1)		
(136, 2)		FALSE
(136, 3)		
(136, 6)		
(136, 33)		Dataset
(136, 34)		
(136, 35)		dataset.1
(136, 36)		2x2
(137, 2)		FALSE
(137, 3)		
(137, 6)		
(137, 33)		Imprecision
(137, 34)		
(137, 35)		sd.1
(137, 36)		1
(138, 6)		
(138, 33)		Variable Name
(138, 34)		
(138, 35)		variable.2
(138, 36)		1
(139, 6)		
(139, 33)		Dataset
(139, 34)		
(139, 35)		dataset.2
(139, 36)		2x2
(140, 2)		SurvivalAnalysis
(140, 3)		
(140, 6)		
(140, 33)		Imprecision
(140, 34)		
(140, 35)		sd.2
(140, 36)		1
(141, 2)		FALSE
(141, 3)		
(141, 6)		
(141, 33)		X Datapoints
(141, 34)		
(141, 35)		line.x
(141, 36)		2
(142, 2)		#NAME?
(142, 3)		
(142, 6)		
(142, 33)		Y Datapoints
(142, 34)		
(142, 35)		line.y
(142, 36)		2
(143, 2)		TRUE
(143, 3)		
(143, 6)		
(143, 33)		Fitted Y
(143, 34)		
(143, 35)		line.fitted
(143, 36)		2
(144, 2)		FALSE
(144, 3)		
(144, 6)		
(145, 2)		FALSE
(145, 3)		
(145, 6)		
(145, 32)		styles.passing.bablok
(145, 33)		Statistics
(145, 34)		MOD1.XLA!AH150:AK156
(146, 2)		FALSE
(146, 3)		
(146, 6)		
(146, 33)		Fitted Graph
(146, 34)		MOD1.XLA!AH142:AK144
(147, 2)		FALSE
(147, 3)		
(147, 6)		
(147, 33)		Dataset X
(147, 34)		MOD1.XLA!AH94:AK95
(148, 2)		FALSE
(148, 3)		kludge to delete a series (whoever did the excel macro language is a loser, why not a simple command like DELETE.SERIES ??)
(148, 6)		
(148, 33)		Dataset Y
(148, 34)		MOD1.XLA!AH96:AK97
(149, 2)		FALSE
(149, 3)		select the roc chart
(149, 6)		
(149, 33)		<No name defined>
(149, 34)		
(149, 35)		
(149, 36)		
(150, 2)		FALSE
(150, 3)		put in some dummy data to allow us to select the series
(150, 6)		
(150, 33)		Intercept Estimate
(150, 34)		
(150, 35)		estimate.1
(150, 36)		1
(151, 2)		FALSE
(151, 3)		select series 2
(151, 6)		
(151, 33)		CI of Intercept Minimum
(151, 34)		
(151, 35)		ci.minimum.1
(151, 36)		1
(152, 2)		FALSE
(152, 3)		delete it
(152, 6)		
(152, 33)		CI of Intercept Maximum
(152, 34)		
(152, 35)		ci.maximum.1
(152, 36)		1
(153, 2)		FALSE
(153, 3)		activate the results worksheet
(153, 6)		
(153, 33)		Slope Estimate
(153, 34)		
(153, 35)		estimate.2
(153, 36)		1
(154, 2)		FALSE
(154, 3)		
(154, 6)		
(154, 33)		CI of Slope Minimum
(154, 34)		
(154, 35)		ci.minimum.2
(154, 36)		1
(155, 2)		FALSE
(155, 3)		
(155, 6)		
(155, 33)		CI of Slope Maximum
(155, 34)		
(155, 35)		ci.maximum.2
(155, 36)		1
(156, 6)		
(157, 6)		
(157, 32)		styles.roc.analysis
(157, 33)		Statistics
(157, 34)		MOD1.XLA!AH163:AK167
(158, 1)		
(158, 2)		Two sample test macros
(158, 3)		
(158, 6)		
(158, 33)		Curve 1
(158, 34)		MOD1.XLA!AH172:AK179
(159, 6)		
(159, 33)		Curve 2
(159, 34)		MOD1.XLA!AH180:AK187
(160, 6)		
(160, 33)		Dataset 1
(160, 34)		MOD1.XLA!AH168:AK169
(161, 2)		GalenAndGambinoCutOffTest
(161, 3)		performs galen and gambino test
(161, 6)		
(161, 33)		Dataset 2
(161, 34)		MOD1.XLA!AH170:AK171
(162, 2)		FALSE
(162, 3)		
(162, 6)		
(162, 33)		<No name defined>
(162, 34)		
(162, 35)		
(162, 36)		
(163, 2)		FALSE
(163, 3)		
(163, 6)		
(163, 33)		W Statistic
(163, 34)		
(163, 35)		w
(163, 36)		2
(164, 2)		FALSE
(164, 3)		
(164, 6)		
(164, 33)		Standard Error
(164, 34)		
(164, 35)		se
(164, 36)		2
(165, 2)		#NAME?
(165, 3)		
(165, 6)		
(165, 33)		Z Statistic
(165, 34)		
(165, 35)		z
(165, 36)		2
(166, 2)		TRUE
(166, 3)		
(166, 6)		
(166, 33)		Two-Tailed p
(166, 34)		
(166, 35)		p
(166, 36)		2
(167, 2)		FALSE
(167, 3)		validate the data entered into the dialog box
(167, 6)		
(167, 33)		Variable Name
(167, 34)		
(167, 35)		variable.1
(167, 36)		1
(168, 2)		0
(168, 3)		
(168, 6)		
(168, 33)		Dataset
(168, 34)		
(168, 35)		dataset.1
(168, 36)		2
(169, 2)		FALSE
(169, 3)		
(169, 6)		
(169, 33)		Variable Names
(169, 34)		
(169, 35)		variable.2
(169, 36)		2
(170, 2)		FALSE
(170, 3)		
(170, 6)		
(170, 33)		Dataset
(170, 34)		
(170, 35)		dataset.2
(170, 36)		2x2
(171, 2)		FALSE
(171, 3)		
(171, 6)		
(171, 33)		Cut-Off Value
(171, 34)		
(171, 35)		cutoff.1
(171, 36)		2
(172, 2)		FALSE
(172, 3)		
(172, 6)		
(172, 33)		Sensitivity
(172, 34)		
(172, 35)		sensitivity.1
(172, 36)		2
(173, 2)		FALSE
(173, 3)		
(173, 6)		
(173, 33)		Specificity
(173, 34)		
(173, 35)		specificity.1
(173, 36)		2
(174, 2)		FALSE
(174, 3)		
(174, 6)		
(174, 33)		1-Specificity
(174, 34)		
(174, 35)		specificity1.1
(174, 36)		2
(175, 2)		FALSE
(175, 3)		
(175, 6)		
(175, 33)		True Positive
(175, 34)		
(175, 35)		tp.1
(175, 36)		2
(176, 6)		
(176, 33)		False Positive
(176, 34)		
(176, 35)		fp.1
(176, 36)		2
(177, 6)		
(177, 33)		True Negative
(177, 34)		
(177, 35)		tn.1
(177, 36)		2
(178, 2)		DemingRegressionTest
(178, 3)		performs deming regression
(178, 6)		
(178, 33)		False Negative
(178, 34)		
(178, 35)		fn.1
(178, 36)		2
(179, 2)		FALSE
(179, 3)		
(179, 6)		
(179, 33)		Cut-Off Value
(179, 34)		
(179, 35)		cutoff.2
(179, 36)		2
(180, 2)		FALSE
(180, 3)		
(180, 6)		
(180, 33)		Sensitivity
(180, 34)		
(180, 35)		sensitivity.2
(180, 36)		2
(181, 2)		FALSE
(181, 3)		
(181, 6)		
(181, 33)		Specificity
(181, 34)		
(181, 35)		specificity.2
(181, 36)		2
(182, 1)		
(182, 2)		TRUE
(182, 3)		display the dialog box if the selected areas are not measured in replicates
(182, 6)		
(182, 33)		1-Specificity
(182, 34)		
(182, 35)		specificity1.2
(182, 36)		2
(183, 1)		
(183, 2)		FALSE
(183, 3)		
(183, 6)		
(183, 33)		True Positive
(183, 34)		
(183, 35)		tp.2
(183, 36)		2
(184, 2)		FALSE
(184, 3)		
(184, 6)		
(184, 33)		False Positive
(184, 34)		
(184, 35)		fp.2
(184, 36)		2
(185, 2)		#REF!
(185, 3)		
(185, 6)		
(185, 33)		True Negative
(185, 34)		
(185, 35)		tn.2
(185, 36)		2
(186, 2)		#NAME?
(186, 3)		
(186, 6)		
(186, 33)		False Negative
(186, 34)		
(186, 35)		fn.2
(186, 36)		2
(187, 2)		TRUE
(187, 3)		
(187, 6)		
(188, 2)		FALSE
(188, 3)		validate the imprecisions entered in the dialog box
(188, 6)		
(188, 32)		styles.gng.cutoff
(188, 33)		Statistics
(188, 34)		MOD1.XLA!AH192:AK199
(189, 2)		0
(189, 3)		
(189, 6)		
(189, 33)		Dataset 1
(189, 34)		MOD1.XLA!AH94:AK95
(190, 1)		
(190, 2)		TRUE
(190, 3)		
(190, 6)		
(190, 33)		Dataset 2
(190, 34)		MOD1.XLA!AH96:AK97
(191, 1)		
(191, 2)		FALSE
(191, 3)		
(191, 6)		
(191, 33)		<No name defined>
(191, 34)		
(191, 35)		
(191, 36)		
(192, 2)		FALSE
(192, 3)		
(192, 6)		
(192, 33)		Cut-Off Value
(192, 34)		
(192, 35)		cutoff
(192, 36)		1
(193, 2)		0
(193, 3)		
(193, 6)		
(193, 33)		Contingency Table
(193, 34)		
(193, 35)		dataset.3
(193, 36)		2x2
(194, 1)		
(194, 2)		TRUE
(194, 3)		
(194, 6)		
(194, 33)		Sensitivity
(194, 34)		
(194, 35)		sensitivity
(194, 36)		1
(195, 1)		
(195, 2)		FALSE
(195, 3)		
(195, 6)		
(195, 33)		Specificity
(195, 34)		
(195, 35)		specificity
(195, 36)		1
(196, 1)		
(196, 2)		TRUE
(196, 3)		
(196, 6)		
(196, 33)		Predictive Positive Value
(196, 34)		
(196, 35)		predictive.positive
(196, 36)		1
(197, 2)		FALSE
(197, 3)		
(197, 6)		
(197, 33)		Predictive Negative Value
(197, 34)		
(197, 35)		predictive.negative
(197, 36)		1
(198, 2)		FALSE
(198, 3)		
(198, 6)		
(198, 33)		Efficiency
(198, 34)		
(198, 35)		efficiency
(198, 36)		1
(199, 2)		FALSE
(199, 3)		
(199, 6)		
(199, 33)		Prevalence
(199, 34)		
(199, 35)		prevalence
(199, 36)		1
(200, 2)		FALSE
(200, 3)		set any missing entries to #NUM
(200, 6)		
(201, 1)		
(201, 2)		FALSE
(201, 3)		
(201, 6)		
(201, 32)		styles.reverse.gng
(201, 33)		Statistics
(201, 34)		MOD1.XLA!AH202:AK209
(202, 2)		FALSE
(202, 3)		
(202, 6)		
(202, 33)		<No name defined>
(202, 34)		
(202, 35)		
(202, 36)		
(203, 1)		
(203, 2)		FALSE
(203, 3)		label the x and y axis on the line fit plot
(203, 6)		
(203, 33)		2x2 Contingency Table
(203, 34)		
(203, 35)		dataset
(203, 36)		2x2
(204, 1)		
(204, 2)		#NAME?
(204, 3)		
(204, 6)		
(204, 33)		Sensitivity
(204, 34)		
(204, 35)		sensitivity
(204, 36)		1
(205, 2)		FALSE
(205, 3)		
(205, 6)		
(205, 33)		Specificity
(205, 34)		
(205, 35)		specificity
(205, 36)		1
(206, 6)		
(206, 33)		Predictive Positive Value
(206, 34)		
(206, 35)		predictive.positive
(206, 36)		1
(207, 6)		
(207, 33)		Predictive Negative value
(207, 34)		
(207, 35)		predictive.negative
(207, 36)		1
(208, 2)		AltmanBlandTest
(208, 3)		performs altman and bland method comparison
(208, 33)		Efficiency
(208, 34)		
(208, 35)		efficiency
(208, 36)		1
(209, 2)		FALSE
(209, 3)		
(209, 33)		Prevalence
(209, 34)		
(209, 35)		prevalence
(209, 36)		1
(210, 2)		#NAME?
(210, 3)		
(211, 2)		TRUE
(211, 3)		
(212, 2)		FALSE
(212, 3)		
(213, 2)		FALSE
(213, 3)		
(214, 2)		FALSE
(214, 3)		
(215, 2)		FALSE
(215, 3)		
(216, 1)		
(216, 2)		FALSE
(216, 3)		
(217, 1)		
(217, 2)		FALSE
(217, 3)		
(218, 2)		FALSE
(218, 3)		
(221, 2)		ROCAnalysisTest
(221, 3)		performs ROC analysis
(222, 2)		FALSE
(222, 3)		
(223, 2)		#NAME?
(223, 3)		
(224, 2)		TRUE
(224, 3)		
(225, 2)		FALSE
(225, 3)		
(226, 2)		FALSE
(226, 3)		
(227, 2)		FALSE
(227, 3)		
(228, 2)		FALSE
(228, 3)		
(229, 2)		FALSE
(229, 3)		kludge to delete a series (whoever did the excel macro language is a loser, why not a simple command like DELETE.SERIES ??)
(230, 2)		FALSE
(230, 3)		select the roc chart
(231, 2)		FALSE
(231, 3)		put in some dummy data to allow us to select the series
(232, 2)		FALSE
(232, 3)		select series 2
(233, 2)		FALSE
(233, 3)		delete it
(234, 2)		FALSE
(234, 3)		activate the results worksheet
(235, 2)		FALSE
(235, 3)		
(236, 2)		FALSE
(236, 3)		
(239, 2)		PassingAndBablokMethodComparisonTest
(239, 3)		performs passing and Bablok method comparison
(240, 2)		FALSE
(240, 3)		
(241, 2)		#NAME?
(241, 3)		
(242, 2)		TRUE
(242, 3)		
(243, 2)		FALSE
(243, 3)		
(244, 2)		FALSE
(244, 3)		
(245, 2)		FALSE
(245, 3)		
(246, 2)		FALSE
(246, 3)		
(247, 1)		
(247, 2)		FALSE
(247, 3)		label the x and y axis on the line fit plot
(248, 1)		
(248, 2)		#NAME?
(248, 3)		
(249, 2)		FALSE
(249, 3)		
(254, 1)		
(254, 2)		Miscellaneous
(254, 3)		
(256, 2)		LabelGraph
(256, 3)		
(257, 2)		TRUE
(257, 3)		
(258, 2)		TRUE
(258, 3)		
(259, 2)		TRUE
(259, 3)		
(260, 2)		TRUE
(260, 3)		
(261, 2)		TRUE
(261, 3)		switch of error reporting
(262, 2)		TRUE
(262, 3)		select the chart specified
(263, 2)		TRUE
(263, 3)		name the x axis
(264, 2)		FALSE
(264, 3)		
(265, 2)		FALSE
(265, 3)		
(266, 2)		TRUE
(266, 3)		
(267, 2)		TRUE
(267, 3)		name the y axis
(268, 2)		FALSE
(268, 3)		
(269, 2)		FALSE
(269, 3)		
(270, 2)		TRUE
(270, 3)		
(271, 2)		TRUE
(271, 3)		re-activate the results worksheet
(272, 1)		
(272, 2)		TRUE
(272, 3)		
(274, 2)		ScaleGraphAxis
(274, 3)		
(275, 2)		TRUE
(275, 3)		
(276, 2)		TRUE
(276, 3)		
(277, 2)		TRUE
(277, 3)		
(278, 2)		TRUE
(278, 3)		
(279, 2)		TRUE
(279, 3)		
(280, 2)		FALSE
(280, 3)		select the chart specified
(281, 2)		TRUE
(281, 3)		name the x axis
(282, 2)		FALSE
(282, 3)		select x axis
(283, 2)		FALSE
(283, 3)		scale the axis to the minimum and maximum
(284, 2)		TRUE
(284, 3)		
(285, 2)		TRUE
(285, 3)		name the y axis
(286, 2)		FALSE
(286, 3)		select y axis
(287, 2)		FALSE
(287, 3)		scale the axis to the minimum and maximum
(288, 2)		TRUE
(288, 3)		
(289, 2)		TRUE
(289, 3)		re-activate the results worksheet
(290, 1)		
(290, 2)		TRUE
(290, 3)		
(498, 1)		
(734, 1)		
(738, 1)		
(739, 1)		
(740, 1)		
(741, 1)		
(742, 1)		
(743, 1)		
(744, 1)		
(745, 1)		
(746, 1)		
(747, 1)		
(748, 1)		
(749, 1)		
(750, 1)		
(751, 1)		
(752, 1)		
(753, 1)