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)		
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(1, 30)		
(1, 31)		
(1, 37)		
(2, 1)		labels
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(2, 5)		
(2, 6)		labels
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(2, 18)		
(2, 19)		
(2, 20)		labels
(2, 21)		tool id
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(2, 24)		enabled
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(2, 30)		
(2, 31)		
(2, 33)		listbox item
(2, 34)		reference
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(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
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(2, 49)		on/off
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(2, 51)		toolbar
(3, 1)		
(4, 1)		
(4, 2)		Basic Statistics Module
(4, 6)		
(4, 20)		
(5, 1)		
(5, 2)		  (c) 1993, Diagnostic Development Unit
(5, 6)		dialog.significant.result.single.mean
(5, 7)		
(5, 8)		
(5, 9)		
(5, 10)		411
(5, 11)		159
(5, 12)		Significance - Single Mean
(5, 13)		3
(5, 16)		__LongName
(5, 17)		Astute vsn 1.5
(5, 20)		toolbar
(5, 21)		1
(5, 22)		 'FALSE\BASE.XLA'!DescriptiveTes
(5, 23)		FALSE
(5, 24)		TRUE
(5, 25)		Picture 23
(5, 26)		Single descriptive statistics
(5, 27)		
(5, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(5, 29)		Single Descriptive Statistics
(5, 32)		styles.single.descriptive
(5, 33)		Descriptive
(5, 34)		BASE.XLA!AH9:AK25
(5, 39)		module.description
(5, 40)		
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(5, 44)		&Descriptive Statistics
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(5, 48)		
(5, 49)		
(5, 50)		
(5, 51)		
(5, 52)		
(6, 1)		
(6, 2)		                The University of Leeds
(6, 6)		
(6, 7)		5
(6, 8)		14
(6, 9)		23
(6, 10)		
(6, 11)		
(6, 12)		Mean and Null Hypothesis:
(6, 13)		
(6, 16)		initialised
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(6, 20)		
(6, 21)		2
(6, 22)		 'C:\DEV\STATS\XLBUILD\BASE.XLA'!DescriptiveMultipleTes
(6, 23)		FALSE
(6, 24)		TRUE
(6, 25)		Picture 15
(6, 26)		Multiple descriptive statistics
(6, 27)		
(6, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(6, 29)		Multiple Descriptive Statistics
(6, 33)		Frequency
(6, 34)		BASE.XLA!AH26:AK31
(6, 40)		
(6, 41)		&Statistics
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(6, 46)		
(6, 47)		
(6, 48)		
(6, 49)		
(6, 50)		
(6, 51)		4
(6, 52)		Statistics
(7, 1)		
(7, 2)		                All Rights Reserved
(7, 6)		mean
(7, 7)		5
(7, 8)		14
(7, 9)		10
(7, 10)		
(7, 11)		
(7, 12)		Difference between
(7, 13)		
(7, 16)		string.licence.token
(7, 17)		BASE
(7, 20)		
(7, 21)		4
(7, 22)		 'FALSE\BASE.XLA'!DisplayTransformMen
(7, 23)		FALSE
(7, 24)		TRUE
(7, 25)		Picture 13
(7, 26)		Display transform menu
(7, 27)		
(7, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!102
(7, 29)		Transform
(7, 33)		Dataset
(7, 34)		BASE.XLA!AH32:AK33
(7, 39)		string.mcnemar
(7, 40)		McNemar
(7, 41)		&Statistics
(7, 42)		TRUE
(7, 43)		
(7, 44)		&McNemar
(7, 45)		McNemarTest
(7, 46)		
(7, 47)		McNemar test for the significance of changes
(7, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(7, 49)		
(7, 50)		FALSE
(7, 51)		5
(7, 52)		Statistics
(8, 1)		
(8, 2)		                Simon Huntington
(8, 6)		
(8, 7)		8
(8, 8)		221
(8, 9)		20
(8, 10)		70
(8, 11)		
(8, 12)		
(8, 13)		
(8, 16)		string.loading
(8, 17)		Loading base module (vsn 1.50)...
(8, 20)		
(8, 21)		22
(8, 22)		 'C:\DEV\STATS\XLBUILD\MODULES\BASE.XLA'!DisplaySampleSizeMen
(8, 23)		FALSE
(8, 24)		TRUE
(8, 25)		Picture 26
(8, 26)		Display sample size menu
(8, 27)		
(8, 28)		#NAME?
(8, 29)		Sample Size
(8, 33)		<No name defined>
(8, 34)		
(8, 35)		
(8, 36)		
(8, 39)		string.wilcoxon
(8, 40)		Wilcoxon Signed Rank
(8, 41)		&Statistics
(8, 42)		FALSE
(8, 43)		
(8, 44)		&Wilcoxon Signed Rank
(8, 45)		WilcoxonTest
(8, 46)		
(8, 47)		Wilcoxon matched-pairs signed-ranks test
(8, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(8, 49)		
(8, 50)		FALSE
(8, 51)		6
(8, 52)		Statistics
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(9, 6)		sd
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(9, 9)		47
(9, 10)		
(9, 11)		
(9, 12)		SD:
(9, 13)		
(9, 16)		string.invalid.astute.version
(9, 17)		Base module requires Astute version 1.50 or later.
(9, 20)		
(9, 21)		5
(9, 22)		 'FALSE\BASE.XLA'!McNemarTes
(9, 23)		FALSE
(9, 24)		TRUE
(9, 25)		Picture 17
(9, 26)		McNemar test for the significance of changes
(9, 27)		
(9, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(9, 29)		McNemar
(9, 33)		N
(9, 34)		
(9, 35)		n
(9, 36)		1
(9, 39)		string.fisher
(9, 40)		Fisher Exact Probability
(9, 41)		&Statistics
(9, 42)		FALSE
(9, 43)		
(9, 44)		Fisher &Exact Probability
(9, 45)		FisherTest
(9, 46)		
(9, 47)		Fisher exact probability test
(9, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(9, 49)		
(9, 50)		FALSE
(9, 51)		7
(9, 52)		Statistics
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(10, 2)		
(10, 6)		
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(10, 10)		70
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(10, 12)		
(10, 13)		
(10, 16)		string.module.not.initialised
(10, 17)		This module is not operational.
(10, 20)		
(10, 21)		6
(10, 22)		 'FALSE\BASE.XLA'!WilcoxonTes
(10, 23)		FALSE
(10, 24)		TRUE
(10, 25)		Picture 1
(10, 26)		Wilcoxon matched-pairs signed-ranks test
(10, 27)		
(10, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(10, 29)		Wilcoxon Signed-Ranks
(10, 33)		Sum
(10, 34)		
(10, 35)		sum
(10, 36)		1
(10, 39)		string.chi.square
(10, 40)		Chi-Square
(10, 41)		&Statistics
(10, 42)		FALSE
(10, 43)		
(10, 44)		Chi-S&quare
(10, 45)		ChiSquareTest
(10, 46)		
(10, 47)		Chi-Square test for k independent samples
(10, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(10, 49)		
(10, 50)		FALSE
(10, 51)		8
(10, 52)		Statistics
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(11, 6)		u
(11, 7)		5
(11, 8)		165
(11, 9)		77
(11, 10)		
(11, 11)		
(11, 12)		Power:
(11, 13)		
(11, 16)		string.descriptive.invalid
(11, 17)		Invalid bin criteria.  Please select upto 3 bin criteria.
(11, 20)		
(11, 21)		7
(11, 22)		 'FALSE\BASE.XLA'!FisherTes
(11, 23)		FALSE
(11, 24)		TRUE
(11, 25)		Picture 20
(11, 26)		Fisher exact probability test
(11, 27)		
(11, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(11, 29)		Fisher Exact Probability
(11, 33)		Mean
(11, 34)		
(11, 35)		mean
(11, 36)		1
(11, 39)		string.mann.whitney
(11, 40)		Wilcoxon-Mann-Whitney U
(11, 41)		&Statistics
(11, 42)		FALSE
(11, 43)		
(11, 44)		Wilco&xon-Mann-Whitney U
(11, 45)		MannWhitneyUTest
(11, 46)		
(11, 47)		Wilcoxon-Mann-Whitney U test
(11, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(11, 49)		
(11, 50)		FALSE
(11, 51)		9
(11, 52)		Statistics
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(12, 7)		8
(12, 8)		221
(12, 9)		74
(12, 10)		38
(12, 11)		
(12, 12)		
(12, 13)		
(12, 16)		string.altttest.invalid
(12, 17)		You must specify the N, mean and standard deviation of the second dataset.
(12, 20)		
(12, 21)		8
(12, 22)		 'FALSE\BASE.XLA'!ChiSquareTes
(12, 23)		FALSE
(12, 24)		TRUE
(12, 25)		Picture 2
(12, 26)		Chi-Square test for k independent samples
(12, 27)		
(12, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(12, 29)		Chi-Square
(12, 33)		CI of Mean Minimum
(12, 34)		
(12, 35)		ci.minimum
(12, 36)		1
(12, 40)		
(12, 41)		&Statistics
(12, 42)		TRUE
(12, 43)		
(12, 44)		&Correlation
(12, 45)		
(12, 46)		
(12, 47)		
(12, 48)		
(12, 49)		
(12, 50)		
(12, 51)		
(12, 52)		
(13, 6)		
(13, 7)		5
(13, 8)		261
(13, 9)		77
(13, 10)		
(13, 11)		
(13, 12)		%
(13, 13)		
(13, 16)		string.altttest.invalid.n
(13, 17)		N must be greater than 0.
(13, 20)		
(13, 21)		9
(13, 22)		 'FALSE\BASE.XLA'!MannWhitneyUTes
(13, 23)		FALSE
(13, 24)		TRUE
(13, 25)		Picture 3
(13, 26)		Wilcoxon-Mann-Whitney U test
(13, 27)		
(13, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(13, 29)		Wilcoxon-Mann-Whitney U
(13, 33)		CI of Mean Maximum
(13, 34)		
(13, 35)		ci.maximum
(13, 36)		1
(13, 40)		
(13, 41)		&Statistics
(13, 42)		FALSE
(13, 43)		
(13, 44)		Re&gression
(13, 45)		
(13, 46)		
(13, 47)		
(13, 48)		
(13, 49)		
(13, 50)		
(13, 51)		
(13, 52)		
(14, 6)		v
(14, 7)		5
(14, 8)		72
(14, 9)		101
(14, 10)		
(14, 11)		
(14, 12)		Significance Level:
(14, 13)		
(14, 16)		string.altttest.invalid.sd
(14, 17)		SD must be greater than or equal to 0.
(14, 20)		
(14, 21)		10
(14, 22)		 'FALSE\BASE.XLA'!PearsonTes
(14, 23)		FALSE
(14, 24)		TRUE
(14, 25)		Picture 4
(14, 26)		Pearson correlation coefficient
(14, 27)		
(14, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(14, 29)		Pearson Correlation
(14, 33)		Variance
(14, 34)		
(14, 35)		variance
(14, 36)		1
(14, 40)		
(14, 41)		&Statistics
(14, 42)		TRUE
(14, 43)		
(14, 44)		&T-Test
(14, 45)		
(14, 46)		
(14, 47)		
(14, 48)		
(14, 49)		
(14, 50)		
(14, 51)		
(14, 52)		
(15, 6)		
(15, 7)		8
(15, 8)		221
(15, 9)		98
(15, 10)		38
(15, 11)		
(15, 12)		
(15, 13)		
(15, 16)		string.descriptive.sequence.invalid
(15, 17)		Sequence must contain the same number of rows or columns as the range.
(15, 20)		
(15, 21)		11
(15, 22)		 'FALSE\BASE.XLA'!SpearmanTes
(15, 23)		FALSE
(15, 24)		TRUE
(15, 25)		Picture 5
(15, 26)		Spearman rank-order correlation coefficient
(15, 27)		
(15, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!101
(15, 29)		Spearman Rank Correlation
(15, 33)		Standard Deviation
(15, 34)		
(15, 35)		sd
(15, 36)		1
(15, 39)		string.ftest
(15, 40)		F-Test
(15, 41)		&Statistics
(15, 42)		FALSE
(15, 43)		
(15, 44)		&F-Test
(15, 45)		FTest
(15, 46)		
(15, 47)		Two sample f-test
(15, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!101
(15, 49)		
(15, 50)		FALSE
(15, 51)		17
(15, 52)		Statistics
(16, 6)		
(16, 7)		5
(16, 8)		261
(16, 9)		101
(16, 10)		
(16, 11)		
(16, 12)		%
(16, 13)		
(16, 16)		string.invalid.power
(16, 17)		Power must be a number between 0 and 100.
(16, 20)		
(16, 21)		12
(16, 22)		 'FALSE\BASE.XLA'!KendallTes
(16, 23)		FALSE
(16, 24)		TRUE
(16, 25)		Picture 6
(16, 26)		Kendall rank-order correlation coefficient
(16, 27)		
(16, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!101
(16, 29)		Kendall Rank Correlation
(16, 33)		Standard Error
(16, 34)		
(16, 35)		se
(16, 36)		1
(16, 40)		
(16, 41)		&Statistics
(16, 42)		TRUE
(16, 43)		
(16, 44)		&Analysis of Variance
(16, 45)		
(16, 46)		
(16, 47)		
(16, 48)		
(16, 49)		
(16, 50)		
(16, 51)		
(16, 52)		
(17, 6)		
(17, 7)		101
(17, 8)		310
(17, 9)		7
(17, 10)		88
(17, 11)		
(17, 12)		Compute
(17, 13)		
(17, 16)		string.invalid.significance.level
(17, 17)		Significance Level must be a number between 0 and 100.
(17, 20)		
(17, 21)		13
(17, 22)		 'FALSE\BASE.XLA'!LinearRegressionTes
(17, 23)		FALSE
(17, 24)		TRUE
(17, 25)		Picture 12
(17, 26)		Simple linear regression
(17, 27)		
(17, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!101
(17, 29)		Linear Regression
(17, 33)		Skewness
(17, 34)		
(17, 35)		skewness
(17, 36)		1
(17, 39)		string.single.descriptive
(17, 40)		Single Descriptive
(17, 41)		&Descriptive Statistics
(17, 42)		FALSE
(17, 43)		
(17, 44)		&Single
(17, 45)		DescriptiveTest
(17, 46)		
(17, 47)		Single descriptive statistics
(17, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(17, 49)		
(17, 50)		FALSE
(17, 51)		1
(17, 52)		Statistics
(18, 1)		
(18, 2)		Initialise
(18, 3)		initialises the module and the xla if not loaded
(18, 6)		
(18, 7)		2
(18, 8)		
(18, 9)		
(18, 10)		88
(18, 11)		
(18, 12)		Close
(18, 13)		
(18, 20)		
(18, 21)		3
(18, 22)		 'C:\DEV\STATS\XLBUILD\MODULES\BASE.XLA'!EqualVarianceTTes
(18, 23)		FALSE
(18, 24)		TRUE
(18, 25)		Picture 29
(18, 26)		Two sample t-test assuming equal variances
(18, 27)		
(18, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!101
(18, 29)		Equal Variance t-Test
(18, 33)		Kurtosis
(18, 34)		
(18, 35)		kurtosis
(18, 36)		1
(18, 39)		string.multiple.descriptive
(18, 40)		Multiple Descriptive
(18, 41)		&Descriptive Statistics
(18, 42)		FALSE
(18, 43)		
(18, 44)		&Multiple
(18, 45)		DescriptiveMultipleTest
(18, 46)		
(18, 47)		Multiple descriptive statistics
(18, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(18, 49)		
(18, 50)		FALSE
(18, 51)		2
(18, 52)		Statistics
(19, 1)		
(19, 2)		TRUE
(19, 3)		
(19, 6)		
(19, 7)		24
(19, 8)		
(19, 9)		62
(19, 10)		88
(19, 11)		
(19, 12)		
(19, 13)		
(19, 20)		
(19, 21)		14
(19, 22)		 'C:\DEV\STATS\XLBUILD\MODULES\BASE.XLA'!UnequalVarianceTTes
(19, 23)		FALSE
(19, 24)		TRUE
(19, 25)		Picture 28
(19, 26)		Two sample t-test assuming unequal variances
(19, 27)		
(19, 28)		#NAME?
(19, 29)		Unequal Variance t-Test
(19, 33)		Mode
(19, 34)		
(19, 35)		mode
(19, 36)		1
(19, 39)		string.sequenced.descriptive
(19, 40)		Sequenced Descriptive
(19, 41)		&Descriptive Statistics
(19, 42)		FALSE
(19, 43)		
(19, 44)		Se&quenced
(19, 45)		DescriptiveSequencedTest
(19, 46)		
(19, 47)		Sequenced descriptive statistics
(19, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(19, 49)		
(19, 50)		FALSE
(19, 51)		
(19, 52)		Statistics
(20, 2)		TRUE
(20, 3)		load astute4.xla or astute5.xla
(20, 6)		
(20, 7)		5
(20, 8)		10
(20, 9)		134
(20, 10)		
(20, 11)		
(20, 12)		Minimum sample size =
(20, 13)		
(20, 20)		
(20, 21)		15
(20, 22)		 'FALSE\BASE.XLA'!PairedTTes
(20, 23)		FALSE
(20, 24)		TRUE
(20, 25)		Picture 8
(20, 26)		Two sample paired t-test
(20, 27)		
(20, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!101
(20, 29)		Paired t-test
(20, 33)		Minimum
(20, 34)		
(20, 35)		minimum
(20, 36)		1
(20, 39)		string.trn.log10
(20, 40)		Log 10
(20, 41)		T&ransform
(20, 42)		FALSE
(20, 43)		
(20, 44)		L&og 10
(20, 45)		Log10Transform
(20, 46)		
(20, 47)		Base-10 logarithm transformation
(20, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!102
(20, 49)		
(20, 50)		FALSE
(20, 51)		
(20, 52)		
(21, 1)		astute.path
(21, 2)		 C:\DEV\ASTUTE\VSN.
(21, 3)		
(21, 6)		result
(21, 7)		5
(21, 8)		186
(21, 9)		134
(21, 10)		
(21, 11)		
(21, 12)		
(21, 13)		
(21, 20)		
(21, 21)		17
(21, 22)		 'FALSE\BASE.XLA'!FTes
(21, 23)		FALSE
(21, 24)		TRUE
(21, 25)		Picture 9
(21, 26)		Two sample f-test
(21, 27)		
(21, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!101
(21, 29)		F-test
(21, 33)		Lower Percentile
(21, 34)		
(21, 35)		lower.pcntl
(21, 36)		1
(21, 39)		string.trn.antilog10
(21, 40)		AntiLog 10
(21, 41)		T&ransform
(21, 42)		FALSE
(21, 43)		
(21, 44)		&AntiLog 10
(21, 45)		AntiLogTransform
(21, 46)		
(21, 47)		AntiLog10 transformation
(21, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!102
(21, 49)		
(21, 50)		FALSE
(21, 51)		
(21, 52)		
(22, 1)		
(22, 2)		TRUE
(22, 3)		
(22, 6)		
(22, 20)		
(22, 21)		18
(22, 22)		 'FALSE\BASE.XLA'!OneWayAnovaTes
(22, 23)		FALSE
(22, 24)		TRUE
(22, 25)		Picture 10
(22, 26)		One-way analysis of variance
(22, 27)		
(22, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!101
(22, 29)		One-Way ANOVA
(22, 33)		Median
(22, 34)		
(22, 35)		median
(22, 36)		1
(22, 39)		string.trn.ln
(22, 40)		Ln
(22, 41)		T&ransform
(22, 42)		FALSE
(22, 43)		
(22, 44)		&Ln
(22, 45)		LnTransform
(22, 46)		
(22, 47)		Natural logarithm transformation
(22, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!102
(22, 49)		
(22, 50)		FALSE
(22, 51)		
(22, 52)		
(23, 1)		
(23, 2)		#VALUE!
(23, 3)		
(23, 6)		
(23, 20)		
(23, 21)		19
(23, 22)		 'FALSE\BASE.XLA'!TwoWayAnovaTes
(23, 23)		FALSE
(23, 24)		TRUE
(23, 25)		Picture 11
(23, 26)		Two-way analysis of variance
(23, 27)		
(23, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!101
(23, 29)		Two-Way ANOVA
(23, 33)		Upper Percentile
(23, 34)		
(23, 35)		upper.pcntl
(23, 36)		1
(23, 39)		string.trn.exp
(23, 40)		Exp
(23, 41)		T&ransform
(23, 42)		FALSE
(23, 43)		
(23, 44)		&Exp
(23, 45)		ExpTransform
(23, 46)		
(23, 47)		e raised to the power of x transformation
(23, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!102
(23, 49)		
(23, 50)		FALSE
(23, 51)		
(23, 52)		
(24, 1)		
(24, 2)		FALSE
(24, 3)		initialise the main astute module, we should get called back with registertests
(24, 6)		dialog.significant.result.single.proportion
(24, 7)		
(24, 8)		
(24, 9)		
(24, 10)		411
(24, 11)		159
(24, 12)		Significance - Single Proportion
(24, 13)		2
(24, 20)		
(24, 21)		20
(24, 22)		 'FALSE\BASE.XLA'!KruskalWallisTes
(24, 23)		FALSE
(24, 24)		TRUE
(24, 25)		Picture 18
(24, 26)		Kruskal-Wallis one-way analysis of variance
(24, 27)		
(24, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!101
(24, 29)		Kruskal-Wallis  ANOVA
(24, 33)		Maximum
(24, 34)		
(24, 35)		maximum
(24, 36)		1
(24, 39)		string.trn.reciprocal
(24, 40)		Reciprocal
(24, 41)		T&ransform
(24, 42)		FALSE
(24, 43)		
(24, 44)		&Reciprocal
(24, 45)		ReciprocalTransform
(24, 46)		
(24, 47)		1/x transformation
(24, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!102
(24, 49)		
(24, 50)		FALSE
(24, 51)		
(24, 52)		
(25, 1)		
(25, 2)		FALSE
(25, 3)		
(25, 6)		
(25, 7)		5
(25, 8)		134
(25, 9)		13
(25, 10)		
(25, 11)		
(25, 12)		Proportion:
(25, 13)		
(25, 20)		
(25, 21)		21
(25, 22)		 'C:\DEV\STATS\XLBUILD\BASE.XLA'!FriedmanTes
(25, 23)		FALSE
(25, 24)		TRUE
(25, 25)		Picture 19
(25, 26)		Friedman two-way analysis of variance
(25, 27)		
(25, 28)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!101
(25, 29)		Friedman ANOVA
(25, 33)		Bin Minimum
(25, 34)		
(25, 35)		frequency.minimum
(25, 36)		2
(25, 39)		string.trn.square.root
(25, 40)		Square Root
(25, 41)		T&ransform
(25, 42)		FALSE
(25, 43)		
(25, 44)		S&quare Root
(25, 45)		SqrtTransform
(25, 46)		
(25, 47)		Square-root of x transformation
(25, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!102
(25, 49)		
(25, 50)		FALSE
(25, 51)		
(25, 52)		
(26, 1)		
(26, 2)		FALSE
(26, 3)		terminal error, module not initialised
(26, 6)		mean
(26, 7)		8
(26, 8)		221
(26, 9)		10
(26, 10)		70
(26, 11)		
(26, 12)		
(26, 13)		
(26, 20)		
(26, 21)		32
(26, 22)		 'C:\DEV\STATS\XLBUILD\MODULES\BASE.XLA'!MultipleLinearRegressio
(26, 23)		FALSE
(26, 24)		TRUE
(26, 25)		Picture 24
(26, 26)		Multiple linear regression
(26, 27)		
(26, 28)		#NAME?
(26, 29)		Multiple Linear Regression
(26, 33)		Bin Midpoint
(26, 34)		
(26, 35)		frequency.midpoint
(26, 36)		2
(26, 39)		string.trn.square
(26, 40)		Square
(26, 41)		T&ransform
(26, 42)		FALSE
(26, 43)		
(26, 44)		&Square
(26, 45)		SquareTransform
(26, 46)		
(26, 47)		x squared transformation
(26, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!102
(26, 49)		
(26, 50)		FALSE
(26, 51)		
(26, 52)		
(27, 1)		
(27, 2)		FALSE
(27, 3)		
(27, 6)		
(27, 7)		5
(27, 8)		95
(27, 9)		37
(27, 10)		
(27, 11)		
(27, 12)		Null Hypothesis:
(27, 13)		
(27, 20)		
(27, 22)		FALSE
(27, 28)		FALSE
(27, 33)		Bin Maximum
(27, 34)		
(27, 35)		frequency.maximum
(27, 36)		2
(27, 39)		string.trn.cube
(27, 40)		Cube
(27, 41)		T&ransform
(27, 42)		FALSE
(27, 43)		
(27, 44)		&Cube
(27, 45)		CubeTransform
(27, 46)		
(27, 47)		x cubed transformation
(27, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!102
(27, 49)		
(27, 50)		FALSE
(27, 51)		
(27, 52)		
(28, 2)		FALSE
(28, 3)		return initialised state
(28, 6)		nullh
(28, 7)		8
(28, 8)		221
(28, 9)		34
(28, 10)		70
(28, 11)		
(28, 12)		
(28, 13)		
(28, 20)		
(28, 33)		Frequency
(28, 34)		
(28, 35)		frequency
(28, 36)		2
(28, 39)		string.trn.z
(28, 40)		z
(28, 41)		T&ransform
(28, 42)		FALSE
(28, 43)		
(28, 44)		&z Transform
(28, 45)		ZTransform
(28, 46)		
(28, 47)		Z transformation
(28, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!102
(28, 49)		
(28, 50)		FALSE
(28, 51)		
(28, 52)		
(29, 6)		
(29, 7)		5
(29, 8)		165
(29, 9)		66
(29, 10)		
(29, 11)		
(29, 12)		Power:
(29, 13)		
(29, 20)		
(29, 33)		Cumulative Frequency
(29, 34)		
(29, 35)		frequency.cumulative
(29, 36)		2
(29, 39)		string.trn.formula
(29, 40)		Formula
(29, 41)		T&ransform
(29, 42)		TRUE
(29, 43)		
(29, 44)		&Formula
(29, 45)		FormulaTransform
(29, 46)		
(29, 47)		User formula transformation
(29, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!102
(29, 49)		
(29, 50)		FALSE
(29, 51)		
(29, 52)		
(30, 2)		RegisterTests
(30, 3)		adds the tools to the palette and initialise help
(30, 6)		u
(30, 7)		8
(30, 8)		221
(30, 9)		64
(30, 10)		38
(30, 11)		
(30, 12)		
(30, 13)		
(30, 20)		
(30, 33)		Modal Class
(30, 34)		
(30, 35)		modal.class
(30, 36)		1
(30, 40)		
(30, 41)		&Statistics
(30, 42)		FALSE
(30, 43)		&McNemar
(30, 44)		Sample Si&ze
(30, 45)		
(30, 46)		
(30, 47)		
(30, 48)		
(30, 49)		
(30, 50)		
(30, 51)		22
(30, 52)		Statistics
(31, 2)		FALSE
(31, 3)		return logical
(31, 6)		
(31, 7)		5
(31, 8)		261
(31, 9)		66
(31, 10)		
(31, 11)		
(31, 12)		%
(31, 13)		
(31, 33)		Variable Names
(31, 34)		
(31, 35)		variable
(31, 36)		2
(31, 40)		sig
(31, 41)		Sample Si&ze
(31, 42)		FALSE
(31, 43)		
(31, 44)		&Significance
(31, 45)		None
(31, 46)		
(31, 47)		Sample size required to demonstrate a significant difference
(31, 48)		#NAME?
(31, 49)		
(31, 50)		FALSE
(31, 51)		
(31, 52)		
(32, 2)		FALSE
(32, 3)		
(32, 6)		
(32, 7)		5
(32, 8)		72
(32, 9)		90
(32, 10)		
(32, 11)		
(32, 12)		Significance Level:
(32, 13)		
(32, 33)		Dataset
(32, 34)		
(32, 35)		dataset
(32, 36)		2x2
(32, 40)		sigsm
(32, 41)		Sample Si&ze
(32, 42)		FALSE
(32, 43)		
(32, 44)		     Single Mean
(32, 45)		SignificiantResultSingleMean
(32, 46)		
(32, 47)		Single mean
(32, 48)		#NAME?
(32, 49)		
(32, 50)		FALSE
(32, 51)		
(32, 52)		
(33, 2)		2
(33, 3)		disable user interaction and error reporting
(33, 6)		v
(33, 7)		8
(33, 8)		221
(33, 9)		88
(33, 10)		38
(33, 11)		
(33, 12)		
(33, 13)		
(33, 40)		sigsp
(33, 41)		Sample Si&ze
(33, 42)		FALSE
(33, 43)		
(33, 44)		     Single Proportion
(33, 45)		SignificiantResultSingleProportion
(33, 46)		
(33, 47)		Single proportion
(33, 48)		#NAME?
(33, 49)		
(33, 50)		FALSE
(33, 51)		
(33, 52)		
(34, 2)		FALSE
(34, 3)		get the version of excel in use
(34, 6)		
(34, 7)		5
(34, 8)		261
(34, 9)		90
(34, 10)		
(34, 11)		
(34, 12)		%
(34, 13)		
(34, 32)		styles.multiple.descriptive
(34, 33)		Descriptive
(34, 34)		BASE.XLA!AH38:AK54
(34, 40)		sigtm
(34, 41)		Sample Si&ze
(34, 42)		FALSE
(34, 43)		
(34, 44)		     Two Means
(34, 45)		SignificantResultComparisonTwoMeans
(34, 46)		
(34, 47)		Comparison of two means
(34, 48)		#NAME?
(34, 49)		
(34, 50)		FALSE
(34, 51)		
(34, 52)		
(35, 1)		path
(35, 2)		 C:\DEV\STATS\XLBUILD\MODULE
(35, 3)		read the pathname of the xla
(35, 6)		
(35, 7)		101
(35, 8)		310
(35, 9)		7
(35, 10)		88
(35, 11)		
(35, 12)		Compute
(35, 13)		
(35, 33)		Errorbar Plot
(35, 34)		BASE.XLA!AH55:AK57
(35, 40)		sigtp
(35, 41)		Sample Si&ze
(35, 42)		FALSE
(35, 43)		
(35, 44)		     Two Proportions
(35, 45)		SignificiantResultComparisonTwoProportions
(35, 46)		
(35, 47)		Comparison of two proportions
(35, 48)		#NAME?
(35, 49)		
(35, 50)		FALSE
(35, 51)		
(35, 52)		
(36, 2)		FALSE
(36, 3)		build the filename for help
(36, 6)		
(36, 7)		2
(36, 8)		
(36, 9)		
(36, 10)		88
(36, 11)		
(36, 12)		Close
(36, 13)		
(36, 33)		Dataset
(36, 34)		BASE.XLA!AH58:AK59
(36, 40)		sigcc
(36, 41)		Sample Si&ze
(36, 42)		FALSE
(36, 43)		
(36, 44)		     Case-Control Study
(36, 45)		SignificiantResultCaseControlStudy
(36, 46)		
(36, 47)		Case-control study
(36, 48)		#NAME?
(36, 49)		
(36, 50)		FALSE
(36, 51)		
(36, 52)		
(37, 2)		FALSE
(37, 3)		build the names of the macros on the astute sheet to use
(37, 6)		
(37, 7)		24
(37, 8)		
(37, 9)		62
(37, 10)		88
(37, 11)		
(37, 12)		
(37, 13)		
(37, 33)		<No name defined>
(37, 34)		
(37, 35)		
(37, 36)		
(37, 40)		prec
(37, 41)		Sample Si&ze
(37, 42)		TRUE
(37, 43)		
(37, 44)		&Precision
(37, 45)		None
(37, 46)		
(37, 47)		Sample size required to estimate a quantity of interest with a specified precision
(37, 48)		#NAME?
(37, 49)		
(37, 50)		FALSE
(37, 51)		
(37, 52)		
(38, 2)		FALSE
(38, 3)		
(38, 6)		
(38, 7)		5
(38, 8)		10
(38, 9)		134
(38, 10)		
(38, 11)		
(38, 12)		Minimum sample size =
(38, 13)		
(38, 33)		N
(38, 34)		
(38, 35)		n
(38, 36)		2
(38, 40)		precsm
(38, 41)		Sample Si&ze
(38, 42)		FALSE
(38, 43)		
(38, 44)		     Single Mean�
(38, 45)		PrecisionSingleMean
(38, 46)		
(38, 47)		Single mean
(38, 48)		#NAME?
(38, 49)		
(38, 50)		FALSE
(38, 51)		
(38, 52)		
(39, 2)		FALSE
(39, 3)		
(39, 6)		result
(39, 7)		5
(39, 8)		186
(39, 9)		134
(39, 10)		
(39, 11)		
(39, 12)		
(39, 13)		
(39, 33)		Sum
(39, 34)		
(39, 35)		sum
(39, 36)		2
(39, 40)		precsp
(39, 41)		Sample Si&ze
(39, 42)		FALSE
(39, 43)		
(39, 44)		     Single Proportion�
(39, 45)		PrecisionSingleProportion
(39, 46)		
(39, 47)		Single proportion
(39, 48)		#NAME?
(39, 49)		
(39, 50)		FALSE
(39, 51)		
(39, 52)		
(40, 2)		TRUE
(40, 3)		
(40, 6)		
(40, 33)		Mean
(40, 34)		
(40, 35)		mean
(40, 36)		2
(40, 40)		prectm
(40, 41)		Sample Si&ze
(40, 42)		FALSE
(40, 43)		
(40, 44)		     Two Means�
(40, 45)		PrecisionDifferenceBetweenTwoMeans
(40, 46)		
(40, 47)		Difference between two means
(40, 48)		#NAME?
(40, 49)		
(40, 50)		FALSE
(40, 51)		
(40, 52)		
(41, 2)		FALSE
(41, 3)		if there is no version number it must be version 1.0
(41, 6)		
(41, 33)		CI of Mean Minimum
(41, 34)		
(41, 35)		ci.minimum
(41, 36)		2
(41, 40)		prectp
(41, 41)		Sample Si&ze
(41, 42)		FALSE
(41, 43)		
(41, 44)		     Two Proportions�
(41, 45)		PrecisionDifferenceBetweenTwoProportions
(41, 46)		
(41, 47)		Difference between two proportions
(41, 48)		#NAME?
(41, 49)		
(41, 50)		FALSE
(41, 51)		
(41, 52)		
(42, 2)		FALSE
(42, 3)		
(42, 6)		dialog.significant.result.two.means
(42, 7)		
(42, 8)		
(42, 9)		
(42, 10)		411
(42, 11)		159
(42, 12)		Significance - Two Means
(42, 13)		2
(42, 33)		CI of Mean Maximum
(42, 34)		
(42, 35)		ci.maximum
(42, 36)		2
(42, 39)		string.pearson
(42, 40)		Pearson Correlation
(42, 41)		&Correlation
(42, 42)		FALSE
(42, 43)		
(42, 44)		&Pearson
(42, 45)		PearsonTest
(42, 46)		
(42, 47)		Pearson correlation coefficient
(42, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!100
(42, 49)		
(42, 50)		FALSE
(42, 51)		10
(42, 52)		Statistics
(43, 2)		0
(43, 3)		astute base version less than 1.20
(43, 6)		
(43, 7)		5
(43, 8)		11
(43, 9)		13
(43, 10)		
(43, 11)		
(43, 12)		Difference between means:
(43, 13)		
(43, 33)		Variance
(43, 34)		
(43, 35)		variance
(43, 36)		2
(43, 39)		string.spearman
(43, 40)		Spearman Rank Correlation
(43, 41)		&Correlation
(43, 42)		FALSE
(43, 43)		
(43, 44)		&Spearman Rank
(43, 45)		SpearmanTest
(43, 46)		
(43, 47)		Spearman rank-order correlation coefficient
(43, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!101
(43, 49)		
(43, 50)		FALSE
(43, 51)		11
(43, 52)		Statistics
(44, 2)		FALSE
(44, 3)		
(44, 6)		mean.diff
(44, 7)		8
(44, 8)		221
(44, 9)		10
(44, 10)		70
(44, 11)		
(44, 12)		
(44, 13)		
(44, 33)		Standard Deviation
(44, 34)		
(44, 35)		sd
(44, 36)		2
(44, 39)		string.kendall
(44, 40)		Kendall Rank Correlation
(44, 41)		&Correlation
(44, 42)		FALSE
(44, 43)		
(44, 44)		&Kendall Rank
(44, 45)		KendallTest
(44, 46)		
(44, 47)		Kendall rank-order correlation coefficient
(44, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!101
(44, 49)		
(44, 50)		FALSE
(44, 51)		12
(44, 52)		Statistics
(45, 2)		FALSE
(45, 3)		
(45, 6)		
(45, 7)		5
(45, 8)		65
(45, 9)		37
(45, 10)		
(45, 11)		
(45, 12)		SD 1:
(45, 13)		
(45, 33)		Standard Error
(45, 34)		
(45, 35)		se
(45, 36)		2
(45, 39)		string.linear
(45, 40)		Simple Linear Regression
(45, 41)		Re&gression
(45, 42)		FALSE
(45, 43)		
(45, 44)		&Linear
(45, 45)		LinearRegressionTest
(45, 46)		
(45, 47)		Simple linear regression
(45, 48)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!101
(45, 49)		
(45, 50)		FALSE
(45, 51)		13
(45, 52)		Statistics
(46, 2)		FALSE
(46, 3)		
(46, 6)		
(46, 7)		5
(46, 8)		202
(46, 9)		37
(46, 10)		
(46, 11)		
(46, 12)		2:
(46, 13)		
(46, 33)		Skewness
(46, 34)		
(46, 35)		skewness
(46, 36)		2
(46, 39)		string.multiple.linear.regression
(46, 40)		Multiple Linear Regression
(46, 41)		Re&gression
(46, 42)		FALSE
(46, 43)		
(46, 44)		&Multiple Linear
(46, 45)		MultipleLinearRegression
(46, 46)		
(46, 47)		Multiple linear regression
(46, 48)		#NAME?
(46, 49)		
(46, 50)		FALSE
(46, 51)		32
(46, 52)		Statistics
(47, 2)		FALSE
(47, 3)		initialise the help
(47, 6)		sd.1
(47, 7)		8
(47, 8)		112
(47, 9)		34
(47, 10)		70
(47, 11)		
(47, 12)		
(47, 13)		
(47, 33)		Kurtosis
(47, 34)		
(47, 35)		kurtosis
(47, 36)		2
(47, 39)		string.t.test.equal.variance
(47, 40)		Equal Variance t-test
(47, 41)		&T-Test
(47, 42)		FALSE
(47, 43)		
(47, 44)		&Equal Variance
(47, 45)		EqualVarianceTTest
(47, 46)		
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(48, 47)		Two sample t-test assuming unequal variances
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(48, 50)		FALSE
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(50, 50)		FALSE
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(51, 50)		FALSE
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(58, 6)		
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(58, 12)		Minimum sample size of each group =
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(61, 6)		
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(62, 8)		
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(63, 2)		TRUE
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(68, 2)		TRUE
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(69, 11)		
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(70, 2)		FALSE
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(71, 1)		
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(72, 2)		TRUE
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(72, 6)		
(72, 7)		5
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(72, 11)		
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(73, 2)		TRUE
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(73, 6)		
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(74, 2)		TRUE
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(74, 7)		2
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(74, 9)		
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(75, 1)		
(75, 2)		TRUE
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(75, 6)		
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(75, 8)		
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(75, 12)		
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(75, 35)		df.1
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(76, 1)		
(76, 2)		TRUE
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(76, 6)		
(76, 7)		5
(76, 8)		10
(76, 9)		134
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(77, 1)		
(77, 2)		TRUE
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(77, 12)		
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(78, 2)		FALSE
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(78, 6)		
(78, 33)		Range Names
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(79, 2)		TRUE
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(79, 6)		
(79, 33)		Dataset
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(80, 2)		TRUE
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(80, 7)		
(80, 8)		
(80, 9)		
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(80, 13)		2
(81, 2)		TRUE
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(82, 2)		#VALUE!
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(82, 12)		
(82, 13)		
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(84, 1)		
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(84, 12)		
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(85, 6)		
(85, 7)		5
(85, 8)		197
(85, 9)		66
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(85, 11)		
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(86, 2)		DescriptiveMultipleTest
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(87, 2)		FALSE
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(87, 6)		
(87, 7)		5
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(87, 11)		
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(88, 2)		#NAME?
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(89, 2)		TRUE
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(89, 7)		8
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(90, 6)		
(90, 7)		5
(90, 8)		293
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(90, 10)		
(90, 11)		
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(91, 2)		FALSE
(91, 3)		
(91, 6)		
(91, 7)		101
(91, 8)		342
(91, 9)		7
(91, 10)		88
(91, 11)		
(91, 12)		Compute
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(92, 2)		FALSE
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(92, 6)		
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(92, 9)		
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(92, 35)		
(92, 36)		
(93, 2)		FALSE
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(94, 6)		
(94, 7)		5
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(94, 9)		134
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(96, 2)		DescriptiveSequencedTest
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(97, 1)		
(97, 2)		FALSE
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(98, 8)		
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(102, 3)		validate the minimum, maximum and bin sizes in the dialog box
(102, 6)		se
(102, 7)		8
(102, 8)		221
(102, 9)		34
(102, 10)		70
(102, 11)		
(102, 12)		
(102, 13)		
(102, 33)		Dataset
(102, 34)		
(102, 35)		dataset
(102, 36)		2x2
(103, 1)		
(103, 2)		FALSE
(103, 3)		
(103, 6)		
(103, 7)		101
(103, 8)		310
(103, 9)		7
(103, 10)		88
(103, 11)		
(103, 12)		Compute
(103, 13)		
(104, 1)		
(104, 2)		FALSE
(104, 3)		
(104, 6)		
(104, 7)		2
(104, 8)		
(104, 9)		
(104, 10)		88
(104, 11)		
(104, 12)		Close
(104, 13)		
(104, 32)		styles.one.way.anova
(104, 33)		Statistics
(104, 34)		BASE.XLA!AH107:AK117
(105, 1)		
(105, 2)		FALSE
(105, 3)		
(105, 6)		
(105, 7)		24
(105, 8)		
(105, 9)		62
(105, 10)		88
(105, 11)		
(105, 12)		
(105, 13)		
(105, 33)		Dataset
(105, 34)		BASE.XLA!AH118:AK122
(106, 1)		
(106, 2)		FALSE
(106, 3)		
(106, 6)		
(106, 7)		5
(106, 8)		10
(106, 9)		100
(106, 10)		
(106, 11)		
(106, 12)		Minimum sample size =
(106, 13)		
(106, 33)		<No name defined>
(106, 34)		
(106, 35)		
(106, 36)		
(107, 1)		
(107, 2)		FALSE
(107, 3)		
(107, 6)		result
(107, 7)		5
(107, 8)		186
(107, 9)		100
(107, 10)		
(107, 11)		
(107, 12)		
(107, 13)		
(107, 33)		Treatment Sum of Squares
(107, 34)		
(107, 35)		ss.1
(107, 36)		1
(108, 1)		
(108, 2)		TRUE
(108, 3)		
(108, 6)		
(108, 33)		Treatment Degrees of Freedom
(108, 34)		
(108, 35)		df.1
(108, 36)		1
(109, 1)		
(109, 2)		TRUE
(109, 3)		
(109, 6)		
(109, 33)		Treatment Mean Square
(109, 34)		
(109, 35)		ms.1
(109, 36)		1
(110, 2)		TRUE
(110, 3)		
(110, 6)		dialog.precision.single.proportion
(110, 7)		
(110, 8)		
(110, 9)		
(110, 10)		411
(110, 11)		125
(110, 12)		Precision - Single Proportion
(110, 13)		2
(110, 33)		Treatment F Statistic
(110, 34)		
(110, 35)		f
(110, 36)		1
(111, 2)		TRUE
(111, 3)		
(111, 6)		
(111, 7)		5
(111, 8)		135
(111, 9)		13
(111, 10)		
(111, 11)		
(111, 12)		Proportion:
(111, 13)		
(111, 33)		Treatment p
(111, 34)		
(111, 35)		p
(111, 36)		1
(112, 2)		FALSE
(112, 3)		
(112, 6)		
(112, 7)		8
(112, 8)		221
(112, 9)		10
(112, 10)		70
(112, 11)		
(112, 12)		
(112, 13)		
(112, 33)		Residual Sum of Squares
(112, 34)		
(112, 35)		ss.2
(112, 36)		1
(113, 2)		FALSE
(113, 3)		
(113, 6)		
(113, 7)		5
(113, 8)		60
(113, 9)		37
(113, 10)		
(113, 11)		
(113, 12)		Required Size of SE:
(113, 13)		
(113, 33)		Residual Degrees of Freedom
(113, 34)		
(113, 35)		df.2
(113, 36)		1
(114, 2)		FALSE
(114, 3)		
(114, 6)		
(114, 7)		8
(114, 8)		221
(114, 9)		34
(114, 10)		70
(114, 11)		
(114, 12)		
(114, 13)		
(114, 33)		Residual Mean Square
(114, 34)		
(114, 35)		ms.2
(114, 36)		1
(115, 2)		FALSE
(115, 3)		scale the chart y axis
(115, 6)		
(115, 7)		101
(115, 8)		310
(115, 9)		7
(115, 10)		88
(115, 11)		
(115, 12)		Compute
(115, 13)		
(115, 33)		Total Sum of Squares
(115, 34)		
(115, 35)		ss.3
(115, 36)		1
(116, 2)		FALSE
(116, 3)		
(116, 6)		
(116, 7)		2
(116, 8)		
(116, 9)		
(116, 10)		88
(116, 11)		
(116, 12)		Close
(116, 13)		
(116, 33)		Total Degrees of Freedom
(116, 34)		
(116, 35)		df.3
(116, 36)		1
(117, 6)		
(117, 7)		24
(117, 8)		
(117, 9)		62
(117, 10)		88
(117, 11)		
(117, 12)		
(117, 13)		
(117, 33)		Variable Names
(117, 34)		
(117, 35)		variable
(117, 36)		2
(118, 2)		McNemarTest
(118, 3)		performs mcnemar test
(118, 6)		
(118, 7)		5
(118, 8)		10
(118, 9)		100
(118, 10)		
(118, 11)		
(118, 12)		Minimum sample size =
(118, 13)		
(118, 33)		Dataset
(118, 34)		
(118, 35)		dataset
(118, 36)		2x2
(119, 2)		FALSE
(119, 3)		
(119, 6)		
(119, 7)		5
(119, 8)		186
(119, 9)		100
(119, 10)		
(119, 11)		
(119, 12)		
(119, 13)		
(119, 33)		Group N
(119, 34)		
(119, 35)		n
(119, 36)		2
(120, 2)		#NAME?
(120, 3)		
(120, 6)		
(120, 33)		Group Mean
(120, 34)		
(120, 35)		mean
(120, 36)		2
(121, 2)		TRUE
(121, 3)		
(121, 6)		
(121, 33)		Group SD
(121, 34)		
(121, 35)		sd
(121, 36)		2
(122, 2)		FALSE
(122, 3)		
(122, 6)		dialog.precision.two.means
(122, 7)		
(122, 8)		
(122, 9)		
(122, 10)		411
(122, 11)		125
(122, 12)		Precision - Two Means
(122, 13)		3
(123, 2)		FALSE
(123, 3)		
(123, 6)		
(123, 7)		5
(123, 8)		202
(123, 9)		13
(123, 10)		
(123, 11)		
(123, 12)		2:
(123, 13)		
(123, 32)		styles.kruskal.wallis.anova
(123, 33)		Statistics
(123, 34)		BASE.XLA!AH126:AK132
(124, 2)		FALSE
(124, 3)		
(124, 6)		
(124, 7)		5
(124, 8)		65
(124, 9)		13
(124, 10)		
(124, 11)		
(124, 12)		SD 1:
(124, 13)		
(124, 33)		Dataset
(124, 34)		BASE.XLA!AH133:AK137
(125, 6)		
(125, 7)		8
(125, 8)		112
(125, 9)		10
(125, 10)		70
(125, 11)		
(125, 12)		
(125, 13)		
(125, 33)		<No name defined>
(125, 34)		
(125, 35)		
(125, 36)		
(126, 2)		FisherTest
(126, 3)		performs fisher test
(126, 6)		
(126, 7)		8
(126, 8)		221
(126, 9)		10
(126, 10)		70
(126, 11)		
(126, 12)		
(126, 13)		
(126, 33)		Number of Groups
(126, 34)		
(126, 35)		n.1
(126, 36)		1
(127, 2)		FALSE
(127, 3)		
(127, 6)		
(127, 7)		5
(127, 8)		60
(127, 9)		37
(127, 10)		
(127, 11)		
(127, 12)		Required Size of SE:
(127, 13)		
(127, 20)		
(127, 33)		KW Statistic
(127, 34)		
(127, 35)		kw
(127, 36)		1
(128, 2)		#NAME?
(128, 3)		
(128, 6)		
(128, 7)		8
(128, 8)		221
(128, 9)		34
(128, 10)		70
(128, 11)		
(128, 12)		
(128, 13)		
(128, 20)		
(128, 23)		
(128, 33)		Degrees of Freedom
(128, 34)		
(128, 35)		df
(128, 36)		1
(129, 2)		TRUE
(129, 3)		
(129, 6)		
(129, 7)		101
(129, 8)		310
(129, 9)		7
(129, 10)		88
(129, 11)		
(129, 12)		Compute
(129, 13)		
(129, 23)		
(129, 33)		Two-tailed p
(129, 34)		
(129, 35)		p
(129, 36)		1
(130, 2)		FALSE
(130, 3)		
(130, 6)		
(130, 7)		2
(130, 8)		
(130, 9)		
(130, 10)		88
(130, 11)		
(130, 12)		Close
(130, 13)		
(130, 33)		Corrected for Ties Message
(130, 34)		
(130, 35)		message.1
(130, 36)		1
(131, 2)		FALSE
(131, 3)		
(131, 6)		
(131, 7)		24
(131, 8)		
(131, 9)		62
(131, 10)		88
(131, 11)		
(131, 12)		
(131, 13)		
(131, 33)		Inaccurate p Value Message
(131, 34)		
(131, 35)		message
(131, 36)		1
(132, 2)		FALSE
(132, 3)		
(132, 6)		
(132, 7)		5
(132, 8)		10
(132, 9)		100
(132, 10)		
(132, 11)		
(132, 12)		Minimum sample size =
(132, 13)		
(132, 33)		Variable Names
(132, 34)		
(132, 35)		variable
(132, 36)		2
(133, 6)		
(133, 7)		5
(133, 8)		186
(133, 9)		100
(133, 10)		
(133, 11)		
(133, 12)		
(133, 13)		
(133, 33)		Dataset
(133, 34)		
(133, 35)		dataset
(133, 36)		2x2
(134, 2)		ChiSquareTest
(134, 3)		performs chi-square test
(134, 6)		
(134, 33)		Group N
(134, 34)		
(134, 35)		n
(134, 36)		2
(135, 2)		FALSE
(135, 3)		
(135, 6)		
(135, 33)		Group Rank Sum
(135, 34)		
(135, 35)		sum
(135, 36)		2
(136, 2)		#NAME?
(136, 3)		
(136, 6)		dialog.precision.two.proportions
(136, 7)		
(136, 8)		
(136, 9)		
(136, 10)		411
(136, 11)		125
(136, 12)		Precision - Two Proportions
(136, 13)		3
(136, 33)		Group Mean Rank
(136, 34)		
(136, 35)		mean
(136, 36)		2
(137, 2)		FALSE
(137, 3)		
(137, 6)		
(137, 7)		5
(137, 8)		202
(137, 9)		13
(137, 10)		
(137, 11)		
(137, 12)		2:
(137, 13)		
(138, 2)		FALSE
(138, 3)		
(138, 6)		
(138, 7)		5
(138, 8)		10
(138, 9)		13
(138, 10)		
(138, 11)		
(138, 12)		Proportion 1:
(138, 13)		
(138, 32)		styles.two.way.anova
(138, 33)		Statistics
(138, 34)		BASE.XLA!AH141:AK156
(139, 2)		FALSE
(139, 3)		
(139, 6)		
(139, 7)		8
(139, 8)		112
(139, 9)		10
(139, 10)		70
(139, 11)		
(139, 12)		
(139, 13)		
(139, 33)		Dataset
(139, 34)		BASE.XLA!AH157:AK165
(140, 2)		FALSE
(140, 3)		
(140, 6)		
(140, 7)		8
(140, 8)		221
(140, 9)		10
(140, 10)		70
(140, 11)		
(140, 12)		
(140, 13)		
(140, 33)		<No name defined>
(140, 34)		
(140, 35)		
(140, 36)		
(141, 6)		
(141, 7)		5
(141, 8)		60
(141, 9)		37
(141, 10)		
(141, 11)		
(141, 12)		Required Size of SE:
(141, 13)		
(141, 33)		Treatment Sum of Squares
(141, 34)		
(141, 35)		ss.1
(141, 36)		1
(142, 2)		OneWayAnovaTest
(142, 3)		performs one-way anova
(142, 6)		
(142, 7)		8
(142, 8)		221
(142, 9)		34
(142, 10)		70
(142, 11)		
(142, 12)		
(142, 13)		
(142, 33)		Treatment Degrees of Freedom
(142, 34)		
(142, 35)		df.1
(142, 36)		1
(143, 2)		FALSE
(143, 3)		
(143, 6)		
(143, 7)		101
(143, 8)		310
(143, 9)		7
(143, 10)		88
(143, 11)		
(143, 12)		Compute
(143, 13)		
(143, 33)		Treatment Mean Squares
(143, 34)		
(143, 35)		ms.1
(143, 36)		1
(144, 2)		#NAME?
(144, 3)		
(144, 6)		
(144, 7)		2
(144, 8)		
(144, 9)		
(144, 10)		88
(144, 11)		
(144, 12)		Close
(144, 13)		
(144, 33)		Treatment F Statistic
(144, 34)		
(144, 35)		f.1
(144, 36)		1
(145, 2)		FALSE
(145, 3)		
(145, 6)		
(145, 7)		24
(145, 8)		
(145, 9)		62
(145, 10)		88
(145, 11)		
(145, 12)		
(145, 13)		
(145, 33)		Treatment p
(145, 34)		
(145, 35)		p.1
(145, 36)		1
(146, 2)		FALSE
(146, 3)		
(146, 6)		
(146, 7)		5
(146, 8)		10
(146, 9)		100
(146, 10)		
(146, 11)		
(146, 12)		Minimum sample size =
(146, 13)		
(146, 33)		Block Sum of Squares
(146, 34)		
(146, 35)		ss.2
(146, 36)		1
(147, 2)		FALSE
(147, 3)		
(147, 6)		
(147, 7)		5
(147, 8)		186
(147, 9)		100
(147, 10)		
(147, 11)		
(147, 12)		
(147, 13)		
(147, 33)		Block Degrees of Freedom
(147, 34)		
(147, 35)		df.2
(147, 36)		1
(148, 2)		FALSE
(148, 3)		
(148, 6)		
(148, 33)		Block Mean Square
(148, 34)		
(148, 35)		ms.2
(148, 36)		1
(149, 6)		
(149, 33)		Block F Statistic
(149, 34)		
(149, 35)		f.2
(149, 36)		1
(150, 2)		KruskalWallisTest
(150, 3)		performs kruskal-wallis anova
(150, 6)		
(150, 33)		Block p
(150, 34)		
(150, 35)		p.2
(150, 36)		1
(151, 2)		FALSE
(151, 3)		
(151, 6)		
(151, 33)		Residual Sum of Squares
(151, 34)		
(151, 35)		ss.3
(151, 36)		1
(152, 2)		#NAME?
(152, 3)		
(152, 6)		dialog.descriptive
(152, 7)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!103
(152, 8)		
(152, 9)		
(152, 10)		
(152, 11)		144
(152, 12)		Single Descriptive
(152, 13)		2
(152, 33)		Residual Degrees of Freedom
(152, 34)		
(152, 35)		df.3
(152, 36)		1
(153, 2)		TRUE
(153, 3)		
(153, 6)		
(153, 7)		5
(153, 8)		28
(153, 9)		13
(153, 10)		
(153, 11)		
(153, 12)		&Range:
(153, 13)		
(153, 33)		Residual Mean Square
(153, 34)		
(153, 35)		ms.3
(153, 36)		1
(154, 2)		FALSE
(154, 3)		
(154, 6)		
(154, 7)		10
(154, 8)		86
(154, 9)		10
(154, 10)		160
(154, 11)		
(154, 12)		
(154, 13)		
(154, 33)		Total Sum of Squares
(154, 34)		
(154, 35)		ss.4
(154, 36)		1
(155, 2)		FALSE
(155, 3)		
(155, 6)		
(155, 7)		5
(155, 8)		25
(155, 9)		66
(155, 10)		
(155, 11)		
(155, 12)		&Min:
(155, 13)		
(155, 33)		Total Degrees of Freedom
(155, 34)		
(155, 35)		df.4
(155, 36)		1
(156, 2)		FALSE
(156, 3)		
(156, 6)		dialog.descriptive.min
(156, 7)		8
(156, 8)		61
(156, 9)		64
(156, 10)		51
(156, 11)		
(156, 12)		
(156, 13)		
(156, 31)		
(156, 33)		Variable Names
(156, 34)		
(156, 35)		variable
(156, 36)		2
(157, 6)		
(157, 7)		5
(157, 8)		138
(157, 9)		66
(157, 10)		
(157, 11)		
(157, 12)		Ma&x:
(157, 13)		
(157, 33)		Range Names
(157, 34)		
(157, 35)		range
(157, 36)		2
(158, 2)		FriedmanTest
(158, 3)		performs friedman anova
(158, 6)		dialog.descriptive.max
(158, 7)		8
(158, 8)		178
(158, 9)		64
(158, 10)		51
(158, 11)		
(158, 12)		
(158, 13)		
(158, 33)		Dataset
(158, 34)		
(158, 35)		dataset
(158, 36)		2x2
(159, 2)		FALSE
(159, 3)		
(159, 6)		
(159, 7)		5
(159, 8)		40
(159, 9)		91
(159, 10)		
(159, 11)		
(159, 12)		&Bin Size:
(159, 13)		
(159, 33)		Group N
(159, 34)		
(159, 35)		n.1
(159, 36)		2
(160, 2)		#NAME?
(160, 3)		
(160, 6)		dialog.descriptive.bin.size
(160, 7)		8
(160, 8)		108
(160, 9)		89
(160, 10)		68
(160, 11)		
(160, 12)		
(160, 13)		
(160, 33)		Group Mean
(160, 34)		
(160, 35)		mean.1
(160, 36)		2
(161, 2)		FALSE
(161, 3)		
(161, 6)		
(161, 7)		5
(161, 8)		20
(161, 9)		115
(161, 10)		
(161, 11)		
(161, 12)		&No of Bins:
(161, 13)		
(161, 33)		Group SD
(161, 34)		
(161, 35)		sd.1
(161, 36)		2
(162, 2)		FALSE
(162, 3)		
(162, 6)		dialog.descriptive.bins
(162, 7)		7
(162, 8)		108
(162, 9)		113
(162, 10)		68
(162, 11)		
(162, 12)		
(162, 13)		
(162, 33)		Subject N
(162, 34)		
(162, 35)		n.2
(162, 36)		2
(163, 2)		FALSE
(163, 3)		
(163, 6)		
(163, 7)		14
(163, 8)		10
(163, 9)		44
(163, 10)		236
(163, 11)		94
(163, 12)		Bin Criteria
(163, 13)		
(163, 33)		Subject Mean
(163, 34)		
(163, 35)		mean.2
(163, 36)		2
(164, 2)		FALSE
(164, 3)		
(164, 6)		
(164, 7)		1
(164, 8)		263
(164, 9)		6
(164, 10)		90
(164, 11)		
(164, 12)		OK
(164, 13)		
(164, 33)		Subject SD
(164, 34)		
(164, 35)		sd.2
(164, 36)		2
(165, 6)		
(165, 7)		2
(165, 8)		
(165, 9)		
(165, 10)		90
(165, 11)		
(165, 12)		Cancel
(165, 13)		
(166, 1)		
(166, 2)		TwoWayAnovaTest
(166, 3)		performs two-way anova
(166, 6)		
(166, 7)		24
(166, 8)		
(166, 9)		62
(166, 10)		
(166, 11)		
(166, 12)		
(166, 13)		
(166, 32)		styles.friedman.anova
(166, 33)		Statistics
(166, 34)		BASE.XLA!AH169:AK175
(167, 1)		
(167, 2)		FALSE
(167, 3)		
(167, 6)		
(167, 33)		Dataset
(167, 34)		BASE.XLA!AH176:AK180
(168, 1)		
(168, 2)		#NAME?
(168, 3)		
(168, 6)		
(168, 33)		<No name defined>
(168, 34)		
(168, 35)		
(168, 36)		
(169, 2)		TRUE
(169, 3)		
(169, 6)		dialog.alt.ttest
(169, 7)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!103
(169, 8)		
(169, 9)		
(169, 10)		
(169, 11)		144
(169, 12)		
(169, 13)		2
(169, 33)		Number of Groyps
(169, 34)		
(169, 35)		n.1
(169, 36)		1
(170, 2)		FALSE
(170, 3)		
(170, 6)		
(170, 7)		5
(170, 8)		28
(170, 9)		13
(170, 10)		
(170, 11)		
(170, 12)		&Range:
(170, 13)		
(170, 33)		Number of Subjects
(170, 34)		
(170, 35)		n.2
(170, 36)		1
(171, 1)		
(171, 2)		FALSE
(171, 3)		
(171, 6)		
(171, 7)		10
(171, 8)		86
(171, 9)		10
(171, 10)		160
(171, 11)		
(171, 12)		
(171, 13)		
(171, 33)		Fr Statistic
(171, 34)		
(171, 35)		fr
(171, 36)		1
(172, 1)		
(172, 2)		FALSE
(172, 3)		
(172, 6)		
(172, 7)		5
(172, 8)		51
(172, 9)		66
(172, 10)		
(172, 11)		
(172, 12)		&N:
(172, 13)		
(172, 33)		Degrees of Freedom
(172, 34)		
(172, 35)		df
(172, 36)		1
(173, 1)		
(173, 6)		dialog.alt.ttest.n
(173, 7)		8
(173, 8)		72
(173, 9)		64
(173, 10)		94
(173, 11)		
(173, 12)		
(173, 13)		
(173, 14)		
(173, 33)		Two-tailed p
(173, 34)		
(173, 35)		p
(173, 36)		1
(174, 1)		
(174, 2)		AlternativeTTest
(174, 3)		performs keyboard entry t-test
(174, 6)		
(174, 7)		5
(174, 8)		23
(174, 9)		91
(174, 10)		
(174, 11)		
(174, 12)		&Mean:
(174, 13)		
(174, 14)		
(174, 33)		Inaccurate p  Value Message
(174, 34)		
(174, 35)		message
(174, 36)		1
(175, 1)		
(175, 2)		FALSE
(175, 3)		
(175, 6)		dialog.alt.ttest.mean
(175, 7)		8
(175, 8)		72
(175, 9)		89
(175, 10)		94
(175, 11)		
(175, 12)		
(175, 13)		
(175, 14)		
(175, 33)		Variable Names
(175, 34)		
(175, 35)		variable
(175, 36)		2
(176, 2)		FALSE
(176, 3)		
(176, 6)		
(176, 7)		5
(176, 8)		41
(176, 9)		115
(176, 10)		
(176, 11)		
(176, 12)		&SD:
(176, 13)		
(176, 14)		
(176, 33)		Dataset
(176, 34)		
(176, 35)		dataset
(176, 36)		2x2
(177, 2)		FALSE
(177, 3)		
(177, 6)		dialog.alt.ttest.sd
(177, 7)		8
(177, 8)		72
(177, 9)		113
(177, 10)		94
(177, 11)		
(177, 12)		
(177, 13)		
(177, 14)		
(177, 33)		Group N
(177, 34)		
(177, 35)		n
(177, 36)		2
(178, 2)		FALSE
(178, 3)		
(178, 6)		
(178, 7)		14
(178, 8)		10
(178, 9)		44
(178, 10)		236
(178, 11)		94
(178, 12)		2nd Dataset
(178, 13)		
(178, 33)		Group Rank Sum
(178, 34)		
(178, 35)		sum
(178, 36)		2
(179, 2)		#NAME?
(179, 3)		
(179, 6)		
(179, 7)		1
(179, 8)		263
(179, 9)		6
(179, 10)		90
(179, 11)		
(179, 12)		OK
(179, 13)		
(179, 33)		Group Mean Rank
(179, 34)		
(179, 35)		mean
(179, 36)		2
(180, 2)		TRUE
(180, 3)		
(180, 6)		
(180, 7)		2
(180, 8)		
(180, 9)		
(180, 10)		90
(180, 11)		
(180, 12)		Cancel
(180, 13)		
(181, 2)		FALSE
(181, 3)		validate the n, mean, sd in the dialog box
(181, 6)		
(181, 7)		24
(181, 8)		
(181, 9)		62
(181, 10)		
(181, 11)		
(181, 12)		
(181, 13)		
(182, 2)		FALSE
(182, 3)		
(182, 6)		
(183, 2)		TRUE
(183, 3)		
(183, 6)		
(184, 2)		FALSE
(184, 3)		
(184, 6)		dialog.seq.descriptive
(184, 7)		 C:\DEV\USR\STATS\XLBUILD\astute.hlp!103
(184, 8)		
(184, 9)		
(184, 10)		
(184, 11)		
(184, 12)		Sequenced Descriptive
(184, 13)		2
(184, 33)		Variable Name
(184, 34)		
(184, 35)		variable.1
(184, 36)		1
(185, 1)		
(185, 2)		FALSE
(185, 3)		
(185, 6)		
(185, 7)		5
(185, 8)		32
(185, 9)		13
(185, 10)		
(185, 11)		
(185, 12)		&Range:
(185, 13)		
(185, 33)		Dataset
(185, 34)		
(185, 35)		dataset.1
(185, 36)		2
(186, 2)		FALSE
(186, 3)		
(186, 6)		
(186, 7)		10
(186, 8)		90
(186, 9)		10
(186, 10)		160
(186, 11)		
(186, 12)		
(186, 13)		
(186, 33)		N
(186, 34)		
(186, 35)		n.1
(186, 36)		1
(187, 2)		TRUE
(187, 3)		
(187, 6)		
(187, 7)		5
(187, 8)		5
(187, 9)		37
(187, 10)		
(187, 11)		
(187, 12)		&Sequence:
(187, 13)		
(187, 33)		Sum
(187, 34)		
(187, 35)		sum.1
(187, 36)		1
(188, 2)		FALSE
(188, 3)		
(188, 6)		dialog.seq.descriptive.seq.range
(188, 7)		10
(188, 8)		90
(188, 9)		34
(188, 10)		160
(188, 11)		
(188, 12)		
(188, 13)		
(188, 33)		Mean
(188, 34)		
(188, 35)		mean.1
(188, 36)		1
(189, 2)		FALSE
(189, 3)		
(189, 6)		
(189, 7)		101
(189, 8)		263
(189, 9)		6
(189, 10)		90
(189, 11)		
(189, 12)		OK
(189, 13)		
(189, 33)		Variance
(189, 34)		
(189, 35)		variance.1
(189, 36)		1
(190, 2)		FALSE
(190, 3)		
(190, 6)		
(190, 7)		2
(190, 8)		
(190, 9)		
(190, 10)		90
(190, 11)		
(190, 12)		Cancel
(190, 13)		
(190, 33)		Standard Deviation
(190, 34)		
(190, 35)		sd.1
(190, 36)		1
(191, 2)		TRUE
(191, 3)		
(191, 6)		
(191, 7)		24
(191, 8)		
(191, 9)		62
(191, 10)		
(191, 11)		
(191, 12)		
(191, 13)		
(191, 33)		Standard Error
(191, 34)		
(191, 35)		se.1
(191, 36)		1
(192, 2)		FALSE
(192, 3)		
(192, 6)		
(192, 7)		FALSE
(192, 33)		Skewness
(192, 34)		
(192, 35)		skewness.1
(192, 36)		1
(193, 2)		TRUE
(193, 3)		
(193, 6)		
(193, 33)		Kurtosis
(193, 34)		
(193, 35)		kurtosis.1
(193, 36)		1
(194, 2)		FALSE
(194, 3)		
(194, 6)		
(194, 33)		Minimum
(194, 34)		
(194, 35)		minimum.1
(194, 36)		1
(195, 2)		FALSE
(195, 3)		
(195, 6)		
(195, 33)		Lower Percentile
(195, 34)		
(195, 35)		lower.pcntl.1
(195, 36)		1
(196, 2)		FALSE
(196, 3)		
(196, 6)		
(196, 33)		Median
(196, 34)		
(196, 35)		median.1
(196, 36)		1
(197, 6)		
(197, 33)		Upper Percentile
(197, 34)		
(197, 35)		upper.pcntl.1
(197, 36)		1
(198, 6)		
(198, 33)		Maximum
(198, 34)		
(198, 35)		maximum.1
(198, 36)		1
(199, 6)		
(200, 1)		
(200, 2)		Two sample test macros
(200, 3)		
(200, 6)		
(200, 33)		Variable Name
(200, 34)		
(200, 35)		variable.2
(200, 36)		1
(201, 6)		
(201, 33)		Dataset
(201, 34)		
(201, 35)		dataset.2
(201, 36)		2
(202, 2)		WilcoxonTest
(202, 3)		performs wilcoxon signed-ranks test
(202, 6)		
(202, 33)		N
(202, 34)		
(202, 35)		n.2
(202, 36)		1
(203, 2)		FALSE
(203, 3)		
(203, 6)		
(203, 33)		Sum
(203, 34)		
(203, 35)		sum.2
(203, 36)		1
(204, 2)		#NAME?
(204, 3)		
(204, 6)		
(204, 33)		Mean
(204, 34)		
(204, 35)		mean.2
(204, 36)		1
(205, 2)		FALSE
(205, 3)		
(205, 6)		
(205, 33)		Variance
(205, 34)		
(205, 35)		variance.2
(205, 36)		1
(206, 2)		FALSE
(206, 3)		
(206, 6)		
(206, 33)		Standard Deviation
(206, 34)		
(206, 35)		sd.2
(206, 36)		1
(207, 2)		FALSE
(207, 3)		
(207, 6)		
(207, 33)		Standard Error
(207, 34)		
(207, 35)		se.2
(207, 36)		1
(208, 2)		FALSE
(208, 3)		
(208, 6)		
(208, 33)		Skewness
(208, 34)		
(208, 35)		kurtosis.2
(208, 36)		1
(209, 2)		FALSE
(209, 3)		
(209, 33)		Kurtosis
(209, 34)		
(209, 35)		skewness.2
(209, 36)		1
(210, 33)		Minimum
(210, 34)		
(210, 35)		minimum.2
(210, 36)		1
(211, 2)		MannWhitneyUTest
(211, 3)		performs mann-whitney u test
(211, 33)		Lower Percentile
(211, 34)		
(211, 35)		lower.pcntl.2
(211, 36)		1
(212, 2)		FALSE
(212, 3)		
(212, 33)		Median
(212, 34)		
(212, 35)		median.2
(212, 36)		1
(213, 2)		#NAME?
(213, 3)		
(213, 33)		Upper Percentile
(213, 34)		
(213, 35)		upper.pcntl.2
(213, 36)		1
(214, 2)		TRUE
(214, 3)		
(214, 33)		Maximum
(214, 34)		
(214, 35)		maximum.2
(214, 36)		1
(215, 2)		FALSE
(215, 3)		
(216, 2)		FALSE
(216, 3)		
(217, 2)		FALSE
(217, 3)		
(217, 32)		styles.ftest
(217, 33)		Statistics
(217, 34)		BASE.XLA!AH221:AK227
(218, 2)		FALSE
(218, 3)		
(218, 33)		Dataset 1
(218, 34)		BASE.XLA!AH185:AK199
(219, 33)		Dataset 2
(219, 34)		BASE.XLA!AH201:AK215
(220, 2)		SpearmanTest
(220, 3)		performs spearman rank correlation test
(220, 33)		<No name defined>
(220, 34)		
(220, 35)		
(220, 36)		
(221, 2)		FALSE
(221, 3)		
(221, 33)		F Statistic
(221, 34)		
(221, 35)		f
(221, 36)		1
(222, 2)		#NAME?
(222, 3)		
(222, 33)		CI of F Minimum
(222, 34)		
(222, 35)		ci.minimum
(222, 36)		1
(223, 2)		FALSE
(223, 3)		
(223, 33)		CI of F Maximum
(223, 34)		
(223, 35)		ci.maximum
(223, 36)		1
(224, 2)		FALSE
(224, 3)		
(224, 33)		Numerator Degrees of Freedom
(224, 34)		
(224, 35)		df.1
(224, 36)		1
(225, 2)		FALSE
(225, 3)		
(225, 33)		Denominator Degrees of Freedom
(225, 34)		
(225, 35)		df.2
(225, 36)		1
(226, 2)		FALSE
(226, 3)		
(226, 33)		Two-tailed p
(226, 34)		
(226, 35)		p
(226, 36)		1
(227, 2)		FALSE
(227, 3)		
(228, 2)		FALSE
(228, 3)		label the x and y axis on the scatter plot
(228, 32)		styles.two.sample.ttest
(228, 33)		Statistics
(228, 34)		BASE.XLA!AH232:AK239
(229, 2)		FALSE
(229, 3)		scale the x and y axis on the scatter plot
(229, 33)		Dataset 1
(229, 34)		BASE.XLA!AH185:AK199
(230, 2)		FALSE
(230, 3)		
(230, 33)		Dataset 2
(230, 34)		BASE.XLA!AH201:AK215
(231, 33)		<No name defined>
(231, 34)		
(231, 35)		
(231, 36)		
(232, 2)		EqualVarianceTTest
(232, 3)		performs equal variance ttest
(232, 33)		Mean Difference
(232, 34)		
(232, 35)		mean.3
(232, 36)		1
(233, 2)		FALSE
(233, 3)		
(233, 33)		SE of Difference
(233, 34)		
(233, 35)		se.3
(233, 36)		1
(234, 2)		#NAME?
(234, 3)		
(234, 33)		CI of Mean Difference Minimum
(234, 34)		
(234, 35)		ci.minimum
(234, 36)		1
(235, 2)		TRUE
(235, 3)		
(235, 33)		CI of Mean Difference Maximum
(235, 34)		
(235, 35)		ci.maximum
(235, 36)		1
(236, 2)		FALSE
(236, 3)		
(236, 33)		t Statistic
(236, 34)		
(236, 35)		t
(236, 36)		1
(237, 2)		FALSE
(237, 3)		
(237, 33)		Degrees of Freedom
(237, 34)		
(237, 35)		df
(237, 36)		1
(238, 2)		FALSE
(238, 3)		
(238, 33)		Two-tailed p
(238, 34)		
(238, 35)		p
(238, 36)		1
(239, 2)		FALSE
(239, 3)		
(240, 3)		
(240, 32)		styles.two.sample.alternative.ttest
(240, 33)		Statistics Equal Variance
(240, 34)		BASE.XLA!AH245:AK252
(241, 2)		UnequalVarianceTTest
(241, 3)		performs unequal variance ttest
(241, 33)		Statistics Unequal Variance
(241, 34)		BASE.XLA!AH253:AK259
(242, 2)		FALSE
(242, 3)		
(242, 33)		Dataset 1
(242, 34)		BASE.XLA!AH185:AK199
(243, 2)		#NAME?
(243, 3)		
(243, 33)		Keyboard Entry
(243, 34)		BASE.XLA!AH260:AK265
(244, 2)		TRUE
(244, 3)		
(244, 33)		<No name defined>
(244, 34)		
(244, 35)		
(244, 36)		
(245, 2)		FALSE
(245, 3)		
(245, 33)		Mean Difference
(245, 34)		
(245, 35)		mean.3
(245, 36)		1
(246, 2)		FALSE
(246, 3)		
(246, 33)		SE of Difference
(246, 34)		
(246, 35)		se.3
(246, 36)		1
(247, 2)		FALSE
(247, 3)		
(247, 33)		CI of Mean Difference Minimum
(247, 34)		
(247, 35)		ci.minimum.1
(247, 36)		1
(248, 2)		FALSE
(248, 3)		
(248, 33)		CI of Mean Difference Maximum
(248, 34)		
(248, 35)		ci.maximum.1
(248, 36)		1
(249, 3)		
(249, 33)		t Statistic
(249, 34)		
(249, 35)		t.1
(249, 36)		1
(250, 2)		PairedTTest
(250, 3)		performs paired-t test
(250, 33)		Degrees of Freedom
(250, 34)		
(250, 35)		df.1
(250, 36)		1
(251, 2)		FALSE
(251, 3)		
(251, 33)		Two-tailed p
(251, 34)		
(251, 35)		p.1
(251, 36)		1
(252, 2)		#NAME?
(252, 3)		
(252, 33)		<No name defined>
(252, 34)		
(252, 35)		
(252, 36)		
(253, 2)		TRUE
(253, 3)		
(253, 33)		SE of Difference
(253, 34)		
(253, 35)		se.4
(253, 36)		1
(254, 2)		FALSE
(254, 3)		
(254, 33)		CI of Mean Difference Minimum
(254, 34)		
(254, 35)		ci.minimum.2
(254, 36)		1
(255, 2)		FALSE
(255, 3)		
(255, 33)		CI of Mean Difference Maximum
(255, 34)		
(255, 35)		ci.maximum.2
(255, 36)		1
(256, 2)		FALSE
(256, 3)		
(256, 33)		t Statistic
(256, 34)		
(256, 35)		t.2
(256, 36)		1
(257, 2)		FALSE
(257, 3)		
(257, 33)		Degrees of Freedom
(257, 34)		
(257, 35)		df.2
(257, 36)		1
(258, 33)		Two-tailed p
(258, 34)		
(258, 35)		p.2
(258, 36)		1
(259, 2)		FTest
(259, 3)		perfroms f test
(259, 33)		N
(259, 34)		
(259, 35)		n.2
(259, 36)		1
(260, 2)		FALSE
(260, 3)		
(260, 33)		Sum
(260, 34)		
(260, 35)		sum.2
(260, 36)		1
(261, 2)		#NAME?
(261, 3)		
(261, 33)		Mean
(261, 34)		
(261, 35)		mean.2
(261, 36)		1
(262, 2)		TRUE
(262, 3)		
(262, 33)		Variance
(262, 34)		
(262, 35)		variance.2
(262, 36)		1
(263, 2)		FALSE
(263, 3)		
(263, 33)		Standard Deviation
(263, 34)		
(263, 35)		sd.2
(263, 36)		1
(264, 2)		FALSE
(264, 3)		
(264, 33)		Standard Error
(264, 34)		
(264, 35)		se.2
(264, 36)		1
(265, 2)		FALSE
(265, 3)		
(266, 2)		FALSE
(266, 3)		
(266, 32)		styles.paired.ttest
(266, 33)		Statistics
(266, 34)		BASE.XLA!AH271:AK277
(267, 33)		Dataset 1
(267, 34)		BASE.XLA!AH185:AK199
(268, 2)		LinearRegressionTest
(268, 3)		performs simple linear regression
(268, 33)		Dataset 2
(268, 34)		BASE.XLA!AH201:AK215
(269, 2)		FALSE
(269, 3)		
(269, 33)		Differences
(269, 34)		BASE.XLA!AH278:AK285
(270, 2)		#NAME?
(270, 3)		
(270, 33)		<No name defined>
(270, 34)		
(270, 35)		
(270, 36)		
(271, 2)		TRUE
(271, 3)		
(271, 33)		Mean Difference
(271, 34)		
(271, 35)		mean.4
(271, 36)		1
(272, 2)		FALSE
(272, 3)		
(272, 33)		CI of Mean Difference Minimum
(272, 34)		
(272, 35)		ci.minimum
(272, 36)		1
(273, 2)		FALSE
(273, 3)		
(273, 33)		CI of Mean Difference Maximum
(273, 34)		
(273, 35)		ci.maximum
(273, 36)		1
(274, 2)		FALSE
(274, 3)		
(274, 33)		t Statistic
(274, 34)		
(274, 35)		t
(274, 36)		1
(275, 2)		FALSE
(275, 3)		
(275, 33)		Degrees of Freedom
(275, 34)		
(275, 35)		df
(275, 36)		1
(276, 2)		FALSE
(276, 3)		label the x and y axis on the scatter plot
(276, 33)		Two-tailed p
(276, 34)		
(276, 35)		p
(276, 36)		1
(277, 2)		FALSE
(277, 3)		label the x axis on the residual plot
(277, 33)		N
(277, 34)		
(277, 35)		n.3
(277, 36)		1
(278, 2)		FALSE
(278, 3)		scale the x axis on the residual plot
(278, 33)		Sum
(278, 34)		
(278, 35)		sum.3
(278, 36)		1
(279, 2)		FALSE
(279, 3)		scale the x and y axis on the scatter plot
(279, 33)		Mean
(279, 34)		
(279, 35)		mean.3
(279, 36)		1
(280, 2)		FALSE
(280, 3)		
(280, 33)		Variance
(280, 34)		
(280, 35)		variance.3
(280, 36)		1
(281, 33)		Standard Deviation
(281, 34)		
(281, 35)		sd.3
(281, 36)		1
(282, 2)		MultipleLinearRegression
(282, 3)		performs multiple linear regression
(282, 33)		Standard Error
(282, 34)		
(282, 35)		se.3
(282, 36)		1
(283, 2)		FALSE
(283, 3)		
(283, 33)		Skewness
(283, 34)		
(283, 35)		skewness.3
(283, 36)		1
(284, 2)		#NAME?
(284, 3)		
(284, 33)		Kurtosis
(284, 34)		
(284, 35)		kurtosis.3
(284, 36)		1
(285, 2)		TRUE
(285, 3)		
(286, 2)		FALSE
(286, 3)		
(286, 32)		styles.wilcoxon
(286, 33)		Statistics
(286, 34)		BASE.XLA!AH290:AK302
(287, 2)		FALSE
(287, 3)		
(287, 33)		Dataset 1
(287, 34)		BASE.XLA!AH185:AK199
(288, 2)		FALSE
(288, 3)		
(288, 33)		Dataset 2
(288, 34)		BASE.XLA!AH201:AK215
(289, 2)		FALSE
(289, 3)		
(289, 33)		<No name defined>
(289, 34)		
(289, 35)		
(289, 36)		
(290, 2)		FALSE
(290, 3)		scale the x axis on the residual plot
(290, 33)		Positive N
(290, 34)		
(290, 35)		n.3
(290, 36)		1
(291, 2)		FALSE
(291, 3)		
(291, 33)		Positive Rank Sum
(291, 34)		
(291, 35)		sum.3
(291, 36)		1
(292, 2)		FALSE
(292, 3)		
(292, 33)		Positive Mean Rank
(292, 34)		
(292, 35)		mean.3
(292, 36)		1
(293, 33)		Negative N
(293, 34)		
(293, 35)		n.4
(293, 36)		1
(294, 2)		KendallTest
(294, 3)		performs kendall coefficient
(294, 33)		Negative Rank Sum
(294, 34)		
(294, 35)		sum.4
(294, 36)		1
(295, 2)		FALSE
(295, 3)		
(295, 33)		Negative Mean Rank
(295, 34)		
(295, 35)		mean.4
(295, 36)		1
(296, 2)		#NAME?
(296, 3)		
(296, 33)		Excluded N
(296, 34)		
(296, 35)		n.5
(296, 36)		1
(297, 2)		FALSE
(297, 3)		
(297, 33)		Wilcoxon R
(297, 34)		
(297, 35)		sum.5
(297, 36)		1
(298, 2)		FALSE
(298, 3)		
(298, 33)		z Statistic
(298, 34)		
(298, 35)		z
(298, 36)		1
(299, 2)		FALSE
(299, 3)		
(299, 33)		Two-tailed p
(299, 34)		
(299, 35)		p
(299, 36)		1
(300, 2)		FALSE
(300, 3)		
(300, 33)		Corrected for Ties Message
(300, 34)		
(300, 35)		message.1
(300, 36)		1
(301, 2)		FALSE
(301, 3)		
(301, 33)		Inaccurate p Value Message
(301, 34)		
(301, 35)		message
(301, 36)		1
(302, 2)		FALSE
(302, 3)		label the x and y axis on the scatter plot
(303, 2)		FALSE
(303, 3)		scale the x and y axis on the scatter plot
(303, 32)		styles.wilcoxon.mann.whitney
(303, 33)		Statistics
(303, 34)		BASE.XLA!AH307:AK312
(304, 2)		FALSE
(304, 3)		
(304, 33)		Dataset 1
(304, 34)		BASE.XLA!AH313:AK330
(305, 33)		Dataset 2
(305, 34)		BASE.XLA!AH331:AK348
(306, 2)		PearsonTest
(306, 3)		performs pearson coefficient
(306, 33)		<No name defined>
(306, 34)		
(306, 35)		
(306, 36)		
(307, 2)		FALSE
(307, 3)		
(307, 33)		Mann-Whitney U Statistic
(307, 34)		
(307, 35)		u.3
(307, 36)		1
(308, 2)		#NAME?
(308, 3)		
(308, 33)		z Statistic
(308, 34)		
(308, 35)		z
(308, 36)		1
(309, 2)		FALSE
(309, 3)		
(309, 33)		Two-tailed p
(309, 34)		
(309, 35)		p
(309, 36)		1
(310, 2)		FALSE
(310, 3)		
(310, 33)		Corrected for Ties Message
(310, 34)		
(310, 35)		message.1
(310, 36)		1
(311, 2)		FALSE
(311, 3)		
(311, 33)		Inaccurate p Value Message
(311, 34)		
(311, 35)		message
(311, 36)		1
(312, 2)		FALSE
(312, 3)		
(312, 33)		Variable Name
(312, 34)		
(312, 35)		variable.1
(312, 36)		1
(313, 2)		FALSE
(313, 3)		
(313, 33)		Dataset
(313, 34)		
(313, 35)		dataset.1
(313, 36)		2
(314, 2)		FALSE
(314, 3)		label the x and y axis on the scatter plot
(314, 33)		N
(314, 34)		
(314, 35)		n.1
(314, 36)		1
(315, 2)		FALSE
(315, 3)		scale the x and y axis on the scatter plot
(315, 33)		Rank Sum
(315, 34)		
(315, 35)		sum.3
(315, 36)		1
(316, 2)		FALSE
(316, 3)		
(316, 33)		Mean Rank
(316, 34)		
(316, 35)		mean.3
(316, 36)		1
(317, 33)		Mann-Whitney U Statistic
(317, 34)		
(317, 35)		u.1
(317, 36)		1
(318, 6)		
(318, 33)		Sum
(318, 34)		
(318, 35)		sum.1
(318, 36)		1
(319, 1)		
(319, 2)		Transform macros
(319, 3)		
(319, 6)		
(319, 33)		Mean
(319, 34)		
(319, 35)		mean.1
(319, 36)		1
(320, 6)		
(320, 33)		Variance
(320, 34)		
(320, 35)		variance.1
(320, 36)		1
(321, 2)		Log10Transform
(321, 3)		performs log10 transform
(321, 6)		
(321, 33)		Standard Deviation
(321, 34)		
(321, 35)		sd.1
(321, 36)		1
(322, 2)		FALSE
(322, 3)		
(322, 6)		
(322, 33)		Standard Error
(322, 34)		
(322, 35)		se.1
(322, 36)		1
(323, 2)		#NAME?
(323, 3)		
(323, 6)		
(323, 33)		Skewness
(323, 34)		
(323, 35)		skewness.1
(323, 36)		1
(324, 2)		TRUE
(324, 3)		
(324, 6)		
(324, 33)		Kurtosis
(324, 34)		
(324, 35)		kurtosis.1
(324, 36)		1
(325, 6)		
(325, 33)		Minimum
(325, 34)		
(325, 35)		minimum.1
(325, 36)		1
(326, 2)		LnTransform
(326, 3)		performs ln transform
(326, 6)		
(326, 33)		Lower Percentile
(326, 34)		
(326, 35)		lower.pcntl.1
(326, 36)		1
(327, 2)		FALSE
(327, 3)		
(327, 6)		
(327, 33)		Median
(327, 34)		
(327, 35)		median.1
(327, 36)		1
(328, 2)		#NAME?
(328, 3)		
(328, 6)		
(328, 33)		Upper Percentile
(328, 34)		
(328, 35)		upper.pcntl.1
(328, 36)		1
(329, 2)		TRUE
(329, 3)		
(329, 6)		
(329, 33)		Maximum
(329, 34)		
(329, 35)		maximum.1
(329, 36)		1
(330, 6)		
(330, 33)		Variable Name
(330, 34)		
(330, 35)		variable.2
(330, 36)		1
(331, 2)		ExpTransform
(331, 3)		performs exp transform
(331, 6)		
(331, 33)		Dataset
(331, 34)		
(331, 35)		dataset.2
(331, 36)		2
(332, 2)		FALSE
(332, 3)		
(332, 6)		
(332, 33)		N
(332, 34)		
(332, 35)		n.2
(332, 36)		1
(333, 2)		#NAME?
(333, 3)		
(333, 6)		
(333, 33)		Rank Sum
(333, 34)		
(333, 35)		sum.4
(333, 36)		1
(334, 2)		TRUE
(334, 3)		
(334, 6)		
(334, 33)		Mean Rank
(334, 34)		
(334, 35)		mean.4
(334, 36)		1
(335, 6)		
(335, 33)		Mann-Whitney U Statistic
(335, 34)		
(335, 35)		u.2
(335, 36)		1
(336, 2)		ReciprocalTransform
(336, 3)		performs 1/x transform
(336, 6)		
(336, 33)		Sum
(336, 34)		
(336, 35)		sum.2
(336, 36)		1
(337, 1)		
(337, 2)		FALSE
(337, 3)		
(337, 6)		
(337, 33)		Mean
(337, 34)		
(337, 35)		mean.2
(337, 36)		1
(338, 2)		#NAME?
(338, 3)		
(338, 6)		
(338, 33)		Variance
(338, 34)		
(338, 35)		variance.2
(338, 36)		1
(339, 2)		TRUE
(339, 3)		
(339, 6)		
(339, 33)		Standard Deviation
(339, 34)		
(339, 35)		sd.2
(339, 36)		1
(340, 6)		
(340, 33)		Standard Error
(340, 34)		
(340, 35)		se.2
(340, 36)		1
(341, 2)		SquaredTransform
(341, 3)		performs squared transform
(341, 6)		
(341, 33)		Skewness
(341, 34)		
(341, 35)		kurtosis.2
(341, 36)		1
(342, 2)		FALSE
(342, 3)		
(342, 6)		
(342, 33)		Kurtosis
(342, 34)		
(342, 35)		skewness.2
(342, 36)		1
(343, 2)		#NAME?
(343, 3)		
(343, 6)		
(343, 33)		Minimum
(343, 34)		
(343, 35)		minimum.2
(343, 36)		1
(344, 1)		
(344, 2)		TRUE
(344, 3)		
(344, 6)		
(344, 33)		Lower Percentile
(344, 34)		
(344, 35)		lower.pcntl.2
(344, 36)		1
(345, 1)		
(345, 6)		
(345, 33)		Median
(345, 34)		
(345, 35)		median.2
(345, 36)		1
(346, 2)		CubedTransform
(346, 3)		performs cubed transform
(346, 6)		
(346, 33)		Upper Percentile
(346, 34)		
(346, 35)		upper.pcntl.2
(346, 36)		1
(347, 1)		
(347, 2)		FALSE
(347, 3)		
(347, 6)		
(347, 33)		Maximum
(347, 34)		
(347, 35)		maximum.2
(347, 36)		1
(348, 1)		
(348, 2)		#NAME?
(348, 3)		
(348, 6)		
(349, 2)		TRUE
(349, 3)		
(349, 6)		
(349, 32)		styles.linear.regression
(349, 33)		Regression Statistics
(349, 34)		BASE.XLA!AH355:AK369
(350, 6)		
(350, 33)		ANOVA Statistics
(350, 34)		BASE.XLA!AH370:AK379
(351, 2)		SqrtTransform
(351, 3)		performs sqrt transform
(351, 6)		
(351, 33)		Fitted and Residual Graph
(351, 34)		BASE.XLA!AH384:AK388
(352, 2)		FALSE
(352, 3)		
(352, 6)		
(352, 33)		Dataset X
(352, 34)		BASE.XLA!AH380:AK381
(353, 2)		#NAME?
(353, 3)		
(353, 6)		
(353, 33)		Dataset Y
(353, 34)		BASE.XLA!AH382:AK383
(354, 2)		TRUE
(354, 3)		
(354, 6)		
(354, 33)		<No name defined>
(354, 34)		
(354, 35)		
(354, 36)		
(355, 6)		
(355, 33)		N
(355, 34)		
(355, 35)		n
(355, 36)		1
(356, 2)		AntiLogTransform
(356, 3)		performs antilog transform
(356, 6)		
(356, 33)		R Square
(356, 34)		
(356, 35)		multiple.r.2
(356, 36)		1
(357, 2)		FALSE
(357, 3)		
(357, 6)		
(357, 33)		Intercept Coefficient
(357, 34)		
(357, 35)		estimate.1
(357, 36)		1
(358, 2)		#NAME?
(358, 3)		
(358, 6)		
(358, 33)		CI of Intercept Minimum
(358, 34)		
(358, 35)		ci.minimum.1
(358, 36)		1
(359, 2)		TRUE
(359, 3)		
(359, 33)		CI of Intercept Maximum
(359, 34)		
(359, 35)		ci.maximum.1
(359, 36)		1
(360, 33)		Intercept Standard Error
(360, 34)		
(360, 35)		se.1
(360, 36)		1
(361, 2)		ZTransform
(361, 3)		performs z transform
(361, 33)		Intercept t Statistic
(361, 34)		
(361, 35)		t.1
(361, 36)		1
(362, 2)		FALSE
(362, 3)		
(362, 33)		Intercept Two-tailed p
(362, 34)		
(362, 35)		p.1
(362, 36)		1
(363, 2)		#NAME?
(363, 3)		
(363, 33)		Slope Coefficient
(363, 34)		
(363, 35)		estimate.2
(363, 36)		1
(364, 2)		TRUE
(364, 3)		
(364, 33)		CI of Slope Minimum
(364, 34)		
(364, 35)		ci.minimum.2
(364, 36)		1
(365, 2)		FALSE
(365, 3)		
(365, 33)		CI of Slope Maximum
(365, 34)		
(365, 35)		ci.maximum.2
(365, 36)		1
(366, 2)		FALSE
(366, 3)		compute the mean of the range
(366, 33)		Slope Standard Error
(366, 34)		
(366, 35)		se.2
(366, 36)		1
(367, 2)		FALSE
(367, 3)		compute the sd of the range
(367, 33)		Slope t Statistic
(367, 34)		
(367, 35)		t.2
(367, 36)		1
(368, 2)		TRUE
(368, 3)		
(368, 33)		Slope Two-tailed p
(368, 34)		
(368, 35)		p.2
(368, 36)		1
(369, 33)		Regreesion Sum of Squares
(369, 34)		
(369, 35)		ss.3
(369, 36)		1
(370, 2)		FormulaTransform
(370, 3)		performs specified formula
(370, 33)		Regression Degrees of Freedom
(370, 34)		
(370, 35)		df.3
(370, 36)		1
(371, 2)		FALSE
(371, 3)		
(371, 33)		Regression Mean Square
(371, 34)		
(371, 35)		ms.3
(371, 36)		1
(372, 2)		#NAME?
(372, 3)		
(372, 33)		Regression F Statistic
(372, 34)		
(372, 35)		f.3
(372, 36)		1
(373, 2)		TRUE
(373, 3)		
(373, 33)		Regression p
(373, 34)		
(373, 35)		p.3
(373, 36)		1
(374, 33)		Residual Sum of Squares
(374, 34)		
(374, 35)		ss.4
(374, 36)		1
(375, 2)		DisplayTransformMenu
(375, 3)		displays the transform submenu
(375, 33)		Residual Degrees of Freedom
(375, 34)		
(375, 35)		df.4
(375, 36)		1
(376, 2)		FALSE
(376, 3)		
(376, 33)		Residual Mean Square
(376, 34)		
(376, 35)		ms.4
(376, 36)		1
(377, 2)		TRUE
(377, 3)		send the keys to display the menu
(377, 33)		Total Sum of Squares
(377, 34)		
(377, 35)		ss.5
(377, 36)		1
(378, 2)		TRUE
(378, 3)		
(378, 33)		Total Degrees of Freedom
(378, 34)		
(378, 35)		df.5
(378, 36)		1
(379, 33)		Variable Name
(379, 34)		
(379, 35)		variable.1
(379, 36)		1
(380, 33)		Dataset
(380, 34)		
(380, 35)		dataset.1
(380, 36)		2
(381, 33)		Variable Name
(381, 34)		
(381, 35)		variable.2
(381, 36)		1
(382, 33)		Dataset
(382, 34)		
(382, 35)		dataset.2
(382, 36)		2
(383, 1)		
(383, 2)		Miscellaneous
(383, 3)		
(383, 33)		X Datapoints
(383, 34)		
(383, 35)		line.x
(383, 36)		2
(384, 33)		Y Datapoints
(384, 34)		
(384, 35)		line.y
(384, 36)		2
(385, 2)		LabelGraph
(385, 3)		
(385, 33)		Fitted Y
(385, 34)		
(385, 35)		line.fitted
(385, 36)		2
(386, 2)		TRUE
(386, 3)		
(386, 33)		Residual
(386, 34)		
(386, 35)		line.residual
(386, 36)		2
(387, 2)		TRUE
(387, 3)		
(387, 33)		Standardised Residual
(387, 34)		
(387, 35)		line.stdz.residual
(387, 36)		2
(388, 2)		TRUE
(388, 3)		
(389, 2)		TRUE
(389, 3)		
(389, 32)		styles.pearson.correlation
(389, 33)		Statistics
(389, 34)		BASE.XLA!AH394:AK401
(390, 2)		TRUE
(390, 3)		switch of error reporting
(390, 33)		Scatter Graph
(390, 34)		BASE.XLA!AH402:AK403
(391, 2)		TRUE
(391, 3)		select the chart specified
(391, 33)		Dataset X
(391, 34)		BASE.XLA!AH185:AK199
(392, 2)		TRUE
(392, 3)		name the x axis
(392, 33)		Dataset Y
(392, 34)		BASE.XLA!AH201:AK215
(393, 2)		FALSE
(393, 3)		
(393, 33)		<No name defined>
(393, 34)		
(393, 35)		
(393, 36)		
(394, 2)		FALSE
(394, 3)		
(394, 33)		N
(394, 34)		
(394, 35)		n
(394, 36)		1
(395, 2)		TRUE
(395, 3)		
(395, 33)		r Statistic
(395, 34)		
(395, 35)		pearson.r
(395, 36)		1
(396, 2)		TRUE
(396, 3)		name the y axis
(396, 33)		CI of r Minimum
(396, 34)		
(396, 35)		ci.minimum
(396, 36)		1
(397, 2)		FALSE
(397, 3)		
(397, 33)		CI of r Maximum
(397, 34)		
(397, 35)		ci.maximum
(397, 36)		1
(398, 2)		FALSE
(398, 3)		
(398, 33)		t Statistic
(398, 34)		
(398, 35)		t
(398, 36)		1
(399, 2)		TRUE
(399, 3)		
(399, 33)		Degrees of Freedom
(399, 34)		
(399, 35)		df
(399, 36)		1
(400, 2)		TRUE
(400, 3)		re-activate the results worksheet
(400, 33)		Two-tailed p
(400, 34)		
(400, 35)		p
(400, 36)		1
(401, 1)		
(401, 2)		TRUE
(401, 3)		
(401, 33)		X Datapoints
(401, 34)		
(401, 35)		line.x
(401, 36)		2
(402, 33)		Y Datapoints
(402, 34)		
(402, 35)		line.y
(402, 36)		2
(403, 2)		ScaleScatterGraph
(403, 3)		
(404, 2)		TRUE
(404, 3)		
(404, 32)		styles.kendall.correlation
(404, 33)		Statistics
(404, 34)		BASE.XLA!AH409:AK417
(405, 2)		TRUE
(405, 3)		
(405, 33)		Scatter Graph
(405, 34)		BASE.XLA!AH402:AK403
(406, 2)		TRUE
(406, 3)		switch of error reporting
(406, 33)		Dataset X
(406, 34)		BASE.XLA!AH185:AK199
(407, 2)		TRUE
(407, 3)		select the chart specified
(407, 33)		Dataset Y
(407, 34)		BASE.XLA!AH201:AK215
(408, 2)		FALSE
(408, 3)		select x axis
(408, 33)		<No name defined>
(408, 34)		
(408, 35)		
(408, 36)		
(409, 2)		FALSE
(409, 3)		scale the axis to the minimum and maximum
(409, 33)		N
(409, 34)		
(409, 35)		n
(409, 36)		1
(410, 2)		FALSE
(410, 3)		select y axis
(410, 33)		tau Statistic
(410, 34)		
(410, 35)		tau
(410, 36)		1
(411, 2)		FALSE
(411, 3)		scale the axis to the minimum and maximum
(411, 33)		CI of tau Minimum
(411, 34)		
(411, 35)		ci.minimum
(411, 36)		1
(412, 2)		TRUE
(412, 3)		re-activate the results worksheet
(412, 33)		CI of tau Maximum
(412, 34)		
(412, 35)		ci.maximum
(412, 36)		1
(413, 1)		
(413, 2)		TRUE
(413, 3)		
(413, 33)		z Statistic
(413, 34)		
(413, 35)		z
(413, 36)		1
(414, 33)		Two-tailed p
(414, 34)		
(414, 35)		p
(414, 36)		1
(415, 2)		ScaleMultipleDescriptiveCharts
(415, 3)		
(415, 33)		Corrected for Ties Message
(415, 34)		
(415, 35)		message.1
(415, 36)		1
(416, 2)		TRUE
(416, 3)		
(416, 33)		Inaccurate p Value Message
(416, 34)		
(416, 35)		message
(416, 36)		1
(417, 2)		TRUE
(417, 3)		switch of error reporting
(418, 2)		FALSE
(418, 3)		select the box and whisker plot
(418, 32)		styles.spearman.correlation
(418, 33)		Statistics
(418, 34)		BASE.XLA!AH423:AK432
(419, 2)		FALSE
(419, 3)		select y axis
(419, 33)		Scatter Graph
(419, 34)		BASE.XLA!AH402:AK403
(420, 2)		FALSE
(420, 3)		scale the axis to the minimum and maximum
(420, 33)		X Dataset
(420, 34)		BASE.XLA!AH185:AK199
(421, 2)		FALSE
(421, 3)		select the errorbar plot
(421, 33)		Y Dataset
(421, 34)		BASE.XLA!AH201:AK215
(422, 2)		FALSE
(422, 3)		select y axis
(422, 33)		<No name defined>
(422, 34)		
(422, 35)		
(422, 36)		
(423, 2)		FALSE
(423, 3)		scale the axis to the minimum and maximum
(423, 33)		N
(423, 34)		
(423, 35)		n
(423, 36)		1
(424, 2)		TRUE
(424, 3)		re-activate the results worksheet
(424, 33)		rs Statistic
(424, 34)		
(424, 35)		rs
(424, 36)		1
(425, 1)		
(425, 2)		TRUE
(425, 3)		
(425, 33)		CI of rs Minimum
(425, 34)		
(425, 35)		ci.minimum
(425, 36)		1
(426, 33)		CI of rs Maximum
(426, 34)		
(426, 35)		ci.maximum
(426, 36)		1
(427, 2)		ScaleResidualGraph
(427, 3)		
(427, 33)		t Statistic
(427, 34)		
(427, 35)		t
(427, 36)		1
(428, 2)		TRUE
(428, 3)		
(428, 33)		Degrees of Freedom
(428, 34)		
(428, 35)		df
(428, 36)		1
(429, 2)		TRUE
(429, 3)		
(429, 33)		Two-tailed p
(429, 34)		
(429, 35)		p
(429, 36)		1
(430, 2)		TRUE
(430, 3)		switch of error reporting
(430, 33)		Corrected for Ties Message
(430, 34)		
(430, 35)		message.1
(430, 36)		1
(431, 2)		TRUE
(431, 3)		select the chart specified
(431, 33)		Inaccurate p Value Message
(431, 34)		
(431, 35)		message
(431, 36)		1
(432, 2)		FALSE
(432, 3)		select x axis
(433, 2)		FALSE
(433, 3)		scale the axis to the minimum and maximum
(433, 32)		styles.multpile.linear.regression
(433, 33)		Regression Statistics
(433, 34)		BASE.XLA!AH439:AK456
(434, 2)		TRUE
(434, 3)		re-activate the results worksheet
(434, 33)		ANOVA Statistics
(434, 34)		BASE.XLA!AH370:AK379
(435, 1)		
(435, 2)		TRUE
(435, 3)		
(435, 33)		Fitted and Residual Graph
(435, 34)		BASE.XLA!AH384:AK388
(436, 33)		Dataset X
(436, 34)		BASE.XLA!AH457:AK458
(437, 33)		Dataset Y
(437, 34)		BASE.XLA!AH382:AK383
(438, 33)		<No name defined>
(438, 34)		
(438, 35)		
(438, 36)		
(439, 33)		N
(439, 34)		
(439, 35)		n
(439, 36)		1
(440, 1)		
(440, 2)		significant result sample size calculators
(440, 3)		
(440, 33)		Multiple R
(440, 34)		
(440, 35)		multiple.r.1
(440, 36)		1
(441, 1)		
(441, 33)		R Square
(441, 34)		
(441, 35)		multiple.r.2
(441, 36)		1
(442, 2)		DisplaySampleSizeMenu
(442, 3)		displays the sample size menu
(442, 33)		Adjusted R Square
(442, 34)		
(442, 35)		multiple.r.3
(442, 36)		1
(443, 2)		FALSE
(443, 3)		
(443, 33)		Standard Error
(443, 34)		
(443, 35)		se
(443, 36)		1
(444, 2)		FALSE
(444, 3)		send the keys to display the menu
(444, 33)		Intercept Coefficient
(444, 34)		
(444, 35)		estimate.1
(444, 36)		1
(445, 2)		TRUE
(445, 3)		
(445, 33)		CI of Intercept Minimum
(445, 34)		
(445, 35)		ci.minimum.1
(445, 36)		1
(446, 33)		CI of Intercept Maximum
(446, 34)		
(446, 35)		ci.maximum.1
(446, 36)		1
(447, 2)		None
(447, 3)		does nothing
(447, 33)		Intercept Standard Error
(447, 34)		
(447, 35)		se.1
(447, 36)		1
(448, 2)		TRUE
(448, 3)		
(448, 33)		Intercept t Statistic
(448, 34)		
(448, 35)		t.1
(448, 36)		1
(449, 33)		Intercept Two-tailed p
(449, 34)		
(449, 35)		p.1
(449, 36)		1
(450, 33)		Slope Coefficient
(450, 34)		
(450, 35)		estimate.2
(450, 36)		2
(451, 2)		SignificiantResultSingleMean
(451, 3)		
(451, 33)		CI of Slope Minimum
(451, 34)		
(451, 35)		ci.minimum.2
(451, 36)		2
(452, 2)		FALSE
(452, 3)		reset the fillin fields to empty
(452, 33)		CI of Slope Maximum
(452, 34)		
(452, 35)		ci.maximum.2
(452, 36)		2
(453, 2)		FALSE
(453, 3)		
(453, 33)		Slope Standard Error
(453, 34)		
(453, 35)		se.2
(453, 36)		2
(454, 2)		TRUE
(454, 3)		
(454, 33)		Slope t Statistic
(454, 34)		
(454, 35)		t.2
(454, 36)		2
(455, 2)		TRUE
(455, 3)		
(455, 33)		Slope Two-tailed p
(455, 34)		
(455, 35)		p.2
(455, 36)		2
(456, 2)		TRUE
(456, 3)		
(456, 33)		Variable Names
(456, 34)		
(456, 35)		variable.2
(456, 36)		2
(457, 2)		TRUE
(457, 3)		
(457, 33)		Dataset
(457, 34)		
(457, 35)		dataset.2
(457, 36)		2x2
(458, 2)		TRUE
(458, 3)		display the dialog box
(459, 2)		FALSE
(459, 3)		validate the power is between 0 and 100
(460, 2)		TRUE
(460, 3)		
(461, 2)		FALSE
(461, 3)		validate the significance level is between 0 and 100
(462, 2)		TRUE
(462, 3)		
(463, 2)		TRUE
(463, 3)		
(464, 2)		TRUE
(464, 3)		setup names for the parameters entered into the dialog box
(465, 2)		TRUE
(465, 3)		
(466, 2)		TRUE
(466, 3)		
(467, 2)		TRUE
(467, 3)		
(468, 2)		#NAME?
(468, 3)		compute the sample size
(469, 2)		TRUE
(469, 3)		fillin the sample size field in the dialog box
(470, 2)		TRUE
(470, 3)		
(471, 2)		TRUE
(471, 3)		
(472, 2)		TRUE
(472, 3)		end of macro close the dialog box
(473, 2)		FALSE
(473, 3)		
(474, 2)		TRUE
(474, 3)		
(477, 2)		SignificiantResultSingleProportion
(477, 3)		
(478, 2)		FALSE
(478, 3)		reset the fillin fields to empty
(479, 2)		FALSE
(479, 3)		
(480, 2)		TRUE
(480, 3)		
(481, 2)		TRUE
(481, 3)		
(482, 2)		TRUE
(482, 3)		
(483, 2)		TRUE
(483, 3)		
(484, 2)		TRUE
(484, 3)		display the dialog box
(485, 2)		FALSE
(485, 3)		validate the power is between 0 and 100
(486, 2)		TRUE
(486, 3)		
(487, 2)		FALSE
(487, 3)		validate the significance level is between 0 and 100
(488, 2)		TRUE
(488, 3)		
(489, 2)		TRUE
(489, 3)		
(490, 2)		TRUE
(490, 3)		setup names for the parameters entered into the dialog box
(491, 2)		TRUE
(491, 3)		
(492, 2)		TRUE
(492, 3)		
(493, 2)		TRUE
(493, 3)		
(494, 2)		61.4076676975953
(494, 3)		compute the sample size
(495, 2)		TRUE
(495, 3)		fillin the sample size field in the dialog box
(496, 2)		TRUE
(496, 3)		
(497, 2)		TRUE
(497, 3)		
(498, 2)		TRUE
(498, 3)		end of macro close the dialog box
(499, 2)		FALSE
(499, 3)		
(500, 2)		TRUE
(500, 3)		
(503, 2)		SignificantResultComparisonTwoMeans
(503, 3)		
(504, 2)		FALSE
(504, 3)		reset the fillin fields to empty
(505, 2)		FALSE
(505, 3)		
(506, 2)		TRUE
(506, 3)		
(507, 2)		TRUE
(507, 3)		
(508, 2)		TRUE
(508, 3)		
(509, 2)		TRUE
(509, 3)		
(510, 2)		TRUE
(510, 3)		
(511, 2)		TRUE
(511, 3)		display the dialog box
(512, 2)		FALSE
(512, 3)		validate the power is between 0 and 100
(513, 2)		TRUE
(513, 3)		
(514, 2)		FALSE
(514, 3)		validate the significance level is between 0 and 100
(515, 2)		TRUE
(515, 3)		
(516, 2)		TRUE
(516, 3)		
(517, 2)		TRUE
(517, 3)		setup names for the parameters entered into the dialog box
(518, 2)		TRUE
(518, 3)		
(519, 2)		TRUE
(519, 3)		
(520, 2)		TRUE
(520, 3)		
(521, 2)		TRUE
(521, 3)		
(522, 2)		#NUM!
(522, 3)		compute the sample size
(523, 2)		TRUE
(523, 3)		fillin the sample size field in the dialog box
(524, 2)		TRUE
(524, 3)		
(525, 2)		TRUE
(525, 3)		
(526, 2)		TRUE
(526, 3)		end of macro close the dialog box
(527, 2)		FALSE
(527, 3)		
(528, 2)		TRUE
(528, 3)		
(531, 2)		SignificiantResultComparisonTwoProportions
(531, 3)		
(532, 2)		FALSE
(532, 3)		reset the fillin fields to empty
(533, 2)		FALSE
(533, 3)		
(534, 2)		TRUE
(534, 3)		
(535, 2)		TRUE
(535, 3)		
(536, 2)		TRUE
(536, 3)		
(537, 2)		TRUE
(537, 3)		
(538, 2)		TRUE
(538, 3)		display the dialog box
(539, 2)		FALSE
(539, 3)		validate the power is between 0 and 100
(540, 2)		TRUE
(540, 3)		
(541, 2)		FALSE
(541, 3)		validate the significance level is between 0 and 100
(542, 2)		TRUE
(542, 3)		
(543, 2)		TRUE
(543, 3)		
(544, 2)		TRUE
(544, 3)		setup names for the parameters entered into the dialog box
(545, 2)		TRUE
(545, 3)		
(546, 2)		TRUE
(546, 3)		
(547, 2)		123.998347804951
(547, 3)		compute the sample size
(548, 2)		TRUE
(548, 3)		fillin the sample size field in the dialog box
(549, 2)		TRUE
(549, 3)		
(550, 2)		TRUE
(550, 3)		
(551, 2)		TRUE
(551, 3)		end of macro close the dialog box
(552, 2)		FALSE
(552, 3)		
(553, 2)		TRUE
(553, 3)		
(556, 2)		SignificiantResultCaseControlStudy
(556, 3)		
(557, 2)		FALSE
(557, 3)		reset the fillin fields to empty
(558, 2)		FALSE
(558, 3)		
(559, 2)		TRUE
(559, 3)		
(560, 2)		TRUE
(560, 3)		
(561, 2)		TRUE
(561, 3)		
(562, 2)		TRUE
(562, 3)		
(563, 2)		TRUE
(563, 3)		display the dialog box
(564, 2)		FALSE
(564, 3)		validate the power is between 0 and 100
(565, 2)		TRUE
(565, 3)		
(566, 2)		FALSE
(566, 3)		validate the significance level is between 0 and 100
(567, 2)		TRUE
(567, 3)		
(568, 2)		TRUE
(568, 3)		
(569, 2)		TRUE
(569, 3)		setup names for the parameters entered into the dialog box
(570, 2)		TRUE
(570, 3)		
(571, 2)		TRUE
(571, 3)		
(572, 2)		TRUE
(572, 3)		
(573, 2)		TRUE
(573, 3)		
(574, 2)		TRUE
(574, 3)		
(575, 2)		176.539333262959
(575, 3)		compute the sample size
(576, 2)		TRUE
(576, 3)		fillin the sample size field in the dialog box
(577, 2)		TRUE
(577, 3)		
(578, 2)		TRUE
(578, 3)		
(579, 2)		TRUE
(579, 3)		end of macro close the dialog box
(580, 2)		FALSE
(580, 3)		
(581, 2)		TRUE
(581, 3)		
(584, 1)		
(584, 2)		precision sample size calculators
(584, 3)		
(586, 2)		PrecisionSingleMean
(586, 3)		
(587, 2)		TRUE
(587, 3)		reset the fillin fields to empty
(588, 2)		TRUE
(588, 3)		
(589, 2)		TRUE
(589, 3)		
(590, 2)		TRUE
(590, 3)		
(591, 2)		TRUE
(591, 3)		display the dialog box
(592, 2)		100
(592, 3)		compute the sample size
(593, 2)		TRUE
(593, 3)		fillin the sample size field in the dialog box
(594, 2)		TRUE
(594, 3)		
(595, 2)		TRUE
(595, 3)		end of macro close the dialog box
(596, 2)		FALSE
(596, 3)		
(597, 2)		TRUE
(597, 3)		
(600, 2)		PrecisionSingleProportion
(600, 3)		
(601, 2)		TRUE
(601, 3)		reset the fillin fields to empty
(602, 2)		TRUE
(602, 3)		
(603, 2)		TRUE
(603, 3)		
(604, 2)		TRUE
(604, 3)		
(605, 2)		TRUE
(605, 3)		display the dialog box
(606, 2)		70
(606, 3)		compute the sample size
(607, 2)		TRUE
(607, 3)		fillin the sample size field in the dialog box
(608, 2)		TRUE
(608, 3)		
(609, 2)		TRUE
(609, 3)		end of macro close the dialog box
(610, 2)		FALSE
(610, 3)		
(611, 2)		TRUE
(611, 3)		
(614, 2)		PrecisionDifferenceBetweenTwoMeans
(614, 3)		
(615, 2)		TRUE
(615, 3)		reset the fillin fields to empty
(616, 2)		TRUE
(616, 3)		
(617, 2)		TRUE
(617, 3)		
(618, 2)		TRUE
(618, 3)		
(619, 2)		TRUE
(619, 3)		
(620, 2)		TRUE
(620, 3)		display the dialog box
(621, 2)		37.7500692092109
(621, 3)		compute the sample size
(622, 2)		TRUE
(622, 3)		fillin the sample size field in the dialog box
(623, 2)		TRUE
(623, 3)		
(624, 2)		TRUE
(624, 3)		end of macro close the dialog box
(625, 2)		FALSE
(625, 3)		
(626, 2)		TRUE
(626, 3)		
(629, 2)		PrecisionDifferenceBetweenTwoProportions
(629, 3)		
(630, 2)		TRUE
(630, 3)		reset the fillin fields to empty
(631, 2)		TRUE
(631, 3)		
(632, 2)		TRUE
(632, 3)		
(633, 2)		TRUE
(633, 3)		
(634, 2)		TRUE
(634, 3)		
(635, 2)		TRUE
(635, 3)		display the dialog box
(636, 2)		46
(636, 3)		compute the sample size
(637, 2)		TRUE
(637, 3)		fillin the sample size field in the dialog box
(638, 2)		TRUE
(638, 3)		
(639, 2)		TRUE
(639, 3)		end of macro close the dialog box
(640, 2)		#VALUE!
(640, 3)		
(641, 2)		TRUE
(641, 3)		
(792, 1)		
(796, 1)		
(797, 1)		
(798, 1)		
(799, 1)		
(800, 1)		
(801, 1)		
(802, 1)		
(803, 1)		
(804, 1)		
(805, 1)		
(806, 1)		
(807, 1)		
(808, 1)		
(809, 1)		
(810, 1)		
(811, 1)