The CICA Windows Explosion!/The CICA Windows Explosion! - Disc 2.iso/excel/astute.zip/BASE.XL_/BASE.XL

Excel Spreadsheet | 1995-04-27 | 158KB | 5,198 lines

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
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(2, 4)
(2, 5)
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(2, 27)        balloon help
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(2, 30)
(2, 31)
(2, 33)        listbox item
(2, 34)        reference
(2, 35)        defined name
(2, 36)        type
(2, 37)
(2, 40)        test name
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(2, 43)        insert before
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(2, 48)        help
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(2, 50)        customize macro
(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)
(5, 41)        &Statistics
(5, 42)        FALSE
(5, 43)
(5, 44)        &Descriptive Statistics
(5, 45)
(5, 46)
(5, 47)
(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
(6, 17)        0
(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
(6, 42)        FALSE
(6, 43)
(6, 44)        T&ransform
(6, 45)
(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
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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
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(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
(10, 1)
(10, 2)
(10, 6)
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(10, 9)        44
(10, 10)        70
(10, 11)
(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
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(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
(11, 1)
(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
(12, 6)
(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
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(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
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(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)
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(98, 8)
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(98, 35)        p.1
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(99, 6)
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(101, 6)
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(101, 12)        Required Size of SE:
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(102, 33)        Dataset
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(105, 12)
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(106, 12)        Minimum sample size =
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(111, 12)        Proportion:
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(113, 6)
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(113, 12)        Required Size of SE:
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(114, 33)        Residual Mean Square
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(115, 12)        Compute
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(118, 12)        Minimum sample size =
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(158, 12)
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(181, 12)
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(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)