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Dataplot Vol 2 Vol 1


    Library Function
    Compute the critical value for the normal probability plot correlation coefficient (PPCC).
    The PPCC value is a measure of distributional goodness of fit based on

    1. The linearity of the probability plot is a good measure of goodness of fit for a distribution.

    2. The correlation coefficient of the points on the probability plot is a good measure of the linearity of the probability plot.

    For a normal distribution, critical values for this statistic have been determined by simulation. The original tables were computed by Filliben and more extensive versions of the tables were computed by Devaney.

    The current tables are available for N = 3 to 1,000 and for signficance levels of 0.01 or 0.05 (the PPCC provides a lower tailed test). Significance levels of 0.99 and 0.95 are interpreted as 0.01 and 0.05, respectively.

    PPCC values less than the critical value reject the hypothesis of a normal distribution.

    LET <y> = NORPPCV(<n>,<alpha>)
    where <n> is a variable, parameter or number indicating the sample size;
    and <k> is a variable, parameter or number indicating the significance level;
    <y> is a variable or a parameter (depending on what <n> and <k> are) where the computed critical value is stored.
    LET A = NORPPCV(N,0.01)
    LET A = NORPPCV(N,0.05)
    LET A = NORPPCV(105,0.05)
    Significance levels other than 0.01, 0.05, 0.95, and 0.99 will use 0.05. Values for the sample size outside the range 3 to 1,000 will return an error message.
Related Commands: References:
    James J. Filliben (1975), "The Probability Plot Correlation Coefficient Test for Normality", Technometrics, Vol. 17, No. 1.

    Judy Devaney, Phd Thesis, George Mason University.

    Distributional Goodness of Fit
Implementation Date:
    . Step 1:   Read the data
    skip 25
    read zarr13.dat y
    . Step 2:   Generate normal probability plot with PPCC
    .           value and associated critical values
    let n = size y
    let alpha = 0.01
    let cv1 = norppcv(n,alpha)
    let cv1 = round(cv1,3)
    let alpha = 0.05
    let cv2 = norppcv(n,alpha)
    let cv2 = round(cv2,3)
    char circle
    char fill on
    char hw 0.5 0.375
    line blank
    y1label Sorted Data
    x1label Percentiles of Normal Distribution
    title Normal Probability Plot for ZARR13.DAT
    title case asis
    title offset 2
    label case asis
    ylimits 9.1  9.4
    major y1tic mark number 4
    normal probability plot y
    let ppcc = round(ppcc,3)
    let ppa0 = round(ppa0,3)
    let ppa1 = round(ppa1,3)
    case asis
    justification left
    move 16 88
    text Location: ^ppa0
    move 16 85
    text Scale: ^ppa1
    move 16 82
    text PPCC: ^ppcc
    move 16 79
    text 0.01 CV: ^cv1
    move 16 76
    text 0.05 CV: ^cv2

    plot generated by sample program

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Date created: 01/31/2015
Last updated: 01/31/2015

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