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Download the documentation - True BASIC

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74 Statistics Graphics Toolkit<br />

ANOVA Statistics Subscript Functions<br />

an_msw mean square within<br />

an_msb<br />

an_ssw<br />

an_ssb<br />

an_sst<br />

an_dfw<br />

an_dfb<br />

an_dft<br />

an_f<br />

an_p<br />

mean square between<br />

sum of squares within<br />

sum of squares between<br />

sum of squares total<br />

degrees of freedom within<br />

degrees of freedom between<br />

degrees of freedom total<br />

F-statistic<br />

Prob(F)<br />

PrintAnova (#n, d(,))<br />

PrintAnova computes a one-factor analysis of variance from <strong>the</strong> data points d(,) and<br />

prints an ANOVA table from <strong>the</strong> results. Each column of d(,) is taken as one dataset, so<br />

“within” refers to columns and “between” refers to rows. If you want to get fancier, try<br />

<strong>the</strong> PrintMultiRegress routine.<br />

Channel #n must already be open. It can refer to a window or a text file. Pass #0 to<br />

print in <strong>the</strong> current window.<br />

Exceptions:<br />

7004 Channel isn’t open.<br />

8501 Must be text file.<br />

MedFit (x(), y(), slope, inter, mad,resid())<br />

MedFit computes a “median” (least absolute deviation) linear fit of <strong>the</strong> dependent variable<br />

y() to <strong>the</strong> independent variable x(). The x() and y() arrays must have <strong>the</strong> same sizes<br />

but needn’t have <strong>the</strong> same upper and lower bounds; <strong>the</strong>y’re taken as paired data points<br />

(x i<br />

, y i<br />

) starting with <strong>the</strong> first element of each array. If ei<strong>the</strong>r number in a pair is missing,<br />

<strong>the</strong> pair is not used.<br />

MedFit is like LSFit except that it uses a more “robust” technique so that outliers have<br />

less effect on <strong>the</strong> fitted line. It is, however, significantly slower than LSFit.<br />

MedFit returns <strong>the</strong> slope and intercept of <strong>the</strong> fitted line in slope and inter, <strong>the</strong> mean<br />

absolute deviation mad, and <strong>the</strong> residuals in resid().<br />

Use SetLineFit to switch to using least-absolute-deviation fits instead of least-squares<br />

fits in scatter and regression plots.<br />

01/01

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