Conditional Independence Test: sdmuc10
Observed No. training pts, n = 18
Expected No. of training points, T = 22.022
Difference, T-n = 4.022
Standard Deviation of T = 7.246

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Conditional Independence Ratio: 0.817 <simply the ratio n/T>
Values below 1.00 may indicate conditional dependence
among two or more of your data sets.  <Bonham-Carter(1994,ch.9)
suggest that values <0.85 may indicate a problem>

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Agterberg & Cheng Conditional Independence Test <See Agterberg and
Cheng, Natural Resources Research 11(4), 249-255, 2002>
This is a one-tailed test of the null hypothesis that T-n=0.  The test
statistic is (T-n)/standard deviation of T. Probability values greater
than 95% or 99% indicate that the hypothesis of CI should be rejected,
but any value greater than 50% indicates that some conditional
dependence occurs>

Probability that this model is not conditionally independent with
T-n/STD = 0.555 is 71.1%
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See the associated database table "sdmuc10_CI"
for T values of each unique condition. 

Evidence layers & Generalization tables
avg_perm_m_new_CD & Gen_avg_perm_m_new_CD
kst_buffm_new_CA & Gen_kst_buffm_new_CA
ovbdn_m_new_CA & Gen_ovbdn_m_new_CA

