Can anyone tell me why the ncv.test output and the gvlma output would be
contradictory on the question of heteroscedasticity?  Below, the ncv.test
output reveals a problem with heteroscedasticity, but the gvlma output says
that the assumptions are acceptable.  How is this reconciled?

> ncv.test(defmodA)
Non-constant Variance Score Test
Variance formula: ~ fitted.values
Chisquare = 7.360374    Df = 1     p = 0.00666769

> gvlma(defmodA)

Call:
lm(formula = DefPunWmn1 ~ DefPersBenef, data = Data)

Coefficients:
 (Intercept)  DefPersBenef
      1.2579        0.1572


ASSESSMENT OF THE LINEAR MODEL ASSUMPTIONS
USING THE GLOBAL TEST ON 4 DEGREES-OF-FREEDOM:
Level of Significance =  0.05

Call:
 gvlma(x = defmodA)

                     Value   p-value                   Decision
Global Stat        37.3746 1.508e-07 Assumptions NOT satisfied!
Skewness           32.8916 9.744e-09 Assumptions NOT satisfied!
Kurtosis            2.6248 1.052e-01    Assumptions acceptable.
Link Function       0.3684 5.439e-01    Assumptions acceptable.
Heteroscedasticity  1.4899 2.222e-01    Assumptions acceptable.

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