sample size. Thus, in such situations the derived statistics are not necessarily the best

indicator of "goodness of fit". Examination of residuals helps to reaffirm the "goodness

of fit" of the regression equation in conjunction with the R2 value. This examination

involved plotting the residuals vs. the dependent variable. If the residuals exhibit a

random distribution and have a more or less even split above and below the zero line then

it is possible to say that the equation describes the relationship well (Kirkup, 2002)

 The other test requires formulating a null hypothesis and an alternate hypothesis,

which are then tested using ANOVA and t-statistic. This test helps to access the

suitability of the best-fit equation that describes the relationship between the variables

(Kirkup, 2002). The hypothesis test utilize in this analysis is a one-tailed ANOVA. In

this case the null and alternate hypotheses are,

 Ho: the equation has a zero slope;

 Ha: the equation has non-zero slope.

 Since the linear regression equation relates porosity to permeability using the slope

and the intercept of the equation, the null hypothesis is that of zero slope, which yields a

constant function. The hypothesis tests were performed at the 95% confidence interval.

 Results

 Permeability values were plotted on an outline of Neuzil's (1994) compiled range

of permeabilities as a function of porosity for argillaceous sediments (Figure 2-5). The

majority of permeability values were enveloped within Neuzil's (1994) plot. However,

several samples from Site 1231 of Peru and Sites 1039 and 1040 of Costa Rica plotted

outside Neuzil's (1994) plotted area. These include non-argillaceous sediments of

calcareous oozes and few samples containing siliceous oozes.