high correlation between the observed values and the predicted
values. This means that regression model provides an excellent
explanation of the relationship between the independent variables
(factors) and the response (bioethanol production). The
lack-of-fit term was non-significant as it was desired. The nonsignificant
value of lack of fit observed was 0.23 which was more
than the probability of 0.05 and this revealed that the quadratic
model was valid for the present study. Standard error plot was
also predicted and is presented in Fig. 1a–d.
The individual and interaction effects of incubation period
and temperature on the bioethanol production were tested.
Incubation period was significant but temperature and its
interactions were not significant on bioethanol production
(Table 2). The maximum bioethanol yield of 69.58% was obtained
at a temperature of 30 C in 89 h of incubation
(Fig. 2a).
The sawdust concentration was significant but the interaction
effect of the temperature and sawdust was not significant
on bioethanol production (Table 2). The maximum yield of
69.58% bioethanol was obtained with the sawdust concentration
of 6.84 mg l1 at 30 C (Fig. 2b).
The individual and interaction effects of the agitation and
temperature on the bioethanol production were tested. The
individual effect of the agitation was significant but its interaction
was not significant on the bioethanol production. Statistically,
bioethanol production was significantly increased with
increasing rotations per minute (Table 2; Fig. 2c).
Bioethanol production varied significantly between the sawdust
concentrations or incubation period (h) and also their
interaction and combined effects (Table 2). The maximum
yield of 69.58% bioethanol was obtained with the sawdust
concentration of 6.84 mg l1 at 89 h of incubation (Fig. 2d).