Objective of the Study
Durbin-Watson = 1.924
The broad objective of this study is
to determine labour cost analysis and transmission effect of cassava processing
in Ebonyi state.
The specific objective is to determine
the relationship between the socioeconomic characteristics of cassava processors and their level of utilization of cassava in the study area.
Hypothesis
The null hypothesis was used Ho: The socio economic characteristics of cassava
processor do not have significant difference on their level of cassava
utilization in the study area.
Data Analysis
Model
specification
Multiple
regression analysis were used to determine the socioeconomic characteristics of
cassava processors on their level cassava utilization in the study area.
The
model are stated as;
Y = (FX1,
X2, X3, X4, X4, X5, X6+
Implicit form
Y = a0+
a1 + a1 + a2 + a2 + a3 +
a3 + a4 + a4 + a5 + a5
Explicit function
Where;
Y = Level of
cassava utilization
X1 = Age
(years)
X2 = Marital status
X3 = Educational level (yrs)
X5 = Labour cost.
X6 = Faming experience 9yrs)
qt = Error
term
F = Function
ao = constant
a1 = Multiple
regression coefficient
Variable
|
Variable
name
|
Regression
coefficient
|
Std
error
|
T-value
|
Sign
|
Consistent
|
-
|
1.757
|
0.727
|
2.146
|
0.021
|
X1
|
Age
|
-0.112
|
0.074
|
-1.505
|
0.142
|
X2
|
Marital
status
|
0.007
|
0.229
|
0.030
|
0.976
|
X3
|
Educational
level
|
0.056
|
0.076
|
0.736
|
0.467
|
X4
|
Annual
income
|
0.373
|
0.184
|
2.028
|
0.051
|
X5
|
Labour
cost
|
-0.023
|
0.56
|
-0.401
|
0.691
|
X6
|
Farming
experience
|
0.313
|
0.114
|
-2.747
|
0.010
|
R2
(square) = 0.867
Adjusted
R2 = 0.841
Standard
Error of the estimates = 106.73636
Durbin-Watson = 1.924
Source:
Computed data, 2013
After
critical examination of the regression coefficient result the coefficient of determination(R2)
value was 0.867(86.7%) indicating that about 86.7% variation in the dependent
variable (level of cassava utilization) used in the regression model was caused
by combined effect of charges in the explanatory variables (Socioeconomic
characteristics adopted). The R2 value is high-enough to justify the
good fit of regression model, since explanatory variables exert effects on the
explained variable. Also, adjusted value R2 square 0.841(84.1%)
which is very close in numerical value of R2 square. This closeness
in the numerical implies that the explanatory power of regression mode adopted
was not exaggerated.
The regression coefficient of Age(X1)
was negatively signed and was statistically insignificant. This means there is
inverse relationship between age (X1) and level of cassava
utilization among the selected farmers in the study area. The apriori
expectation was not met. This is true because increase in age will increase
level of cassava utilization among the farmers in study area.
Marital status (X2)
coefficient had a positive coefficient and statistically insignificant beyond
the 15% level of significance.
This implies that there is a
positive relationship between the martial status (X2) and cassava
utilization in the study area. This could be that there is no gender difference
among the cassava processing in the study area. This conforms with the apriori
expectation of the study.
The coefficient of educational level
(X3) and annual income (X4) has a positive relationship
with the utilization of cassava product by the selected cassava processors in
the study area. The regression model also showed that educational level was
statistically insignificant while the annual income was statistically
significant at 10% level of significance. This implies that the educational
status and annual income has a significant role to play in utilization of
cassava product. The apriori expectation was met because increase in
educational level and annual income will positively influence the extensively
utilization of cassava. Education helps in terms of well utilization of
available resources and adoption of new innovation.
Furthermore, the coefficient of
labour cost bore negative coefficient and statistically in significant beyond
10% level of significance in regression model. This means that inverse
relationship exists between the labour cost (X5) and level of
cassava utilization among the selected cassava processors. The apriori
expectation was met because increase in labour cost will negatively influence
the price in market which the poor one can afford and leads to hunger in the
study area.
Farming experience (X6)
had a positive relationship relented to the apriori expectation of the study.
It was also statistically significant at 10% level of significance in the
regression model of the study. This is true the more the farmer gets knowledge
the higher the adoption of new technology and good management of the whole farm
(wise in utilization of available resources) by the farmer.
Y = 1.757 +
0.112 + 0.007 + 0.056 + 0.373
(0.727) (0.074) (0.229) (0.076) (0.184)
-0.023 +
0.313 + et
(0.056) (0.114)
Testing of hypothesis
Decision Rule
If
the f-cal is greater than f-tab, null hypothesis will be reject otherwise
accept the alternative hypothesis.
F-cal =
R2(N-k)
1-R2(k-1)
where;
R2 = coefficient
of determination
N = Sample
size
K = Number
of variables
F-cal = 0.867(40-6)
1.0867(6-1)
F-cal = 38.148
0.665
F-cal = 57.36
F-tab
at 0.05 level of significance
f-tab = 2.76
However, since the f-cal is greater
than the f-tab, the null hypothesis was alternative hypothesis.
And this implies that the
socio-economic characteristics of cassava processors have significant
difference on their level of cassava utilization in the study area.