@article{MAKHILLJMMS202115428223,
    title = {Discriminant Analysis and It’s Application to the Oil Palm Cultivation in Nigeria},
    journal = {Journal of Modern Mathematics and Statistics},
    volume = {15},
    number = {4},
    pages = {47-53},
    year = {2021},
    issn = {1994-5388},
    doi = {jmmstat.2021.47.53},
    url = {https://makhillpublications.co/view-article.php?issn=1994-5388&doi=jmmstat.2021.47.53},
    author = {Rasheed},
    keywords = {soil characteristics,soil class,Discriminant function,raphia growing zone,Southern Nigeria},
    abstract = {The study centralized on using discriminant
analysis to predict soil group membership in order to
correctly classify future unknown observation into any of
the five soil groups based on observed predictors (soil
characteristics) in soils supporting Raphia palms of
Southern Nigeria. Four functions were estimated but only
one with higher eigenvalue of 1.315 which explain the
variation of the soil groups by 55.6% was significant. The
estimated canonical correlation, the Wilks’ Lambda and
their associated Chi-square values used to measure the
significant performance of the function were 0.754 and
0.178 and 82.503, respectively and was significant at
p<0.05, of the 13 predictors variables used in the analysis,
7 were significant with mg ranking in discriminating
among the groups. When actual grouping of the five soil
types was compared to the predicted groupings generated
by the discriminant functions, group 1 and 5 were
predicted the best with 75% of the cases correctly
classified while group 3 had a moderate performance with
64.2% success rate. Group 2 and 4 were predicted the
worst with 58.3 and 56.8%, respectively of the cases were
correctly classified. The overall discriminant function
(hit ratio) had 67.7% success rate in classifying the
samples. The estimated press&#146;s Q statistic used to
determined the predictive power of the model was 347.56
and this was greater than the calculated value (11.1),
implying that the model predictive accuracy is greater
than that expected by chance. Thus, the model has a good
predictive power and can be generalized as a fairly good
tools for classifying new cases. Finally, the result shows
that discriminant analysis is a fairly good method for
predicting new cases into any of the five soil types
dominant in the Raphia growing zone of southern Nigeria
and that mg, p, Cu, pH, Ca, Mn and K were identified as
the soil properties that best discriminate among the soil
types in the zone studied.}
    }