@article{MAKHILLERJ202014428460,
    title = {Regression Analysis of the Climatic Variables over Greater Yola, Adamawa State
Northeastern Nigeria},
    journal = {Environmental Research Journal},
    volume = {14},
    number = {4},
    pages = {116-123},
    year = {2020},
    issn = {1994-5396},
    doi = {erj.2020.116.123},
    url = {https://makhillpublications.co/view-article.php?issn=1994-5396&doi=erj.2020.116.123},
    author = {B.,I.M.,I.,A.C. and},
    keywords = {Climate change,Yola,global warming,regression analysis,Northeastern Nigeria},
    abstract = {Climate change has the tendency of negatively
affecting the socioeconomic development of a society at
both community and national level. The change climate
which is manifested as the temporal variability in the
climatic elements, varies spatially with location. In this
work, attempt is made to analyse the magnitude and
direction of the changes of the various climatic elements
of greater Yola, Adamawa State Nigeria over a three
decade time series. The works involves linear regression
analysis of the temporal variability of the mean annual
minimum, maximum and meanannual temperatures, mean
annual rainfall, mean annual sunshine, mean annual
humidity and the mean annual evaporation. The results
indicate positive trends for all the variables except for the
sunshine and evaporation data which showed negative
trends. The model regression equations derived were
tested at 0.05 level of significance and the results showed
that a p-value of 0.201 (>0.05) for annual mean maximum
temperature, 0.000 (<0.05) mean annual minimum
temperature, 0.000 (>0.05) for the mean annual maximum
temperature, 0.672 (>0.05) mean annual rainfall, 0.7768
(> 0,.5) for the mean annual sunshine hours, 0.129 (>0.05)
for the mean annual relative humidity and 0.263 (>0.05)
for the mean annual evaporation. Hence, only the mean
annual minimum and maximum temperatures are
predictable using their respective model equations. The
work however showed evidence of local eratic and
irregular climate variability relative to global warming.}
    }