Linear regression methods impose strong constraints on regression models, especially on
the error terms where it assumes that it is independent and follows normal distribution, and
this may not be satisfied in many studies, leading to bias that can
not be ignored from the
actual model, which affects the credibility of the study.
We present in this paper the problem of estimating the regression function using the
Nadarya Watson kernel and k- nearest neighbor estimators as alternatives to the parametric
linear regression estimators through a simulation study on an imposed model, where we
conducted a comparative study between these methods using the statistical programming
language R in order to know the best of these estimations. Where the mean squares errors
(MSE) was used to determine the best estimate.
The results of the simulation study also indicate the effectiveness and efficiency of the
nonparametric in the representation of the regression function as compared to linear
regression estimators, and indicate the convergence of the performance of these two
estimates.
The present study describes a simple stability-indicating reversed-phase HPLC assay
for pentoxifylline in its pharmaceutical dosage forms. Separation of the drug and the
degradation products، under stress conditions was successfully achieved on a C
18 column
utilizing water: MeOH (60:40 v/v)، pumped at a flow rate of 1 ml min-1 with UV detection
at 272 nm. The retention time of pentoxifylline was about 14 min. The method was
satisfactorily validated with respect to linearity، precision، accuracy and selectivity. The
response was linear in the range of 0.6-3.5 μg/ml with R2 0.994. The method was accurate
(recovery 100.1%) and precise (RSD < 2%). Detection and quantification limit were 0.2
μg/ml and 0.4 μg/ml respectively. The suggested method was successfully applied for the
analysis of pentoxifylline in extended release tablets available in Syrian market.
Groundwater is one of the major sources of exploitation in arid and semi-arid
regions, Thus for protecting groundwater quality, data on spatial and temporal distribution
are important. Geostatistics methods are one of the most advanced techniques f
or
interpolation of groundwater quality. In this research, IDW, Kriging methods were used for
predicting spatial distribution of nitrate NO3
-. Data were taken from 21 wells study within
eastern Damascus's Ghouta.
After normalization of data, variograme was drawn. The less RSS was used, so
Spherical model was the best. By using cross-validation and RMSE, the best method for
interpolation was selected; Results showed that Kriging method is superior to IDW
method.
there is a big spatial dependence for nitrate variable that amounts to 2.2 %. Finally,
maps of distribution of nitrate in groundwater were executed by Kriging method, in
addition to executed maps that show goodness of groundwater for drinking and irrigation.
Then it was prepared map of Probability Map of nitrate at threshold 50 mg/l.
The development of Information Systems (I.S) is of great importance for
researchers and industrial users. Different methods for (I.S) design have been
proposed until now. Some of them put emphasis on the statical aspect, and
others on the dynamica
l aspect. A third category of methods has recently
appeared, which try to take into account both aspects, and therefore provide
unified view of data and treatments. Also there exist approaches that put the
stress on rigour in the specification and validation process.
This paper presents a tool (CASE) which constitute a conceptual help to
Information Systems design: that tries, on one hand, to handle the statical and
dynamical, aspects while providing at the same time, the user with products
that are readable and easy to understand and on the other hand, to validate the
specification obtained in a rigorous way. In fact, it is an attempt to satisfy both
the designer and the user.
