The aim of the research is to study the comparison between (ARIMA) Auto Regressive Integrated Moving Average and(ANNs) Artificial Neural Networks models and to select the best one for prediction the monthly relative humidity values depending upon the standard errors between estimated and observe values . It has been noted that both can be used for estimation and the best on among is (ANNs) as the values (MAE,RMSE, R2) is )0.036816,0.0466,0.91) respectively for the best formula for model (ARIMA) (6,0,2)(6,0,1) whereas the values of estimates relative to model (ANNs) for the best formula (5,5,1) is (0.0109, 0.0139 ,0.991) respectively. so that model (ANNs) is superior than (ARIMA) in a such evaluation.

Analysis the economic and financial phenomena and other requires to build the appropriate model, which represents the causal relations between factors. The operation building of the model depends on Imaging conditions and factors surrounding an in mathematical formula and the Researchers target to build that formula appropriately. Classical linear regression models are an important statistical tool, but used in a limited way, where is assumed that the relationship between the variables illustrations and response variables identifiable. To expand the representation of relationships between variables that represent the phenomenon under discussion we used Varying Coefficient Models

Comparisons of two life tables constructed to display alfaifa weevil Hypera posticoa (Gryllenhal), populations in southeentral Wisconsin, U. S. A. under epizootic and enzootic conditions of fungal diseasea, caused by Erynia phytonomi Arthur suggests that the “prepupal” stage provided the greates contribution to population changes under both conditions due to the high mortality rate. The principle mortality agents during this stage are E. phytonomi and the parasitoids complex of Bathyp1ectes curculionis and Buthyp1ectes anurus respectively under the two condition.