ABSTRACT:In this paper, the Binary Logistic Regression Analysis BLRA technique has been used and applied for building the best model for Hepatitis disease data using best subsets regression and stepwise procedures and depending on some laboratory tests such as glutamate oxalate transaminase, glutamate pyruvate transaminase, alkaline phosphatase, and total serum bilirubin which represents explanatory variables. Also, the technique has used for classifying persons into two groups which are infected and non-infected with viral Hepatitis disease. A random sample size consists of 200 persons has been selected which represents 86 of uninfected and 114 of infected persons. The results of the analysis showed that first, the two procedures identified the same three explanatory variables out of four and they were statistically significant, and it has been reliable in building the logistic model. And second, the percentage of visible correct classification rate was about 98% which represents the high ability of the model for classification.

In this paper, we used a hybrid method based on wavelet transforms and ARIMA models and applied on the time series annual data of rain precipitation in the Province of Erbil-Iraq in millimeters. A sample size has been taken during the period 1970-2014.We intended to obtain the ability to explain how the hybrid method can be useful when making a forecast of time series and how the quality of forecasting can be enhanced through applying it on actual data and comparing the classical ARIMA method and our suggested method depending on some statistical criteria. Results of the study proved an advantage of the statistical hybrid method and showed that the forecast error could be reduced when applying Wavelet-ARIMA technique and this helps to give the enhancement of forecasting of the classical model. In addition, it was found that out of wavelet families, Daubechies wavelet of order two using fixed form thresholding with soft function is very suitable when de-noising the data and performed better than the others. The annual rainfall in Erbil in the coming years will be close to 370 millimeters.

In this paper, the Box-Jenkins methodology has been applied and ‎used to forecast the ratio of Iraq's ‎rural population from 1960 to 2019. A sample size of (60) observations of the ‎annually rural population ‎of Iraq has been taken. A combination of ‎some adequate time series models has been prepared and ‎‎obtained and some statistical criteria have been used for comparison and model selection. Results of ‎the study concluded ‎that the ARIMA (0,2,1) is an adequate and best model to be used for ‎forecasting ‎the annual ratio of rural population data in Iraq. ‎During the period 2020 to 2030, the ratio of the rural ‎population ‎will keep decreasing gradually, and the percentage of the rural ‎population of Iraq in 2030 ‎will be (27.732).‎

Many applications have been done in the field of using wavelet analysis for time series analysis. In this study, we used the quarterly data of Electric Energy Supply in Duhok Province-Iraq in Megawatt which represents a sample size (46) observations during the period 2004 and 2015.we aim to describe how wavelet de-noising can be used in time series forecasting and improve the forecasting quality through presenting some proposed methods based on wavelet analysis and SARIMA method and applying on real data and make comparison between methods depending on some statistical criteria.Results from the analysis showed the superiority of the three proposed methods and showed that we can get more information from a series when using Wavelet-SARIMA method and this leads to enhance the classical SARIMA model in forecasting. Furthermore, after many empirical experiments with many wavelet families, it has been found that Daubechies, Coiflets, Discrete Meyer(dmey) and Symlet wavelets are very suitable when denoising the data and out of these four wavelet families, the Daubechies and Discrete Meyer performed better.