In this study, the variance compounds parameters of the mixed bi-division variance analysis sample are estimated. This estimation is obtained, by Bayes quadratic unbiased estimator. The second way to estimate variance compounds parameters of a suggested tow-way analysis of variance mixed model with interaction. estimation is done out by the approach called (MINQUÉ). The estimation approach is conducted on true obtained from departments at the college of agriculture/university of Mosul. These data represent the development of growing various kinds of tomato so that the development represents three factors: the first is tomato kind, this is the first factor (H) and the factor of natural fertilizer rate, and this is the second factor (M), and the interaction between the two factors (HM). A random sample is taken from these data in order to get the random linear sample. The elementary values estimated by Bayes unbiased estimator are very much close to those estimated by variance analysis style when compared with the estimated values of the variance estimation parameters done by minimum standard quadratic unbiased estimation. The elementary values represent random linear sample parameters used to estimate minimum quadratic unbiased standard. The elementary values of the estimations are also obtained via analyzing bi-division variance, then these estimations are employed in estimating minimum quadratic unbiased standard. the estimation results by Bayes approach are very similar to those done by variance analysis   http://dx.doi.org/10.25130/tjps.25.2020.038

The aim of this research is to analyze the time series of Thalassemia cancer cases by making assumptions on the number of cases to formulate the problem to find the best model for predicting the number of patients in Nineveh governorate using (Box and Jenkins) method of analysis based on the monthly data provided by Al Salam Hospital in Nineveh for the period (2014-2018). The results of the analysis showed that the appropriate model of analysis is the Auto-Regressive Integrated Moving Average (ARIMA) (2,1,0) and based on this model the number of people with this disease was predicted for the next two years where the results showed values ​​consistent with the original values which indicates the good quality of the model.

This research deal with estimation of  trend surface analysis and with spatial data with three models to real spatial data represents a rising  ground water. The first method in assessment is to estimation trend surface model parameters by (ml), the second method requires decision on the maximum time difference to be calculated (s). while, the third method need a resolution, and the residuals r of dots to take for the conjecture the f(Dij) parameters. The first and second methods require resolution principle of "neighbor" from determines of  "W". These three approaches are applied to real data which represent the ground water levels in 47 wells in mountain region in Sin jar district in Nineveh  governorate.

In this study, the problem of outlier detection in “linear regression” analysis is studied using the “median” and “mean” “absolute deviation” about the median. “The mean and standard deviation” are heavily affected by outliers Hence, the outlier detection techniques based on these measures may not correctly identify all outliers in a dataset. However, “the mean absolute deviation about the median”, in combination with the median is sufficiently robust in the presence of outliers and provides a better alternative. The conceptualized method was tested using leukemia patients data and the results indicate that the new method performed better than the methods based on the mean/ standard deviation combination. It is recommended that the median and “mean absolute deviation” about the median be used in detecting outliers in regression analysis due to their inherent potential for increasing the” goodness-of-fit of the “linear regression mode