big data analysis by using one covariate at a time multiple testing (ocmt) method: early school dropout in iraq

The early school dropout is very significant portents that controls the future of societies and determine the nature of its elements. therefore, studying this phenomenon and find explanations of it is a necessary matter, by finding or developing appropriate models to predict it in the future. the variables that affect the early school dropout iraq takes a large size and multiple sources and types due the political and economic situation , which attributes it as a sort of big data that must be explored by using new statistical approaches. the research aims at using one covariate at a time. multiple testing ocmt method to analyze the data from surveys collected by the central statistical organization iraq, which contains many indicators related to school dropout and meaningfully affect the life of the iraqi persons. the ridge regression method as well as the ocmt method were chosen to analyze data and the mean square errors mse was used to compare the two methods and from the results we find that ocmt estimator is better than ridge estimator with big data conditions.