Optimum estimator in simple random sampling using two auxiliary attributes with application in agriculture, fisheries and education sectors

In modern age of information technology, data is available everywhere in huge amount. Every sector generates lot of data every day. The investigation of each unit of data is not feasible due to limited resources like time, labor, and cost. In such situations, survey sampling is recommended to draw the information about the population parameters. Therefore, the main objective of present study is to develop an estimation method for obtaining the information about population parameter. We propose an optimum estimator for enhanced estimation of population mean in simple random sampling by utilizing the information of the two auxiliary attribute. The expression for bias, mean squared error (MSE) and minimum mean squared error of the proposed estimator are derived up to the first order of approximation and it is shown that the proposed estimator under derived conditions perform better than the existing estimators theoretically. Four population are demonstrated to assess the performance as well as applicability of the proposed estimator. The percentage relative efficiency (PRE) of proposed estimator for all the populations is 209.533, 163.852, 210.398 and 340.578, respectively. The numerical illustrations confirm that the proposed estimator dominates over the existing estimators. • The main objective of present study is to propose a new estimator/method for estimation of population mean using two auxiliary attributes under simple random sampling. • The bias and mean square error of the proposed estimator/method is derived and compared with the existing estimators to compare the efficiency theoretically. • Applications of the proposed method/estimator is highlighted using thorough the real data sets of various sectors.