Sequential sampling procedure constitute a powerful tool to achieve experimental efficiency a sequence of sample are taken from the lot and allow the number of samples to be determined entirely by the result of the sampling process, it forms the basis of numerous sequential technique for different applications which was the special case for multiple acceptance plans, the performance of sequential probability ratio test provides better detection for a wide range of source strength and useful in choosing the best statistical test, which take less time to make decision, was developed on the software reliability measure based on failure intensity for the life time random variable for a truncated life test. In this paper, we considered the performance of acceptance sampling plans using sequential probability ration (SPRT) based on truncated life tests on Maxwell distribution and exponential distribution function  in terms of obtaining the minimum number of sample sizes necessary to obtain specified average life time under a given consumer’s and producer’s risk. The SPRT provide an alternative to fix the plans that can help diminish producer and consumer risk of reaching at a wrong decision.

This paper proposed a sequential probability sampling plan for a truncated life test using a Rayleigh distribution from  a designed double sampling plans where the interest was to obtain the minimum sample size necessary to assure that the average life time of a product is longer than the default life time at the specified consumer’s and producer’s confidence level. Estimations of minimum sample, acceptance and rejection numbers obtained are analyzed and presented to explain the usefulness of sequential plans in relation to single and double sampling plan. Probability of acceptance (Pa), Average sample number (ASN) and Average outgoing quality (AOQ) for the plans are computed. The three regions; acceptance, continue sampling and rejection were determined. The five points necessary to plot ASN curve were also computed and presented.

Human-assisted surveys, such as medical and social science surveys, are frequently plagued by non-response or missing observations. Several authors have devised different imputation algorithms to account for missing observations during analyses. Nonetheless, several of these imputation schemes' estimators are based on known auxiliary variable parameters that can be influenced by outliers. In this paper, we suggested new classes of exponential-ratio-type imputation method that uses parameters that are robust against outliers. Using the Taylor series expansion technique, the MSE of the class of estimators presented was derived up to first order approximation. Conditions were also specified for which the new estimators were more efficient than the other estimators studied in the study. The results of numerical examples through simulations revealed that the suggested class of estimators is more efficient.

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