PurposeThis study aims to focus on proposing a new memory-type chart called progressive mean exponentially weighted moving average (PMEWMA) control chart. This memory-type chart is an improvement on the existing progressive mean control chart, to detect small and moderate shifts in a process.Design/methodology/approachThe PMEWMA control chart is developed by using a cumulative average of the exponentially weighted moving average scheme known as the progressive approach. This scheme is designed based on the assumption that data follow a normal distribution. In addition, the authors investigate the robustness of the proposed chart to the normality assumption.FindingsThe variance and the mean of the scheme are computed, and the mean is found to be an unbiased estimator of the population mean. The proposed chart's performance is compared with the existing charts in the literature by using the average run-length as the performance measure. Application examples from the petroleum and bottling industry are also presented for practical considerations. The comparison shows that the PMEWMA chart is quicker in detecting small shifts in the process than the other memory-type charts covered in this study. The authors also notice that the PMEWMA chart is affected by higher kurtosis and skewness.Originality/valueA new memory-type scheme is developed in this research, which is efficient in detecting small and medium shifts of a process mean.

People are often exposed to toxic or hazardous (e.g. radioactive radon and lead) elements and rays, without even knowing so. Toxicity often results from an individual's prolonged exposure to toxic substances. A thorough examination of some individuals' blood or urine samples for the quantities of hazardous substances or elements, often gives a multivariate data (i.e. matrix of cases against elements) on toxicity. The pertinent response variable is often binary response (or count data) type and hence the Generalized Linear Models (GLM) of it can be fitted using our proposed techniques. This paper purports to identify models in GLM that can be used to study toxicity when it is 'captured' as count data or Binary Response Variables (BRV). An illustration of how the techniques work is done by using a sample of data on some artisans.

In launching an environmental technique, statistics plays an important role. The art of using statistical methods, models and principles to work on environmental issues is hereby utilized in dealing with a well-ordered set of quantities on exposures of some Artisans to toxicity. The Poisson was initially assumed to be the intrinsic distribution, but following a strong advocacy in favour of the Lindley over members of the exponential family of distributions, the population, (𝑋 1 , . . . , 𝑋 𝑛 ), of hazardous elements, in the blood/urine of Artisans is assumed to be Lindley. The derivation of other "locational" distributions therefore originates from it. Numerical illustrations of how the proposed technique can be utilized concludes this work.

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