Objective: This study was performed to provide the curves of weight and height of 0-2 year-old children in Jahrom, Southeastern Iran. Methods: In a prospective study, 597 children born in Jahrom entered our study from April 2001 to December 2002. The height and weight of these children were recorded 18 times from one month to two years of age. Healy-Rasbash-Yang (HRY) method was used to estimate age related smoothed centiles. This method was implemented for the World Health Organisation as GROSTAT computer package. Findings: The mean weight of male newborns, except for one-month olds, was more than that of females. Before two months of age and among 2-year-olds, there was no significant difference between the weight of males and females. The mean height of male newborns, except for one month of age, was more than that of females. No more than cubic polynomials were needed to smoothly fit height for age and weight for age of children. The height of male and female children showed an increase by age up to two years. The weight of male and female children also increased by age but the speed of increase to six months was more than that of six months to two years of age.Conclusion: Our findings show that height and weight of children under two years of age in Jahrom were lower than those of National Center for Health Statistics (NCHS). It is therefore deemed necessary to provide and establish a corresponding standard curve for this region. According to the NCHS measurements, some children may be considered thinner or shorter for no obvious reason.

Traditional Shewhart-type charting methods assume high volume manufacturing processes where a large number of samples can be gathered to estimate the process parameters before on-line charting actually begins. However, in situations involving low-volume or short-run productions where total output is not sufficiently large, the fairly minimal data requirements of standard control charts may not be achieved, making it necessary to find a new approach in establishing control chart limits. In this paper, control charts that use "Likelihood Ratio" statistics are suggested so that they can be reliably used when fewer than the usually recommended samples are available.

Purpose: In many industrial processes, there are situations where simultaneous monitoring and control of two or more dependent variables are necessary. In such cases, univariate control of quality characteristics can be misleading when considered independently. In the classical approach, when a quality characteristic falls outside the specified technical limits, the quality loss is regarded as a cost. All products within the technical limits of the quality characteristic are assumed to have similar quality, regardless of the deviation of the quality characteristic from its target value. However, it is essential to distinguish between products that fall within the technical limits of the quality characteristic, as any deviation from the target value incurs a proportional loss.  Methodology: This paper introduces, for the first time in the literature, a reflected normal loss function to determine the average cost of producing non-conforming products when two quality characteristics are evaluated. In summary, this study focuses on the statistical-economic design of a multivariate T²-Hotelling control chart with multivariate variable sampling intervals in the presence of a reflected normal multivariate loss function (RNLMVSIT²). Additionally, sensitivity analysis is employed to understand the effects of time and cost parameters on design parameters and the average cost. Findings: The results demonstrate the satisfactory performance of the proposed models. Originality/Value: This paper introduces, for the first time in the literature, a reflected normal loss function to determine the average cost of producing non-conforming products when two quality characteristics are evaluated.

In Statistical quality control a very widely used measure is average run length (ARL) which may be worked out by different methods like integral equation, approximations, and Monte Carlo simulations. The ARL measure and the other related measures are of major significance in every type of production process. An omission in its computation (and hence its related measures such as extra quadratic loss (EQL)) may cause a loss. This necessitates great care in the choice of its evaluation method. This article will deal with this issue using some approximation methods and the Monte Carlo simulations. The discrepancies among the results will be examined to highlight the deficiencies of using approximation methods in quality control techniques.