Shewhart control charts are widely accepted as standard tools monitoring manufacturing statistical processes. The control charts have not applied, when the process distribution is not normal. The BOOTSTRAP is one of the resampling methods that can be used in statistical quality control without normality assumption. In most of papers, only the PERCENTILE BOOTSTRAP CONFIDENCE INTERVAL is used for control limits. In this paper, we apply PERCENTILE BOOTSTRAP, BOOTSTRAP-t, bias corrected accelerated (BCa) and approximate BOOTSTRAP CONFIDENCE INTERVAL (ABC) for mean control limits of statistical process. Then, the BOOTSTRAP CONFIDENCE INTERVALs are used and compared for mean control limits in simulation study. Finally, the BOOTSTRAP control limits are used for mean of CO2 data in Isfahan Zamzam factory.

In the statistical downscaling methods which is based on the relationship between AOGCMs data and ground based climatic variables (such as rain and temperature), the future period of those variables are simulated. Since in the simulation, all effective parameters cannot be modeled, estimated values suffers from be uncertainty. The outputs of downscaling models are used as inputs to agriculture and water resources models; therefore, identifying the models inputs’ error or uncertainty is essential to realize the reliability of obtained results. In this research, an attempt is made to investigate the uncertainty of Artificial Neural Network (ANN) as a downscaling model in a case study in the northwest of Iran. For this purpose, precipitation, minimum and maximum temperature variables were used in the designed ANN model, and the NCEP data was employed for its calibration and validation. The HadCM3 was the selected AOGCM in this study. Observed daily time series were gathered at all stations in the study period and on the basis of BOOTSTRAP method the 99% CONFIDENCE INTERVAL was calculated for all the variables. In the next step, the simulated (downscaled) mean and variance of the variables by the ANN model, compared to the calculated CONFIDENCE INTERVAL. To compare the results, the criterion of the number of station-month was used. The results showed that the average maximum temperature at 14 station-months were within the CONFIDENCE INTERVAL. The results of monthly analysis showed that the accuracy of ANN model in summer was low and its uncertainty is more than the other seasons. In the simulation of minimum temperature based on this criterion, 18 station-months were within the CONFIDENCE INTERVAL. The accuracy of ANN to estimate the minimum temperature in summer was low with high uncertainty in almost all the stations. Moreover, in June and August in any of the stations estimated values were not within the CONFIDENCE INTERVAL. Due to the high variability of rainfall in relation to temperature, CONFIDENCE range was very high, and in some stations was more than 50% of average monthly precipitation. Because of the high CONFIDENCE rang of precipitation, in 53 Stations-month cases were within the CONFIDENCE INTERVAL.

The mean response time plays an important role in the analyzing and optimizing the queuing system, which determines the number and type of giving service. In this paper, new CONFIDENCE INTERVALs of mean response time for an M/G/1 FCFS queuing system is contrasted based on the nonparametric delta method and five BOOTSTRAP methods. These methods include: nonparametric delta method CONFIDENCE INTERVAL based on the influence function, standard BOOTSTRAP CONFIDENCE INTERVAL, PERCENTILE BOOTSTRAP CONFIDENCE INTERVAL, BOOTSTRAP-t CONFIDENCE INTERVAL, bias corrected, acceleration BOOTSTRAP CONFIDENCE INTERVAL, and BOOTSTRAP pivotal CONFIDENCE INTERVAL. In a simulation study, these six methods are compared and evaluated the accuracy and performance of the CONFIDENCE INTERVALs for three different M/G/1 FCFS queuing systems based on the coverage percentage and the average length of CONFIDENCE INTERVALs.

Background: The prevalence of type 2 diabetes mellitus (T2DM) is increasing in Iran. This study determined the prevalence of undiagnosed type 2 diabetes mellitus in apparently normal individuals in Hamadan, west of Iran. Study design: A cross-sectional study. Methods: A sample of 106 apparently normal volunteers aged 18 yr and more were enrolled, and T2DM was diagnosed using hemoglobin A1c (HbA1c) levels from 2015-2016. The nonparametric BOOTSTRAP method was used to eliminate the undesirable effect of small sample size on the estimation of standard error of multiple logistic regression coefficients and CONFIDENCE INTERVAL for the prevalence of undiagnosed T2DM. Results: Overall, 23 (21. 69%) were male. The mean (± sd) age of the participants was 43. 76 ± 14. 01 year. In 78. 3% of individuals, HbA1c level was within normal range (<5. 7), 13. 21% was in the range of 5. 7-6. 4 (undiagnosed pre-T2DM), and 8. 49% was ≥ 6. 5 (undiagnosed T2DM). Multiple logistic regression gave the characteristic distribution of volunteers such as sedentary hour (P=0. 001), family history of diabetes (P=0. 001), smoking (P=0. 002), and age (P=0. 012) had the odds on the significant effect on undiagnosed T2DM. Conclusions: The prevalence of undiagnosed T2DM among apparently normal individuals in Hamadan was relatively high. Addition to age, factors such as sedentary, exposure to smoking and having a history of diabetes in family can be a prognosis for undiagnosed T2DM in apparently normal individuals.

Applied researches in many financial markets show that Stock price index will fluctuate with changes in macroeconomic variables. Since the stock value in theory is equal to the sum of discounted future cash flows and these cash flows are affected by macroeconomic events. Therefore they can be also affected by oil shock. This study aims to investigate asymmetric effects of oil price shocks on the stock price index in Iran. In this regard, SVEC model with BOOTSTRAP CONFIDENCE INTERVAL in Impulse Response Functions are used.The estimation results of Impulse Response Functions and Forecast Error Variance Decomposition indicate that positive oil price shock has an increasing effect on stock price index while negative oil price shock decreases the stock price index. Also the results show that the effects are asymmetric, So that the effect of negative oil price shock on the stock price index is far larger and more lasting than the positive oil price shock. The results of CONFIDENCE INTERVALs in IRFs indicate that impulse responses of stock price to oil shocks are reasonable and significant in all cases.

Process capability indices are widely used in various industries as a statistical measure to assess how well a process meets a predetermined level of production tolerance. In this paper, we propose new CONFIDENCE INTERVALs for the ratio and difference of two Cpmk indices, based on the asymptotic and parametric BOOTSTRAP approaches. We compare the performance of our proposed methods with generalized CONFIDENCE INTERVALs in term of coverage probability and average length via a simulation study. Our simulation results show the merits of our proposed methods.

This paper deals with construction of CONFIDENCE INTERVALs for process capability index using BOOTSTRAP method (proposed by Chen and Pearn in Qual Reliab Eng Int 13(6): 355– 360, 1997) by applying simulation technique. It is assumed that the quality characteristic follows type-II generalized log-logistic distribution introduced by Rosaiah et al. in Int J Agric Stat Sci 4(2): 283– 292, (2008). Discussed different BOOTSTRAP CONFIDENCE INTERVALs for process capability index. Maximum likelihood method is considered for obtaining the estimators of the parameter. Monte Carlo simulation technique is applied to find out the coverage probabilities and average widths of the BOOTSTRAP CONFIDENCE INTERVALs. The results are illustrated with real data sets.