In this study, drought characteristics of Arak, Bandar Anzali, Tabriz, Tehran, Rasht, Zahedan, Shiraz and Kerman stations during the statistical period of 1956 to 2015 were studied by Reconnaissance Drought Index (RDI) and Standardized Precipitation Index. Precipitation and temperature data were needed to calculate RDI. Precipitation data was also required to estimate SPI. In this study, Drinc software was used to calculate RDI, SPI and potential evapotranspiration (PET). The software calculated PET by the Thornthwaite method. One of the main challenges in drought monitoring is to determine the indicator that has a high reliability based on its monitoring purpose. Therefore, in this research, two methods used for selecting the appropriate index based on the minimum rainfall and normal distribution were evaluated. The results of the evaluation of the minimum rainfall method for selecting the appropriate index showed that most drought indices with the occurrence of minimum rainfall level indicated severe or very severe drought situations; in most cases, it could not lead to selecting an exact and unique index. Based on the results of the normal distribution method for the stations of Arak, Tabriz, Rasht, Zahedan, Shiraz and Kerman, SPI index, and for the stations of Bandar Anzali and Tehran, RDI index were selected as the most appropriate ones.

In many industrial and non-industrial applications the quality of a process or product is characterized by a relationship between a response variable and one or more explanatory variables. This relationship is referred to as profile. In the past decade, profile monitoring has been extensively studied under the normal response variable, but it has paid a little attention to the profile with the non-normal response variable. In this paper, the focus is especially on the binary response followed by the bernoulli distribution due to its application in many fields of science and engineering. Some methods have been suggested to monitor such profiles in phase I, the modeling phase, however, no method has been proposed for monitoring them in phase II, the detecting phase. In this paper, two methods are proposed for phase II logistic profile monitoring. The first method is a combination of two exponentially weighted moving average (EWMA) control charts for mean and variance monitoring of the residuals defined in logistic regression models and the second method is a Multivariate T2 chart to monitor model parameters. The simulation study is done to investigate the performance of the methods.

Statistical variables are divided into two categories: nominal and ordinal, both of which have many uses. In some statistical process monitoring applications, the quality of a process or product is described by multiple ordinal quality characteristics, which is called ordinal Multivariate process. An ordinal contingency table is used to show the relationships between these variables and is modeled on an ordinal log-linear model. In our manuscript, two new statistics including simple ordinal categorical and Generalized-p are developed for Phase II monitoring the ordinal log-linear model-based processes. The performance of the proposed statistics will be evaluated using some simulation studies and real-world numerical examples. The results show the advantages of a simple ordinal category control card. In addition, the performance of these statistics is accessed through sensitivity analysis of the row and column sizes of the contingency table. Meanwhile, a sensitivity analysis with three and four categorical factors is performed and similar results are obtained.

profile monitoring in statistical quality control has attracted attention of many researchers recently. A profile is a function between response variables and one or more independent variables. There have been only a limited number of researches on monitoring Multivariate linear profiles. Indeed, monitoring correlated Multivariate profiles is a new subject in the filled of statistical process control. In this paper, we investigate the effect of autocorrelations in monitoring Multivariate linear profiles in phase II. The effect of three main models namely AR (1), MA (1), and ARMA (1, 1) on the methods of Multivariate linear profile monitoring is evaluated and compared by using simulation study and average run length criteria. Results indicate that autocorrelation affects performance of the existing methods significantly.