نشریه بررسی های آمار رسمی ایران
سال بیستم شماره 2 (پیاپی 75، پاییز و زمستان 1388)

In this article in order to compare the age, sex, household dimension and economic activity characteristics between migrants and non-migrants, also to study the resident duration and migration cause, we used the data from the 2006 census. Results indicate that there are major differences in above-mentioned characteristics between migrants and non-migrants. With attention to selective migration and life cycle theories, migrants are persons with specific demographic, economic and social characteristics which are different from original and destination persons.Migration causes show that family-follow cause with 46 percent is the most frequent cause and after that three factors of searching occupation, education and conscription are the other three major causes. Proportion of short time migrations (less than 5 years) is more than long time migration (5 and more than 5 years). Less than one year resident duration with 21 percent has got the biggest proportion than the other periods.

Many types of rotation designs are actually used in many surveys as efficient methods. In this paper, we introduce a new method of estimation based on a linear additive model with dependent errors. The new estimator is compared with direct estimator via some simulations in a rotation sampling. The results show that the new method of estimation is more efficient and overlapping coefficient has a significant effect on efficiency.

Most of the National Statistical Offices, in order to economize the available resources in conducting surveys, optimize the sample size at the national level. However, a need to more detailed information in the level of smaller communities such as provinces, towns, regions, etc is always felt. In a sense, the size of sample should be determined in a way that optimizes the estimators both in the national level and in small area levels. Since rotation sampling is regarded as one of the most important methods of survey sampling in national statistical offices, in this paper, an optimal sample size for a Labour Force Survey -which is performed in each reason by means of rotation sampling by Statistical Center of Iran- is obtained initially, then using sample size determination methods for estimations in small areas, the allocated sample sizes for each sub populations (provinces) are obtained.

Many factors are effected in the production process. They may be controllable or uncontrollable. Disregarding these factors, the customer wants a pure, perfect and qualified product. To ensure customer demands, these factors should be controlled as far as possible. One of the methods to control these factors is the statistical quality control. This kind of control is applied in different ways, that one of them is Acceptance Sampling.In acceptance sampling, the product consists of lots with specific sizes. After sampling from each lot, you can estimate its quality. One of the acceptance sampling standard is MIL STD 105E. Therefore in this article along with identification of this standard, implementation of this method in statistical survey is explained.

In the literature of economic planning and policy making, having access to accurate and up-to-date statistics and information, which are the basis of decision-making, is a high priority among factors such as natural resources, financial resources, workforce, proper and progressive technology. Statistics and information have a significant role in the stages of planning (policies and goals), directing administrative affairs, and function assessment. The comprehensive system of environmental statistics is not needed just for assessing the current status of the environment, but also is essential for plan preparation, policy formulation, and sustainable development and the environment protection activities. Therefore, providing comprehensive environmental statistics would make it convenient to meet the Goals of the 5th Chapter of the 4th Development Program (Articles 58-71), In other words, according to the Article 50 of the Constitution, the Goals of the 5th Chapter of the 4th Development Program of the Country (Articles 58-71), Stockholm Declaration, Chapter 8 of the Agenda 21 and especially Part D of the same chapter, Chapter 40 of the Agenda 21, UNEPs, UNSD's and UNDSD's Recommendations, and Millennium Development Goals, organizing, providing and publishing environmental statistics seems to be necessary.This paper discusses and reviews environmental statistics concepts, the essence of providing environmental statistics and indices, different types of data, statistics sources, environmental indices and indicators, environmental statistics data collection methods, different parts of environmental statistics, and different types of international frameworks of environmental statistics.

In different researches, we deal with problems that need to predict the behavior of a response variable by a set of explanatory variables. Multiple regression is one of the statistical methods that is widely used in these type of situations. When there is a linear relation between explanatory variables we will faced with multi-collinearity problem that causes ordinary least squares estimators of regression coefficients not to be robust. Partial least squares regression is a multivariate method that used when there is high collinearity between explanatory variables. In this paper, this method is introduced.

Survey researchers frequently compare the demographic distributions of respondents to the corresponding distribution from the target population (often using census data). Many researchers hope that if the respondents in their study have demographic characteristics similar to those of the target population, their research findings do not suffer from nonresponse bias. Hence, it is useful to ask the question whether the nonresponse biases of demographic statistics are predictive of biases in key substantive statistics of the same surveys. A meta-analysis of 23 methodological studies designed to estimate levels of nonresponse bias in statistics of interest was conducted. The data from the 23 studies suggest that the difference between respondent and nonrespondent means for demographic variables is not predictive of the difference between respondent and nonrespondent mean for substantive variables of the same survey.