Development process of any system is dynamic in nature and depends on large number of parameters. This study attempted to capture latest dynamics of development of districts of Eastern Uttar Pradesh in respect of three dimensions- Agriculture, Social and Infrastructure. Techniques adopted by Narain et al. (1991) have been used in addition to Principal component and factor analysis. Ranking seems to very close to ground reality and provides useful information for further planning and corrective measures for future development of Eastern UttarPradeshâ€™s Districts. The Composite Indices (C.I.) of development in respect of 18 developmental indicators for the total 28 districts of eastern Uttar Pradesh have been estimated for the year 2010-2011. The district Barabanki was showed a higher level of development (C.I. =0.10) in Agricultural development compared to Social development (C.I.=1.12) and Infrastructural development (C.I. =0.89) followed by the district Ambedkar nagar (Agricultural, C.I. =0.52), (Social, C.I. =1.12) and (Infrastructure, C.I. =0.89). District Allahabad secured first position in the Social development (C.I. =0.81) and second in Infrastructural development (C.I. =0.34) as compared to Agriculture (C.I. =0.93). District Varanasi was the most developed district in Infrastructure (C.I. =0.10) as compared to Agriculture (C.I.=0.96) and Social (C.I. =0.96). As per findings of the study, the two districts Mau and Jaunpur were down in their ranking and the districts Chandauli and Maharajganj improved their ranking.
The problem of estimating the population mean using an auxiliary informetion has been dealt with in literature quite extensively. Ratio, product, linear regremion and ratio-type eetimators are well known. A o l w of ratio-cum-product-type estimator is proposed in this paper. Its bias and variance to the first order of approximation are obtained. For an appropriate weight 'a' and good range of a-valuea, i t is found that the proposed eetimator is superior than a set of estimators (i.e.. sample mean, usual ratio and product estimators, SBIVASTAVA'S (1967) estimator, CH~KSABARTY'S (1979) estimator and a product-type estimator) which are, in feet, the particular cases of it. A t optimum value of a, the proposed estimator is as efficient as linear regreasion estimator.
A class of ratio cum product-type estimator is proposed in case of double sampling in the present paper. Its bias and variance to the first order of approximation are obtained. For an appropriate weight 'a' and 8 good range of a-values. it is found that the proposed estimator is more efficient than the set of estimator viz., simple mean estimator, usual ratio and product eetimators, ~BIVASTAVA'S estimator (1967), CHAKARBARTY'~ estimator and product-type estimator, which am in fact the particular cues of it. The proposed estimator is 8s efficient u linear regression estimator in double sampling a t optimum value of a. K e y words: Double sampling, cost-function, preliminary sample.
In the present paper, a model based calibration estimator of population total has been developed when study variable y and auxiliary variable x are inversely related. The relative performance of the proposed model based calibration estimator in comparison to model based estimator, the usual regression estimator and calibration based regression estimator have been examined by conducting a limited simulation study. In view of the results of the simulation study, it has been found that model based calibration estimator has outperformed the other estimators. However, calibration based regression estimator was found to be close to the model based calibration estimator.
An application of principal component analysis for the development of suitable statistical models for preharvest forecast of rice yield based on biometrical characters has been dealt with in the present paper. The data obtained from the two experiments on rice have been utilised to develop the model. The forecast yields of based onthese models have been found to be 24.25, 22.60 and 21.10 q/ha against the actual yield of 28.00, 23.56 and 21.85 q/ha, respectively, in experiment â€“I. For experiment â€“II the forecast yields were found to be 24.62, 28.06 and 29.43 q/ha against the actual yield of 28.82, 29.31 and 26.59 q/ha, respectively. These forecast yields are subject to maximum of almost 10 percent standard error. In most of the cases, the forecast yields were found to be close to the actual yield except in some cases. The values of R2, i.e. 79.80 and 72.60 for experiment â€“I and II, respectively, indicate the validity of the models. Statistical tool like viz. principal component analysis (PCA) has been first time applied to develop pre-harvest forecast model based on experimental data.
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