Faktor-faktor Yang Mempengaruhi Permintaan Bawang Merah Di Kota Medan

Shallot is a horticultural commodity classified as vegetable spices that serve as a seasoning for food and traditional medicine. The importance of shallot for cooking makes the demand for shallot continue to increase every year. This research aims to see what factors influence the demand for shallot in Medan City. This research was conducted in Medan Deli Market and Kemiri Market. Sampling used this research used the Accidental Sampling method, which determines the sample based on people who are accidentally encountered in the study area. The number of samples examined in this research is 40 samples of shallot consumers. This research uses Best Linear Unbiased Estimation and Multiple Linear Regression methods. The variables studied were the price of shallot, the income of consumers, the number of family members and the price of yellow onion. The result of this research indicates that the factor that influences the demand for shallot is the price of shallot, the income of consumers and the number of family members, while those that do not affect is the price of yellow onion.

Best linear unbiased estimation for varying probability with and without replacement sampling

When sample survey data with complex design (stratification, clustering, unequal selection or inclusion probabilities, and weighting) are used for linear models, estimation of model parameters and their covariance matrices becomes complicated. Standard fitting techniques for sample surveys either model conditional on survey design variables, or use only design weights based on inclusion probabilities essentially assuming zero error covariance between all pairs of population elements. Design properties that link two units are not used. However, if population error structure is correlated, an unbiased estimate of the linear model error covariance matrix for the sample is needed for efficient parameter estimation. By making simultaneous use of sampling structure and design-unbiased estimates of the population error covariance matrix, the paper develops best linear unbiased estimation (BLUE) type extensions to standard design-based and joint design and model based estimation methods for linear models. The analysis covers both with and without replacement sample designs. It recognises that estimation for with replacement designs requires generalized inverses when any unit is selected more than once. This and the use of Hadamard products to link sampling and population error covariance matrix properties are central topics of the paper. Model-based linear model parameter estimation is also discussed.

A Compensation Method for Airborne SAR with Varying Accelerated Motion Error

Motion error is one of the most serious problems in airborne synthetic aperture radar (SAR) data processing. For a smoothly distributing backscatter scene or a seriously speed-varying velocity platform, the autofocusing performances of conventional algorithms, e.g., map-drift (MD) or phase gradient autofocus (PGA) are limited by their estimators. In this paper, combining the trajectories measured by global position system (GPS) and inertial navigation system (INS), we propose a novel error compensation method for varying accelerated airborne SAR based on the best linear unbiased estimation (BLUE). The proposed compensating method is particularly intended for varying acceleration SAR or homogeneous backscatter scenes, the processing procedures and computational cost of which are much simpler and lower than those of MD and PGA algorithms.