In sampling theory, auxiliary information may be utilized at any of these three stages or by combining two or all of the three stages. These stages are: (1) at the pre-selection stage or designing stage of the survey in stratifying the population; (2) at the sample selection stage; and (3) at the post-selection or estimation stage. In whatever case, the use of auxiliary information in sample survey is better than the case where no auxiliary information is utilized. Ratio, regression, product and difference estimators take advantage of auxiliary information at the estimation stage. However, when the population information is not known then double sampling method becomes necessary for estimation. [1] is of the opinion that estimation of required parameters can efficiently be done with ratio and regression methods of estimation with two-phase sampling or double sampling method. Double sampling for ratio estimation becomes necessary over double sampling for regression estimation if the data under consideration are well fitted by a straight line through the origin [2]. Among the authors who have recently contributed to the use of auxiliary variable(s) to establish various estimators for the population parameters are [3-5]. However, in both cases of ratio and regression estimations or the use of double sampling in ratio and regression estimations, there must exists positive correlation between the auxiliary variable and study variable. This article, empirically, investigates to ascertain the importance of correlation level in the use of auxiliary variable in estimating the population parameter using double sampling for regression estimation method.
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Most common error associated with correlation and regression analysis, as emphasized by [16], is confusing when interpreting correlation coefficient result. The most common error in correlation coefficient interpretation is to conclude that changes in one variable causes changes in the other. Correlation coefficient indicates that characteristics vary together or in opposite direction. However, not interpreting the results of Correlation coefficient is another common error. [16] claims that the coefficient must be interpreted in light of the relationship under study and [18] has given different ways to interpret and estimate for coefficient of determination, though based on theory dependent. 2ff7e9595c
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