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zhangwayne: ½ð±Ò+1, ¡ïÓаïÖú 2014-08-18 09:33:54
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4Â¥: Originally posted by ssssllllnnnn at 2014-08-17 00:00:19
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when the intercept in the regression line between y and x is understood to be zero £ºy¡¢x»Ø¹éÖ±ÏߵĽؾàΪÁã¡£Õâ¸öºÍ YÓÚXµÄ»Ø¹éÖ±Ïß ÊÇÒ»ÑùµÄ°¡¡£
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zhangwayne: ½ð±Ò+2, ·­ÒëEPI+1, ¡ï¡ï¡ïºÜÓаïÖú 2014-08-18 09:33:03
the regression line of Y on X
2Â¥2014-08-16 22:36:13
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zhangwayne: ½ð±Ò+3, ¡ï¡ï¡ïºÜÓаïÖú 2014-08-18 09:33:13
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(ii) Ratio estimate in double sampling

Ratio estimate is used mainly when the intercept in the regression line between y and x is understood to be zero. The ratio estimate of the population mean is given by

(5.43)

where  denotes the ratio estimate using double sampling. The variance of the estimate is approximately given by
the regression line of Y on X µÄÓ÷¨²»ºÏÌâÒ⣬Ó÷¨ÈçÏÂ

In statistical inference based on regression coefficients, yes; in predictive modelling applications, correction is neither necessary nor appropriate. To understand this, consider the measurement error as follows. Let y be the outcome variable, x be the true predictor variable, and w be an approximate observation of x. Frost and Thompson[2] suggest, for example, that x may be the true, long-term blood pressure of a patient, and w may be the blood pressure observed on one particular clinic visit. Regression dilution arises if we are interested in the relationship between y and x, but estimate the relationship between y and w. Because w is measured with variability, the slope of a regression line of y on w is less than the regression line of y on x.
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zhangwayne: ½ð±Ò+2, ¡ï¡ï¡ïºÜÓаïÖú 2014-08-18 09:33:34
zhangwayne: ½ð±Ò+2 2014-08-18 09:34:14
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4Â¥2014-08-17 00:00:19
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