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ssssllllnnnn
ÖÁ×ðľ³æ (ÖªÃû×÷¼Ò)
Translator and Proofreader
- ·ÒëEPI: 1690
- Ó¦Öú: 452 (˶ʿ)
- ½ð±Ò: 31580.9
- ºì»¨: 100
- Ìû×Ó: 7681
- ÔÚÏß: 19966.6Сʱ
- ³æºÅ: 3328089
- ×¢²á: 2014-07-17
- רҵ: Ö×Áö·¢Éú
2Â¥2014-08-16 22:36:13
RXMCDM
°æÖ÷ (ÎÄ̳¾«Ó¢)
- ·ÒëEPI: 530
- Ó¦Öú: 401 (˶ʿ)
- ¹ó±ö: 1.908
- ½ð±Ò: 38947.6
- É¢½ð: 4908
- ºì»¨: 88
- ɳ·¢: 4
- Ìû×Ó: 11453
- ÔÚÏß: 1355.6Сʱ
- ³æºÅ: 2739168
- ×¢²á: 2013-10-20
- ÐÔ±ð: GG
- רҵ: Ò»°ã¹ÜÀíÀíÂÛÓëÑо¿·½·¨ÂÛ
- ¹ÜϽ: ÂÛÎÄ·Òë
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zhangwayne: ½ð±Ò+3, ¡ï¡ï¡ïºÜÓаïÖú 2014-08-18 09:33:13
zhangwayne: ½ð±Ò+3, ¡ï¡ï¡ïºÜÓаïÖú 2014-08-18 09:33:13
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¶ÔµÄ£¬Ó÷¨ÈçÏ (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÷¨ÈçÏ 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. |

3Â¥2014-08-16 23:28:23
ssssllllnnnn
ÖÁ×ðľ³æ (ÖªÃû×÷¼Ò)
Translator and Proofreader
- ·ÒëEPI: 1690
- Ó¦Öú: 452 (˶ʿ)
- ½ð±Ò: 31580.9
- ºì»¨: 100
- Ìû×Ó: 7681
- ÔÚÏß: 19966.6Сʱ
- ³æºÅ: 3328089
- ×¢²á: 2014-07-17
- רҵ: Ö×Áö·¢Éú
¡¾´ð°¸¡¿Ó¦Öú»ØÌû
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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
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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°æÖ÷дÕâô¶à£¬È´²»ÖªµÀÂ¥Ö÷ÔÚÎÊʲô¡£È˼ÒÔÚÎÊXÓëYµÄ»Ø¹éÖ±Ïߣ¬ºÍRatio estimateµÈ²»ÊÇÒ»»ØÊ¡£Èç¹ûÏëÁ˽âÖ±Ï߻ع鼰Æä±í´ï·½Ê½£¬¿ÉÒÔ¿´¿´ÕâÀ https://www.vitutor.com/statisti ... ear_regression.html |
4Â¥2014-08-17 00:00:19
RXMCDM
°æÖ÷ (ÎÄ̳¾«Ó¢)
- ·ÒëEPI: 530
- Ó¦Öú: 401 (˶ʿ)
- ¹ó±ö: 1.908
- ½ð±Ò: 38947.6
- É¢½ð: 4908
- ºì»¨: 88
- ɳ·¢: 4
- Ìû×Ó: 11453
- ÔÚÏß: 1355.6Сʱ
- ³æºÅ: 2739168
- ×¢²á: 2013-10-20
- ÐÔ±ð: GG
- רҵ: Ò»°ã¹ÜÀíÀíÂÛÓëÑо¿·½·¨ÂÛ
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zhangwayne: ½ð±Ò+1, ¡ïÓаïÖú 2014-08-18 09:33:54
zhangwayne: ½ð±Ò+1, ¡ïÓаïÖú 2014-08-18 09:33:54
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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°¡¡£ The regression line of x on y µÄÒâ˼Ϊ X¶ÔY×÷ͼµÄ»Ø¹éÏß ÕâÁ½¸öµÄÒâ˼»¹ÊÇÓеã²î±ðµÄ¡£ |

5Â¥2014-08-17 23:14:22
ll550
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- Ó¦Öú: 259 (´óѧÉú)
- ½ð±Ò: 4586.3
- É¢½ð: 230
- ºì»¨: 34
- Ìû×Ó: 3331
- ÔÚÏß: 767Сʱ
- ³æºÅ: 3180771
- ×¢²á: 2014-05-05
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6Â¥2014-08-18 00:12:24
ssssllllnnnn
ÖÁ×ðľ³æ (ÖªÃû×÷¼Ò)
Translator and Proofreader
- ·ÒëEPI: 1690
- Ó¦Öú: 452 (˶ʿ)
- ½ð±Ò: 31580.9
- ºì»¨: 100
- Ìû×Ó: 7681
- ÔÚÏß: 19966.6Сʱ
- ³æºÅ: 3328089
- ×¢²á: 2014-07-17
- רҵ: Ö×Áö·¢Éú
|
The regression line of x on y £¬The regression line of y on x ÒÔ¼°The regression line between x and y µÄÒâ˼µÄÈ·ÊDz»Í¬µÄ¡£¾ÍÂ¥Ö÷µÄÎÊÌâ¶øÑÔ£¬ÊÇThe regression line of y on x ¼´¡±YÓÚXµÄ»Ø¹éÖ±Ïß¡°£»¶øThe regression line of x on yÔòΪ¡±XÓÚYµÄ»Ø¹éÖ±Ïß¡°£¬ÕâÔÚʵ¼Êµ±Öм«ÉÙÕâô»Ø¹é¡£The regression line between x and yÔò²»¾ßÌåÖ¸Ã÷ËÓÚ˽øÐлع飬ÊÇÒ»ÖÖÁýͳµÄ˵·¨¡£ |
7Â¥2014-08-18 02:39:07
RXMCDM
°æÖ÷ (ÎÄ̳¾«Ó¢)
- ·ÒëEPI: 530
- Ó¦Öú: 401 (˶ʿ)
- ¹ó±ö: 1.908
- ½ð±Ò: 38947.6
- É¢½ð: 4908
- ºì»¨: 88
- ɳ·¢: 4
- Ìû×Ó: 11453
- ÔÚÏß: 1355.6Сʱ
- ³æºÅ: 2739168
- ×¢²á: 2013-10-20
- ÐÔ±ð: GG
- רҵ: Ò»°ã¹ÜÀíÀíÂÛÓëÑо¿·½·¨ÂÛ
- ¹ÜϽ: ÂÛÎÄ·Òë

8Â¥2014-08-18 09:50:27













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