估计总生育率和避孕避免生育:回归方法

K. K. Singh, Brijesh P Singh, Kushagra Gupta, S. Dst-Cim
{"title":"估计总生育率和避孕避免生育:回归方法","authors":"K. K. Singh, Brijesh P Singh, Kushagra Gupta, S. Dst-Cim","doi":"10.5923/J.STATISTICS.20120205.01","DOIUrl":null,"url":null,"abstract":"Fert ility plays an important ro le in any demographic transition and total fert ility rate (TFR) is one of the basic measurements of fert ility. Due to non-availability of co mp lete and reliable data, a large nu mber of indirect techniques have been developed to estimate the demographic parameters with incomp lete data. So me of these techniques are based on utilizing the data fro m stable population theory while others are based on the regression technique in which the parameters are estimated through regression equations between the dependent variable which is the TFR and the independent variables which is the socio economic well as demographic variables. In the present paper an indirect method has been proposed to estimate the TFR using regression analysis. In these types of analysis the most serious problem is the choice of predictor variable. If the choice of predictor variab le is good then it gives the better estimate for the dependent variable (TFR). Using new predictor variab le (proposed in this paper), the improved model exp lained about 85 percent of the variation in TFR. The findings reveal that the values of TFR calculated by the present method are quite close to the observed values of the TFR without involving much computational comp lexities at state level for different background characteristics. By using this modified estimate of TFR, the demographers can easily calculate the birth averted for different regions as well as states also.","PeriodicalId":91518,"journal":{"name":"International journal of statistics and applications","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2012-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"10","resultStr":"{\"title\":\"Estimation of Total Fertility Rate and Birth Averted due to Contraception: Regression Approach\",\"authors\":\"K. K. Singh, Brijesh P Singh, Kushagra Gupta, S. Dst-Cim\",\"doi\":\"10.5923/J.STATISTICS.20120205.01\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Fert ility plays an important ro le in any demographic transition and total fert ility rate (TFR) is one of the basic measurements of fert ility. Due to non-availability of co mp lete and reliable data, a large nu mber of indirect techniques have been developed to estimate the demographic parameters with incomp lete data. So me of these techniques are based on utilizing the data fro m stable population theory while others are based on the regression technique in which the parameters are estimated through regression equations between the dependent variable which is the TFR and the independent variables which is the socio economic well as demographic variables. In the present paper an indirect method has been proposed to estimate the TFR using regression analysis. In these types of analysis the most serious problem is the choice of predictor variable. If the choice of predictor variab le is good then it gives the better estimate for the dependent variable (TFR). Using new predictor variab le (proposed in this paper), the improved model exp lained about 85 percent of the variation in TFR. The findings reveal that the values of TFR calculated by the present method are quite close to the observed values of the TFR without involving much computational comp lexities at state level for different background characteristics. By using this modified estimate of TFR, the demographers can easily calculate the birth averted for different regions as well as states also.\",\"PeriodicalId\":91518,\"journal\":{\"name\":\"International journal of statistics and applications\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2012-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International journal of statistics and applications\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.5923/J.STATISTICS.20120205.01\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International journal of statistics and applications","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.5923/J.STATISTICS.20120205.01","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 10

摘要

生育率在任何人口转型中都起着重要的作用,总生育率是衡量生育率的基本指标之一。由于无法获得完整的信件和可靠的数据,大量的间接技术已被开发用于估计不完整信件数据的人口统计参数。所以这些技术中有一些是基于利用稳定人口理论的数据,而另一些是基于回归技术,其中参数是通过回归方程来估计的因变量是TFR和自变量是社会经济和人口变量。本文提出了一种利用回归分析估算总生育率的间接方法。在这些类型的分析中,最严重的问题是预测变量的选择。如果预测变量的选择是好的,那么它给出了更好的估计因变量(TFR)。使用新的预测变量(本文提出),改进的模型可以预测约85%的TFR变化。研究结果表明,该方法计算的TFR值与观测值非常接近,并且在不同背景特征下不涉及太多的状态计算复杂度。利用这一修正的TFR估计,人口学家可以很容易地计算出不同地区和州的避免生育。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Estimation of Total Fertility Rate and Birth Averted due to Contraception: Regression Approach
Fert ility plays an important ro le in any demographic transition and total fert ility rate (TFR) is one of the basic measurements of fert ility. Due to non-availability of co mp lete and reliable data, a large nu mber of indirect techniques have been developed to estimate the demographic parameters with incomp lete data. So me of these techniques are based on utilizing the data fro m stable population theory while others are based on the regression technique in which the parameters are estimated through regression equations between the dependent variable which is the TFR and the independent variables which is the socio economic well as demographic variables. In the present paper an indirect method has been proposed to estimate the TFR using regression analysis. In these types of analysis the most serious problem is the choice of predictor variable. If the choice of predictor variab le is good then it gives the better estimate for the dependent variable (TFR). Using new predictor variab le (proposed in this paper), the improved model exp lained about 85 percent of the variation in TFR. The findings reveal that the values of TFR calculated by the present method are quite close to the observed values of the TFR without involving much computational comp lexities at state level for different background characteristics. By using this modified estimate of TFR, the demographers can easily calculate the birth averted for different regions as well as states also.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信