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":"55 1","pages":"47-55"},"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\":\"55 1\",\"pages\":\"47-55\"},\"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}
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.