Multiple linear regression analysis methods for complex random sampled data and their application
10.7644/j.issn.1674-9960.2015.05.015
- VernacularTitle:复杂随机抽样数据的多重线性回归分析方法及其应用
- Author:
Riyang SUN
;
Liangping HU
- Publication Type:Journal Article
- Keywords:
random simulation;
complex random sampling;
observation weight;
sampling weight;
multiple linear regression analysis
- From:
Military Medical Sciences
2015;(5):380-385
- CountryChina
- Language:Chinese
-
Abstract:
Objective To study the significance and function of the comprehensive weight in multiple linear regression analysis of complex random sampled data .Methods Based on the concept of Monte Carlo random simulation , two different multiple linear regression analysis procedures in SAS-REG and SURVEYREG were used to perform regression modeling for the same batch of complex random sampled data ( n=6756 ) at different random sampling proportions .The results were compared.Results In the results of the fitting multiple linear regression model when observation weight and sampling weight were considered or not , it was found that the size of the partial regression coefficient , standard error and P value of independent variables varied .Conclusion In complex random sampled data based on different proportions ,especially in regression modeling of stratified random sampling survey information , the multiple linear regression model makes it possible to more accurately and sensitively perform parameter estimates of regression coefficients and statistical prediction of outcome variables if the comprehensive weight of the survey data is incorporated into the statistical analysis .
