军事医学
軍事醫學
군사의학
BULLETIN OF THE ACADEMY OF MILITARY MEDICAL SCIENCES
2015年
5期
380-385
,共6页
随机模拟%复杂随机抽样%观测权重%抽样权重%多重线性回归分析
隨機模擬%複雜隨機抽樣%觀測權重%抽樣權重%多重線性迴歸分析
수궤모의%복잡수궤추양%관측권중%추양권중%다중선성회귀분석
random simulation%complex random sampling%observation weight%sampling weight%multiple linear regression analysis
目的：探讨综合权重在复杂随机抽样数据线性回归分析中的意义和作用。方法基于蒙特卡洛随机模拟思想，采用SAS中REG和SURVEYREG两个不同的多重线性回归分析过程，分别对同一批复杂随机抽样数据（ n＝6756）在不同随机抽样率条件下进行回归建模，对所得结果进行比较。结果在未考虑和考虑观测权重与抽样权重的多重线性回归模型拟合的结果中，自变量的偏回归系数、标准误及P值的大小均有所不同。结论在对基于不同抽样率的复杂随机抽样资料，尤其是分层随机抽样调查资料的回归建模中，采用多重线性回归模型拟合资料时，将调查数据的综合权重纳入统计分析，方能更准确、灵敏地进行回归系数的参数估计和对结果变量的统计预测。
目的：探討綜閤權重在複雜隨機抽樣數據線性迴歸分析中的意義和作用。方法基于矇特卡洛隨機模擬思想，採用SAS中REG和SURVEYREG兩箇不同的多重線性迴歸分析過程，分彆對同一批複雜隨機抽樣數據（ n＝6756）在不同隨機抽樣率條件下進行迴歸建模，對所得結果進行比較。結果在未攷慮和攷慮觀測權重與抽樣權重的多重線性迴歸模型擬閤的結果中，自變量的偏迴歸繫數、標準誤及P值的大小均有所不同。結論在對基于不同抽樣率的複雜隨機抽樣資料，尤其是分層隨機抽樣調查資料的迴歸建模中，採用多重線性迴歸模型擬閤資料時，將調查數據的綜閤權重納入統計分析，方能更準確、靈敏地進行迴歸繫數的參數估計和對結果變量的統計預測。
목적：탐토종합권중재복잡수궤추양수거선성회귀분석중적의의화작용。방법기우몽특잡락수궤모의사상，채용SAS중REG화SURVEYREG량개불동적다중선성회귀분석과정，분별대동일비복잡수궤추양수거（ n＝6756）재불동수궤추양솔조건하진행회귀건모，대소득결과진행비교。결과재미고필화고필관측권중여추양권중적다중선성회귀모형의합적결과중，자변량적편회귀계수、표준오급P치적대소균유소불동。결론재대기우불동추양솔적복잡수궤추양자료，우기시분층수궤추양조사자료적회귀건모중，채용다중선성회귀모형의합자료시，장조사수거적종합권중납입통계분석，방능경준학、령민지진행회귀계수적삼수고계화대결과변량적통계예측。
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 .
