《医学统计学》课程教学资源：直线回归

sas eIntelligence 直线回归 The power to know

直线回归

sas eIntelligence 编程 可用REG过程: Proc reg data=data d7 1 Model y=x Runa The power to know

一、编程 可用REG过程： Proc reg data=data.d7_1; Model y=x; Run;

sas eIntelligence 分析结果 Analysis of Variance Sum of mean Source DF Squares Square f value prob>F Mode137374106337374106360.1970.0001 Error8496.6893762.08617 C Tota|94234.10000 Root MsE 7.87948 R-square 0.8827 Dep Mean 154.30000 Adj R-Sq 0.8680 5.10660 The power to know

分析结果 Analysis of Variance Sum of Mean Source DF Squares Square F Value Prob>F Model 1 3737.41063 3737.41063 60.197 0.0001 Error 8 496.68937 62.08617 C Total 9 4234.10000 Root MSE 7.87948 R-square 0.8827 Dep Mean 154.30000 Adj R-sq 0.8680 C.V. 5.10660

sas eIntelligence 分析结果 Parameter estimates Parameter Standard T for Ho: Variable DF Estimate Error Parameter=0 Prob>TI NTERCEP1-173574562226443147-078004581 (常数项a) X10.2218940.0285994977590.0001 (回归系数b 回归方程:y=-17.357456+0221894x The power to know

分析结果 Parameter Estimates Parameter Standard T for H0: Variable DF Estimate Error Parameter=0 Prob > |T| INTERCEP 1 -17.357456 22.26443147 -0.780 0.4581 (常数项 a) X 1 0.221894 0.02859949 7.759 0.0001 (回归系数b) 回归方程：y=-17.357456+0.221894x

sas eIntelligence 示准化偏回归系数-stb选项 结果: Standardized Variable Df Estimate INTERCEP 1 0.00000000 1093951745 The power to know

结果： Standardized Variable DF Estimate INTERCEP 1 0.00000000 X 1 0.93951745 标准化偏回归系数--stb选项

sas eIntelligence 残差分析P选项 可计算出数据集中每个观察值的预测值及其标准误,并对残 差进行分析,其输出结果为 Dep var Predict std Err OBS Y Value Predict 1650164.62823 ■■ ■■■ Sum of residuals(残差和) Sum of Squared Residuals(残差平方和) Predicted residua|SS( Press)(预测值的残差平方和) The power to know

残差分析--P选项 可计算出数据集中每个观察值的预测值及其标准误，并对残 差进行分析，其输出结果为： Dep Var Predict Std Err OBS Y Value Predict 1 165.0 164.6 2.823 2 …… …… …… Sum of Residuals （残差和） Sum of Squared Residuals （残差平方和） Predicted Residual SS (Press) （预测值的残差平方和）

sas eIntelligence 测值均数的95%可信区间cm选项 Dep var Predict Std Err Lower95% Upper95% bs y value predict MeanMean Residual 1165.0164.628231581171.10.4041 2158.0155.72498150.016152.2799 3130.014242925135.71492-124065 4180.0175.03.653166.6183.44.9751 5134.0135.73.4531278143.7-1.7496 6167015732.521151.5163.197266 7186.018995.2201779201.9-3.8919 8145.0133.33.678124.8141811.6912 9120.0124.44.586113.9135.0-4.4330 10158.0164.62823158.11711-6.5959 Sum of residuals Sum of Squared residuals 496. 6894 Predicted Resid SS(Press)7497489 The power to know

预测值均数的95%可信区间—clm选项 Dep Var Predict Std Err Lower95% Upper95% Obs Y Value Predict Mean Mean Residual 1 165.0 164.6 2.823 158.1 171.1 0.4041 2 158.0 155.7 2.498 150.0 161.5 2.2799 3 130.0 142.4 2.925 135.7 149.2 -12.4065 4 180.0 175.0 3.653 166.6 183.4 4.9751 5 134.0 135.7 3.453 127.8 143.7 -1.7496 6 167.0 157.3 2.521 151.5 163.1 9.7266 7 186.0 189.9 5.220 177.9 201.9 -3.8919 8 145.0 133.3 3.678 124.8 141.8 11.6912 9 120.0 124.4 4.586 113.9 135.0 -4.4330 10 158.0 164.6 2.823 158.1 171.1 -6.5959 Sum of Residuals 0 Sum of Squared Residuals 496.6894 Predicted Resid SS (Press) 749.7489

sas 测值的参考值范围一c选项 Obep Var Predict Std Err Lower95% Upper95% De s Y Value predict predict predict residua 165.0164.62823145.3183.90.4041 158.0155.72498136.717482.2799 3130.014242925123.01618-12.4065 4180.0175.03.653155.0195.14.9751 5134.0135.73453115.9155.6-17496 6167015732.521138.2176497266 7186.0189.95.220168.1211.7-3.8919 8145.0133.33.678113.3153.411.6912 9120.0124.44586103.4145.5-4.4330 10158.0164.62823145.31839-6.5959 Sum of residuals Sum of Squared residuals 496. 6894 Predicted Resid Ss(Press) 749.7489 The power to know

预测值的参考值范围—cli选项 Dep Var Predict Std Err Lower95% Upper95% Obs Y Value Predict Predict Predict Residual 1 165.0 164.6 2.823 145.3 183.9 0.4041 2 158.0 155.7 2.498 136.7 174.8 2.2799 3 130.0 142.4 2.925 123.0 161.8 -12.4065 4 180.0 175.0 3.653 155.0 195.1 4.9751 5 134.0 135.7 3.453 115.9 155.6 -1.7496 6 167.0 157.3 2.521 138.2 176.4 9.7266 7 186.0 189.9 5.220 168.1 211.7 -3.8919 8 145.0 133.3 3.678 113.3 153.4 11.6912 9 120.0 124.4 4.586 103.4 145.5 -4.4330 10 158.0 164.6 2.823 145.3 183.9 -6.5959 Sum of Residuals 0 Sum of Squared Residuals 496.6894 Predicted Resid SS (Press) 749.7489

sas eIntelligence 交互数据分析 1、交互数据分析→选择数据集 2、 Analyze→ Fit(y x); 3、选择应变量y,点击“y”; 选择自变量x,点击“x 4、OK。 The power to know

二、交互数据分析 1、交互数据分析→选择数据集； 2、Analyze→Fit(y x); 3、选择应变量y，点击“y”； 选择自变量x，点击“x”； 4、OK

sas eIntelligence 、分析员应用 1、分析员应用→选择数据集; 2、 Statistics→ Regression→ Simple.; 3、选择应变量y,点击“ dependent”; 选择自变量x,点击“ independent”; 4、OK。 The power to know

三、分析员应用 1、分析员应用→选择数据集； 2、Statistics→Regression →Simple…； 3、选择应变量y，点击“dependent ”； 选择自变量x，点击“independent”； 4、OK