[例10-1]某地一年级12名女学生的体重（Kg）x与肺活量（L）y数据如下图所示，试用直线回归方程描述其关系。
42.00 2.55 42.00 2.20 46.00 2.75 46.00 2.40 46.00 2.80 50.00 2.81 50.00 3.41 50.00 3.10 52.00 3.46 52.00 2.85 58.00 3.50 58.00 3.00 1.首先使用系统默认选择项进行线性回归分析
Variables Entered/Removed(b) Model 1 Variables Entered X(a) Variables Removed . Method Enter a All requested variables entered. b Dependent Variable: Y Model Summary Model 1 R .749(a) R Square .562 Adjusted R Square .518 Std. Error of the Estimate .28775 a Predictors: (Constant), X Model 1 Regression Residual Total Sum of Squares 1.061 .828 1.8 ANOVA(b)
df 1 10 11 Mean Square 1.061 .083 F 12.817 Sig. .005(a) a Predictors: (Constant), X b Dependent Variable: Y Variables Entered/Removed(b) Model 1 Variables Entered X(a) Variables Removed . Method Enter a All requested variables entered. b Dependent Variable: Y
Coefficients(a)
Unstandardized Coefficients Model 1 (Constant) X B .000 .059 Std. Error .815 .016 Standardized Coefficients Beta .749 t .001 3.580 Sig. 1.000 .005 a Dependent Variable: Y
2，使用选择项线性回归分析
Y X Pearson Correlation Y X X N Model 1 Y X Descriptive Statistics Mean 2.9025 49.3333 Std. Deviation .41442 5.28004 Correlations
Y 1.000 .749 . .003 12 12 X .749 1.000 .003 . 12 12 N 12 12 Sig. (1-tailed) Y Variables Entered/Removed(b) Variables Entered X(a) Variables Removed . Method Enter a All requested variables entered. b Dependent Variable: Y Model Summary(b) Model 1 R .749(a) R Square .562 Adjusted R Square .518 Std. Error of the Estimate .28775 a Predictors: (Constant), X b Dependent Variable: Y Model 1 Regression Residual Total Sum of Squares 1.061 .828 1.8 ANOVA(b)
df 1 10 11 Mean Square 1.061 .083 F 12.817 Sig. .005(a) a Predictors: (Constant), X b Dependent Variable: Y Unstandardized Coefficients Model 1 Coefficients(a)
Standardized Coefficients t .001 3.580 Sig. 1.000 .005 95% Confidence Interval for B Lower Bound Upper Bound -1.815 .022 1.816 .095 B Std. Error Beta (Constan.000 .815 t) X .059 .016 .749 a Dependent Variable: Y Casewise Diagnostics(a) Case Number 1 2 3 4 5 6 7 8 9 10 11 12 a Dependent Variable: Y Predicted Value Std. Predicted Value Standard Error of Predicted Value Adjusted Predicted Value Residual Std. Residual Stud. Residual Deleted Residual Stud. Deleted Residual Mahal. Distance Cook's Distance Centered Leverage Value a Dependent Variable: Y
Std. Residual .274 -.942 .151 -1.065 .325 -.458 1.627 .550 1.392 -.728 .305 -1.433 Y 2.55 2.20 2.75 2.40 2.80 2.81 3.41 3.10 3.46 2.85 3.50 3.00 Predicted Value 2.4711 2.4711 2.70 2.70 2.70 2.9417 2.9417 2.9417 3.0594 3.0594 3.4123 3.4123 Residual .07 -.2711 .0436 -.30 .0936 -.1317 .4683 .1583 .4006 -.2094 .0877 -.4123 Residuals Statistics(a)
Minimum 2.4711 -1.3 .08379 2.4436 -.4123 -1.433 -1.748 -.6138 -1.991 .016 .002 .001 Maximum 3.4123 1.1 .186 3.6138 .4683 1.627 1.701 .5117 1.914 2.694 .747 .245 Mean 2.9025 .000 .11334 2.9175 .0000 .000 -.023 -.0150 -.023 .917 .119 .083 Std. Deviation .31060 1.000 .03225 .33055 .27436 .953 1.050 .33567 1.132 1.065 .208 .097 N 12 12 12 12 12 12 12 12 12 12 12 12 HistogramDependent Variable: Y5432Frequency1Std. Dev = .95 Mean = 0.00N = 12.00-1.50-1.00-.500.00.501.001.500Regression Standardized Residual Normal P-P Plot of Regression Standardized ResidualDependent Variable: Y1.00.75.50Expected Cum Prob.250.000.00.25.50.751.00Observed Cum Prob ScatterplotDependent Variable: Y.6.4.20.0-.2-.4-.6-.8-1.5-1.0-.50.0.51.01.52.0Regression Standardized Residual42.00 42.00 46.00 46.00 46.00 50.00 50.00 50.00 52.00 52.00 58.00 58.00 2.55 2.20 2.75 2.40 2.80 2.81 3.41 3.10 3.46 2.85 3.50 3.00 2.47111 2.47111 2.701 2.701 2.701 2.94172 2.94172 2.94172 3.05937 3.05937 3.41233 3.41233 .078 -.27111 .04359 -.301 .09359 -.13172 .46828 .15828 .40063 -.20937 .08767 -.41233 .14636 .14636 .09950 .09950 .09950 .08379 .08379 .08379 .09391 .09391 .186 .186 2.14501 2.14501 2.48472 2.48472 2.48472 2.75503 2.75503 2.75503 2.85011 2.85011 3.04499 3.04499 2.79721 2.79721 2.92811 2.92811 2.92811 3.12840 3.12840 3.12840 3.26863 3.26863 3.77967 3.77967
