ScatterplotDependent Variable: 物理100908070物理60-2.0-1.5-1.0-.50.0.51.01.52.0Regression Standardized Predicted Value Model Summary(b)

Adjusted R Model 1 R .836(a) R Square .699 Square .683 Std. Error of the Estimate 5.46939 a Predictors: (Constant), 数学 b Dependent Variable: 物理

ANOVA(b)

Sum of Model 1 Regression Residual Total Squares 1252.095 538.455 1790.550 df 1 18 19 Mean Square 1252.095 29.914 F 41.856 Sig. .000(a) a Predictors: (Constant), 数学 b Dependent Variable: 物理

Coefficients(a)

Unstandardized Coefficients Model B Std. Error Standardized Coefficients Beta t Sig.

1 (Constant) 数学 8.184 .855 10.576 .132 .836 .774 6.470 .449 .000 a Dependent Variable: 物理 由散点图模型可知，两个因素之间存在一定的相关关系，由回归模型的拟合优度检验结果可知，相关系数R为0.836，，判定系数R2为0.699，校正判定系数为0.683，说明模型的拟合优度较好。由方差分析结果可知，显著性p=0.000<0.05，可以认为两因素之间有线性关系。从线性回归的系数分析结果可知，常数项显著性

p=0.449>0.05，说明该常数项应为0。因此重新选择去除常数项，即

Model Summary

R Model 1 R .998(b) Square(a) .995 Adjusted R Square .995 Std. Error of the Estimate 5.41132 a For regression through the origin (the no-intercept model), R Square measures the proportion of the variability in the dependent variable about the origin explained by regression. This CANNOT be compared

to R Square for models which include an intercept.

b Predictors: 数学 ANOVA(c,d)

Sum of Model 1 Regression Residual Total Squares 117210.634 556.366 117767.000(b) df 1 19 20 a Predictors: 数学 b This total sum of squares is not corrected for the constant because the constant is zero for regression

through the origin. c Dependent Variable: 物理 d Linear Regression through the Origin

Coefficients(a,b) Model Unstandardized Standardized t Sig. Mean Square 117210.634 29.282 F 4002.768 Sig. .000(a)

Coefficients B 1 数学 .957 Std. Error .015 Coefficients Beta .998 63.267 .000 a Dependent Variable: 物理 b Linear Regression through the Origin

由去除常数项后的分析可知，判定系数R2=0.995，矫正判定系数为0.995，模型拟合优度非常好。方差分析中，p=0.000<0.05，说明两因素存在统计学意义。由线性回归分析可得，该线性方程的系数的p=0.000<0.05，具有统计学意义，因此该线性回归方程为y=0.957x。