相關性分析裡面r值和p值得意義：
高度正相關
esidual standard error: 0.008671 on
98 degrees of freedom
Multiple R-squared:
0.9996, Adjusted
R-squared: 0.9996
F-statistic: 2.286e+05 on 1 and 98
DF, p-value: < 2.2e-16
一般正相關
Residual standard error: 0.301 on 98
degrees of freedom
Multiple R-squared:
0.3929, Adjusted
R-squared: 0.3867
F-statistic: 63.41 on 1 and 98 DF,
p-value: 3.038e-12
一般負相關
Residual standard error: 0.2905 on
98 degrees of freedom
Multiple R-squared:
0.4207, Adjusted
R-squared: 0.4147
F-statistic: 71.16 on 1 and 98 DF,
p-value: 2.959e-13
高度負相關
Residual standard error: 0.008996 on
98 degrees of freedom
Multiple R-squared:
0.9995, Adjusted
R-squared: 0.9995
F-statistic: 2.007e+05 on 1 and 98
DF, p-value: < 2.2e-16
不相關：
Residual standard error: 0.3999 on
98 degrees of freedom
Multiple R-squared:
0.02108, Adjusted
R-squared: 0.01109
F-statistic: 2.11 on 1 and 98 DF,
p-value: 0.1495
這是我用兩個随機數種生成的100個點，完全不相關，但是很遺憾R-squared: 0.02108，換句話說，即使是完全随機的過程要獲得R-squared:
0.014也是要依靠運氣的。
現在我隻有增加樣本了
Residual standard error: 0.3928 on
998 degrees of freedom
Multiple R-squared:
2.47e-05, Adjusted
R-squared: -0.0009773
F-statistic: 0.02465 on 1 and 998
DF, p-value: 0.8753
好了，這次R-squared:
-0.0009773，樣本增加到了1000個點。p-value: 0.8753