最小二乘法的计算,数据y:0.000,0.079,0.157,0.238,0.314,9.393,0.441x:0,1.117,2.234.3.351,4.468,5.585
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![最小二乘法的计算,数据y:0.000,0.079,0.157,0.238,0.314,9.393,0.441x:0,1.117,2.234.3.351,4.468,5.585](/uploads/image/z/3632714-26-4.jpg?t=%E6%9C%80%E5%B0%8F%E4%BA%8C%E4%B9%98%E6%B3%95%E7%9A%84%E8%AE%A1%E7%AE%97%2C%E6%95%B0%E6%8D%AEy%3A0.000%2C0.079%2C0.157%2C0.238%2C0.314%2C9.393%2C0.441x%3A0%2C1.117%2C2.234.3.351%2C4.468%2C5.585)
最小二乘法的计算,数据y:0.000,0.079,0.157,0.238,0.314,9.393,0.441x:0,1.117,2.234.3.351,4.468,5.585
最小二乘法的计算,
数据y:0.000,0.079,0.157,0.238,0.314,9.393,0.441
x:0,1.117,2.234.3.351,4.468,5.585
最小二乘法的计算,数据y:0.000,0.079,0.157,0.238,0.314,9.393,0.441x:0,1.117,2.234.3.351,4.468,5.585
我是在EViews 中计算的.
首先将 x y数据输入EViews中,查看散点图,近似一条直线,因此,设线性的
y =a+bx.
ls y c x ------EViews中进行最小二乘法运算.
运算结果:
Dependent Variable:Y
Method:Least Squares
Date:12/04/08 Time:13:12
Sample:1 6
Included observations:6
Variable Coefficient Std.Error t-Statistic Prob.
C 0.000333 0.000770 0.432627 0.6876
X 0.070367 0.000228 308.8608 0.0000
R-squared 0.999958 Mean dependent var 0.196833
Adjusted R-squared 0.999948 S.D.dependent var 0.147050
S.E.of regression 0.001065 Akaike info criterion -10.59127
Sum squared resid 4.53E-06 Schwarz criterion -10.66068
Log likelihood 33.77381 F-statistic 95394.97
Durbin-Watson stat 2.911765 Prob(F-statistic) 0.000000
线性方程:
Y = 0.0003333333333 + 0.07036705461*X