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线性回归方程的最小二乘法参数估计（可选择是否包含常数项），返回回归方程的系数，如果有常数项则排在第一项
范例(t):
[Code]
Y:=array(0.001,0.564,0.193,0.809,0.585,0.48,0.35,0.896,0.823,0.747);
X:= array(
(0…

7.1节中的例子只是一个简单的单变量线性回归模型，下面我们介绍下更具一般性的多元线性回归模型的理论.
多元线性回归的一般形式是
[center][img id=42145][/im…

范例(t):
[code]
Y:=array(0.001,0.564,0.193,0.809,0.585,0.48,0.35,0.896,0.823,0.747);
X:= array(
(0…

范例(t):
[Code]
Y:=array(0.001,0.564,0.193,0.809,0.585,0.48,0.35,0.896,0.823,0.747);
X:= array(
(0…

范例(t):
[code]
Y:=array(0.001,0.564,0.193,0.809,0.585,0.48,0.35,0.896,0.823,0.747);
X:= array(
(0…

范例(t):
[htm]<table><tbody><tr><td>
年份</td><td>
消费价格指数CPI
X1（以1978年为100）</td><td>
人均可支配收入
X2（元）…

范例(t):
在一次关于某城镇居民上下班使用交通工具的社会调查中，因变量y =1表示居民主要乘坐公共汽车上下班；y=0表示主要骑自行车上下班；自变量x1表示被调查者的年龄；x2表示被调查者的月收入；…

范例(t):

在一次关于某城镇居民上下班使用交通工具的社会调查中，

因变量y =1表示居民主要乘坐公共汽车上下班；y=0表示主要骑自行车上下班；

自变量x1表示被调查者的年龄； …

范例(t):
[Code]
y := array(0.425306623295765,1.36119535984939,0.330434097687351,0.693363166256445, 1…

范例(t):

[code]

U:=array(0.245863,0.056726,-0.145411,-0.287547,-0.410684,-0.012821,0.073042,0.…

范例(t):

Return Regress_AdjustedR2(0.942066,9,1);

//结果：0.93379

参考:[ref]Re…

范例(t):
[code]
U:=array(0.245863,0.056726,-0.145411,-0.287547,-0.410684,0.012821,0.073042,0.201905,…

范例(t):

[code]

U:=array(0.245863,0.056726,-0.145411,-0.287547,-0.410684,-0.012821,0.073042,…

范例(t):
[code]
U:=array(0.245863,0.056726,-0.145411,-0.287547,-0.410684,0.012821,0.073042, 0.201905…

范例(t):
[code]
Y:=array(0.564,0.693,0.809,0.985,1.18,1.896,2.3,2.747,3);
return regress_jbtest(y,0…

范例(t):

[code]
U:=array(0.245863,0.056726,-0.145411,-0.287547,-0.410684,0.012821,0.073042,0.20190…

范例(t):
[code]
x:=array(554.61,562.47,584.42,587.43,600.71,622.9,610.19,624.33,608.8,584.74,590.36,…

范例(t):
[code]
//对序列s跟gdp进行加权最小二乘法估计，权重数列为1/gdp//
s:=array(2010.02,1055.17,2660.93,919.23,847.89,1…

范例(t):
[code]
Y:=array(0.564,0.693,0.809,0.985,1.18,1.896,2.3,2.747,3);
X:=`array(1,2,3,4,5,6,7,8…

范例(t):
[code]
Y:=array(0.001,0.564,0.193,0.809,0.585,0.48,0.35,0.896,0.823,0.747);
X:= array(…

范例(t):
[code]
Y:=array(0.001,0.564,0.193,0.809,0.585,0.48,0.35,0.896,0.823,0.747);
X:= array(…