推荐回答(5个)
R方为决定系数,即拟合模型所能解释的因变量的变化百分比。例如,R方=0.810,说明拟合方程能解释因变量变化的81%,不能解释的19%。
F是方差检验,整个模型的全局检验,看拟合方程是否有意义
T值是对每个自变量进行一个接一个的检验(logistic回归),看其beta值,即回归系数是否有意义
F和T的显著性均为0.05,
回归分析在科学研究领域是最常用的统计方法。《SPSS回归分析》介绍了一些基本的统计方法,例如,相关、回归(线性、多重、非线性)、逻辑(二项、多项)、有序回归和生存分析(寿命表法、Kaplan-Meier法以及Cox回归)。
SPSS是世界上最早的统计分析软件。1968年,斯坦福大学的三位研究生NormanH.Nie,C.Hadlai(Tex)Hull和DaleH.Bent成功地进行了研究和开发。同时成立了SPSS公司。

扩展资料:
原理:
这种表示取决于变量Y中可由控制变量X解释的变化百分比。
决定系数不等于相关系数的平方。这个和相关系数之间的区别是如果你去掉|,R|等于0和1,
由于R2
决定系数:在Y的平方和中,X引起的平方和所占的比例为R2
相关程度由决定系数的程度决定。
R2越接近1,相关方程的参考值越大。反之,越接近0,参考值越低。这就是一元回归分析的情况。但是决定系数和回归系数本质上是不相关的就像标准差和标准误差本质上是不相关的一样。
在多元回归分析中,决定系数为路径系数的平方。
表达式:R2=SSR/SST=1-SSE/SST
其中:SST=SSR+SSE,SST (total sum of squares)为总平方和,SSR (regression sum of squares)为回归平方和,SSE (error sum of squares) 为残差平方和。
参考资料来源:百度百科-SPSS回归分析
t值、f值都是判断显著性的过程值,重点看P值即可。
F值用于判定模型中是否自变量X中至少有一个对因变量Y产生影响,如果呈现出显著性(看P值),则说明所有X中至少一个会对Y产生影响关系。
T值用于判断每个自变量的显著性,如果显著则说明该变量对模型有显著影响。
可是使用spssau进行分析,直接得出文字结果及标准格式数据。


首先R太小
F值是整个回归模型的显著性
T是各个自变量的显著性
你这里没有给出各个自变量的,你可以把里面的回归不好的自变量剔除掉再回归试试
另外SIG太大了,你这模型是无效的
你这是股票的问题。给你找个人,你问他吧。巴菲特股神巴菲特,想必你对这个人是知道的。你请他吃顿饭,然后和他切磋交流股市那个是大神的,什么都知道。
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