多元回归用matlab怎么算
多元回归用matlab怎么算呀？不会，有没有示例程序呀？包括求参数，求残差，求置信区间，最后怎么检验？ 额 我也需要 给个实例，好点儿。。。意思是给个题目 不需要例子的话，直接百度、google就好了。很多很多 本帖最后由 liwenhui 于 2012-8-31 23:35 编辑使用函数，regress()，具体调用方式见matlab的帮助。regress Multiple linear regression using least squares.
B = regress(Y,X) returns the vector B of regression coefficients in the
linear model Y = X*B. X is an n-by-p design matrix, with rows
corresponding to observations and columns to predictor variables. Y is
an n-by-1 vector of response observations.
= regress(Y,X) returns a matrix BINT of 95% confidence
intervals for B.
= regress(Y,X) returns a vector R of residuals.
= regress(Y,X) returns a matrix RINT of intervals that
can be used to diagnose outliers. If RINT(i,:) does not contain zero,
then the i-th residual is larger than would be expected, at the 5%
significance level. This is evidence that the I-th observation is an
outlier.
= regress(Y,X) returns a vector STATS containing, in
the following order, the R-square statistic, the F statistic and p value
for the full model, and an estimate of the error variance.
[...] = regress(Y,X,ALPHA) uses a 100*(1-ALPHA)% confidence level to
compute BINT, and a (100*ALPHA)% significance level to compute RINT.
X should include a column of ones so that the model contains a constant
term. The F statistic and p value are computed under the assumption
that the model contains a constant term, and they are not correct for
models without a constant. The R-square value is one minus the ratio of
the error sum of squares to the total sum of squares. This value can
be negative for models without a constant, which indicates that the
model is not appropriate for the data.
If columns of X are linearly dependent, regress sets the maximum
possible number of elements of B to zero to obtain a "basic solution",
and returns zeros in elements of BINT corresponding to the zero
elements of B.
regress treats NaNs in X or Y as missing values, and removes them. liwenhui 发表于 2012-8-27 19:58 static/image/common/back.gif
使用函数，regress()，具体调用方式见matlab的帮助。
regress Multiple linear regression using least ...
果断有道翻译了一下 数据输入：x1=; x2=; y=;x=;回归、检验、预测：rstool(x,y,'purequadratic') 之后得到一个交互画面，给出两幅图形，左边是x2固定时的曲线y(x1)及其置信区间，右边是x1固定时的曲线y(x2)及其置信区间。假如X1为平均收入，x2为价格，y为商品需求量，改变X1或者X2，画面左边的‘predicted y’下方的数据就为对应的y值。 在画面左下方的下拉式菜单中选‘all’，则beta、rmse和residuals都传送到matlab工作区中，在matlab工作区中输入命令 beta，rmse就能得到结果了·~ 呵呵，要是楼上的把它变成图文形式就更好了。。。
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