多元回归用matlab怎么算 - 数学建模社区-数学中国

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.
[B,BINT] = regress(Y,X) returns a matrix BINT of 95% confidence
intervals for B.
[B,BINT,R] = regress(Y,X) returns a vector R of residuals.
[B,BINT,R,RINT] = 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.
[B,BINT,R,RINT,STATS] = 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.

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