一切都在：

http://en.wikipedia.org/wiki/Ordinary_least_squares.但是这里有一段代码,在R中显示类似于summary(lmAvgs)的输出.要运行它,你需要

GSL Library：

int n = 4;

double x[4] = { 1970, 1980, 1990, 2000};

double y[4] = {1.23, 11.432, 14.653, 21.6534};

double c0, c1, cov00, cov01, cov11, sumsq;

gsl_fit_linear (x, 1, y, 1, n, &c0, &c1, &cov00, &cov01, &cov11, &sumsq);

cout<|t|)"<

double stdev0=sqrt(cov00);

double t0=c0/stdev0;

double pv0=t0<0?2*(1-gsl_cdf_tdist_P(-t0,n-2)):2*(1-gsl_cdf_tdist_P(t0,n-2));//This is the p-value of the constant term

cout<

double stdev1=sqrt(cov11);

double t1=c1/stdev1;

double pv1=t1<0?2*(1-gsl_cdf_tdist_P(-t1,n-2)):2*(1-gsl_cdf_tdist_P(t1,n-2));//This is the p-value of the linear term

cout<

double dl=n-2;//degrees of liberty

double ym=0.25*(y[0]+y[1]+y[2]+y[3]); //Average of vector y

double sct=pow(y[0]-ym,2)+pow(y[1]-ym,2)+pow(y[2]-ym,2)+pow(y[3]-ym,2); // sct = sum of total squares

double R2=1-sumsq/sct;

cout<

double F=R2*dl/(1-R2);

double p_value=1-gsl_cdf_fdist_P(F,1,dl);

cout<

这使 ：

Coefficients Estimate Std. Error t value Pr(>|t|)

Intercept -1267.91 181.409 -6.98922 0.0198633

x 0.644912 0.0913886 7.05681 0.0194956

Multiple R-squared: 0.961389, Adjusted R-squared: 0.942083

F-statistic: 49.7986 on 1 and 2 DF, p-value: 0.0194956

R给出：

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) -1.268e+03 1.814e+02 -6.989 0.0199 *

c(1970, 1980, 1990, 2000) 6.449e-01 9.139e-02 7.057 0.0195 *

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 2.044 on 2 degrees of freedom

Multiple R-squared: 0.9614, Adjusted R-squared: 0.9421

F-statistic: 49.8 on 1 and 2 DF, p-value: 0.01950