从GSL库获取C gsl_fit_linear（）函数中的线性回归的p值

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<<"CoefficientstEstimatetStd. Errortt valuetPr(>|t|)"<<endl;

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<<"Interceptt"<<c0<<"t"<<stdev0<<"t"<<t0<<"t"<<pv0<<endl;

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<<"xt"<<c1<<"t"<<stdev1<<"t"<<t1<<"t"<<pv1<<endl;

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<<"Multiple R-squared: "<<R2<<",    Adjusted R-squared: "<<1-double(n-1)/dl*(1-R2)<<endl;
double F=R2*dl/(1-R2);
double p_value=1-gsl_cdf_fdist_P(F,1,dl);
cout<<"F-statistic:  "<<F<<" on 1 and "<<n-2<<" DF,  p-value: "<<p_value<<endl;

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

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