S_err = np.sqrt(np.sum(resid**2) / dof) # standard deviation of the error actual data from predicted valuesĬhi2 = np.sum((resid / y_model)**2) # chi-squared estimates error in dataĬhi2_red = chi2 / dof # reduced chi-squared measures goodness of fit T = stats.t.ppf(0.975, n - m) # t-statistic used for CI and PI bands N = weights.size # number of observations Y_model = equation(p, x) # model using the fit parameters NOTE: parameters here are coefficients P, cov = np.polyfit(x, y, 1, cov=True) # parameters and covariance from of the fit of 1-D polynom.
#LINEAR REGRESSION SCATTER PLOT CALCULATOR PC#
Pc = sp.polyfit(xs, ys + resamp_resid, 1)Īx.plot(xs, sp.polyval(pc, xs), "b-", linewidth=2, alpha=3.0 / float(nboot))Ĭode # Computations. "Visualizing Confidence Intervals", Various Consequences. Upper and Lower bounds (high and low) (optional) Note: sensitive to outliers The density of overlapping lines indicates improved confidence. It plots `nboot` number of straight lines and outlines the shape of a band. The bootstrap approach iteratively resampling residuals. """Return an axes of confidence bands using a bootstrap approach. """Return an axes of confidence bands using a simple approach. Two detailed options to plot confidence intervals: def plot_ci_manual(t, s_err, n, x, x2, y2, ax=None): I tried to closely emulate your screenshot.
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