  We analyzed the soft X-ray light curves from the {\sl Geostationary
Operational Environmental Satellites (GOES)} over the last 37 years (1975-2011)
and measured with an automated flare detection algorithm over 300,000 solar
flare events (amounting to $\approx 5$ times higher sensitivity than the NOAA
flare catalog). We find a powerlaw slope of $\alpha_F=1.98\pm0.11$ for the
(background-subtracted) soft X-ray peak fluxes that is invariant through three
solar cycles and agrees with the theoretical prediction $\alpha_F=2.0$ of the
{\sl fractal-diffusive self-organized criticality (FD-SOC)} model. For the soft
X-ray flare rise times we find a powerlaw slope of $\alpha_T =2.02\pm0.04$
during solar cycle minima years, which is also consistent with the prediction
$\alpha_T=2.0$ of the FD-SOC model. During solar cycle maxima years, the
powerlaw slope is steeper in the range of $\alpha_T \approx 2.0-5.0$, which can
be modeled by a solar cycle-dependent flare pile-up bias effect. These results
corroborate the FD-SOC model, which predicts a powerlaw slope of $\alpha_E=1.5$
for flare energies and thus rules out significant nanoflare heating. While the
FD-SOC model predicts the probability distribution functions of spatio-temporal
scaling laws of nonlinear energy dissipation processes, additional physical
models are needed to derive the scaling laws between the geometric SOC
parameters and the observed emissivity in different wavelength regimes, as we
derive here for soft X-ray emission. The FD-SOC model yields also statistical
probabilities for solar flare forecasting.
