Practical consequence of the noise spectral density
The 6.6 presumably is some number related to statistical
  probably of the resulting peak to peak voltage


When you have gaussian noise you have a picture like this: -



The RMS value of the noise occurs at 1\$\sigma\$ (one standard deviation) and that means the signal is constrained as a peak-to peak signal to this RMS number for 68% of the time. That's not great for estimating p-p amplitude so, some people go for 6 \$\sigma\$ i.e. multiply the RMS value by 6 to get p-p but that is only true 99.7% of the time. Some people use 6.6 \$\sigma\$ and that guarantees the p-p value is constrained in amplitude to 6.6 x RMS for 99.9% of the time. This is the general norm but, some folk might want to go much higher and maybe use 7 or 8: -




  If the frequency of interest is from 1 Hz to 100 Hz, what would be the
  resulting peak-to-peak voltage variation of the output?


Your CT's data sheet appears to say that the spectral density is constant from DC i.e. there is no flicker-noise (low frequency) effects so, if you have a bandwidth of 1 Hz to 100 Hz then the output noise will be: -

6.13 uV x \$\sqrt{99}\$ = 61 uV RMS (402.6 uVp-p using 6.6 \$\sigma\$).

However, this assumes a perfect brick-wall filter above 100 Hz. If you have a 1st order, low pass filter shaping the upper spectrum, the voltage noise will be \$\sqrt{\pi/2}\$ times higher at 504.5 uVp-p or 76.4 uV RMS. The hike of \$\sqrt{\pi/2}\$ is due to the noise equivalent bandwidth (in case you want to look it up). Here is a table that relates the filter order to the factor you have to use for powers and, thanks to @carloc, correcting my earlier mistake, these values need to be square rooted when talking about voltage noise: -



Also, the above table is for a butterworth type filtering. If different filters are used, the values are somewhat altered.