Float type variable uncertainty
The number 1.19e-07 is the difference from 1.0 to the next larger representable floating point number. Single precision floating numbers have the form $x = ± 2^k m$, where k is a small integer, and 1 ≤ m &lt; 2 is a multiple of $2^{-23}$. 

If you calculate a result x, and $2^k ≤ x &lt; 2^{k+1}$, then x will be rounded to the nearest multiple of $2^{k-23}$, with a rounding error up to $2^{k-24}$. 

Don't think of floating-point arithmetic to have "uncertainty". It gives a perfectly fine well-defined result, just not the one you might have wanted. 

As far as your table is concerned, it doesn't make any sense to me, but I am very confident that there is no square root involved when you try to find upper bounds for floating point errors. 

To analyse floating point errors: Multiplication and division may add the relative errors of their inputs, and then another rounding error is added (unless you can prove the result is exact, for example multiplying by 2.0). Addition and subtraction may add the absolute errors of their inputs, and then another rounding error is added (unless you can prove the result is exact, for example subtracting x-y where x/2 ≤ y ≤ 2x.

For tightest bounds, you analyse the error depending on the input values, and you can also improve the error by arranging operations in the right order.