Step 1

Introduction:

The normal probability is a type of continuous probability distribution that can take random values. The normal distribution is determined by the two parameters - the population mean \((\mu)\) and population variance \(\displaystyle{\left(\sigma^{{{2}}}\right)}\). It is symmetric with respect to its mean.

Given information:

\(\displaystyle{X}\sim{N}{\left({6},{4}\right)}\)

Therefore,

\(\displaystyle\mu={6}\)

\(\displaystyle\sigma^{{{2}}}={4}\)

Step 2

\(\displaystyle{P}{\left({X}{<}{3}\right)}\) is computed as follows:

\(\displaystyle{P}{\left({X}{<}{3}\right)}={P}{\left({\frac{{{X}-\mu}}{{\sqrt{{\sigma^{{{2}}}}}}}}{<}{\frac{{{3}-\mu}}{{\sqrt{{\sigma^{{{2}}}}}}}}\right)}\)

\(\displaystyle={P}{\left({Z}{<}{\frac{{{3}-{6}}}{{\sqrt{{{4}}}}}}\right)}\)

\(\displaystyle={P}{\left({Z}{<}-{1.5}\right)}\)

\(\displaystyle={1}-{P}{\left({Z}{<}{1.5}\right)}\)

\(\displaystyle={1}-{0.93319}={0.06681}\)

Therefore,

\(\displaystyle{P}{\left({X}{<}{3}\right)}={0.0668}\)