Answer:
a) [tex]P(X>106.11)=P(\frac{X-\mu}{\sigma}<\frac{106.11-\mu}{\sigma})=P(Z>\frac{106.11-106}{0.3})=P(z>0.37)[/tex]
And we can find this probability with the complement rule:
[tex]P(z>0.37)=1-P(z<0.37) =1-0.644= 0.356[/tex]
b) [tex] z =\frac{106.11- 106}{\frac{0.3}{\sqrt{44}}}= 2.431[/tex]
And if we use the z score we got:
[tex] P(z>2.431) =1-P(z<2.431) =1-0.992=0.008[/tex]
Step-by-step explanation:
Let X the random variable that represent the lengths of a population, and for this case we know the distribution for X is given by:
[tex]X \sim N(106,0.3)[/tex]
Where [tex]\mu=106[/tex] and [tex]\sigma=0.3[/tex]
Part a
We are interested on this probability
[tex]P(X>106.11)[/tex]
And we can use the z score formula given by:
[tex]z=\frac{x-\mu}{\sigma}[/tex]
And using this formula we got:
[tex]P(X>106.11)=P(\frac{X-\mu}{\sigma}<\frac{106.11-\mu}{\sigma})=P(Z>\frac{106.11-106}{0.3})=P(z>0.37)[/tex]
And we can find this probability with the complement rule:
[tex]P(z>0.37)=1-P(z<0.37) =1-0.644= 0.356[/tex]
Part b
For this case we select a sample of n =44 and the new z score formula is given by:
[tex]z=\frac{x-\mu}{\frac{\sigma}{\sqrt{n}}}[/tex]
And if we find the z score we got:
[tex] z =\frac{106.11- 106}{\frac{0.3}{\sqrt{44}}}= 2.431[/tex]
And if we use the z score we got:
[tex] P(z>2.431) =1-P(z<2.431) =1-0.992=0.008[/tex]