Answer:
95.86%.
Step-by-step explanation:
Problems of normally distributed samples can be solved using the z-score formula.
In a set with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the zscore of a measure X is given by:
[tex]Z = \frac{X - \mu}{\sigma}[/tex]
The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the pvalue, we get the probability that the value of the measure is greater than X.
In this problem, we have that:
[tex]\mu = 0.6, \sigma = \sqrt{\frac{p(1-p)}{n}} = 0.0245[/tex]
[tex]P(0.55 \leq x \leq 0.65[/tex]
This probability is the pvalue of Z when [tex]X = 0.65[/tex] subtracted by the pvalue of Z when [tex]X = 0.55[/tex]. So
X = 0.65
[tex]Z = \frac{X - \mu}{\sigma}[/tex]
[tex]Z = \frac{0.65 - 0.6}{0.0245}[/tex]
[tex]Z = 2.04[/tex]
[tex]Z = 2.04[/tex] has a pvalue of 0.9793
X = 0.55
[tex]Z = \frac{X - \mu}{\sigma}[/tex]
[tex]Z = \frac{0.55 - 0.6}{0.0245}[/tex]
[tex]Z = -2.04[/tex]
[tex]Z = -2.04[/tex] has a pvalue of 0.0207
So this probability is 0.9793 - 0.0207 = 0.9586 = 95.86%.