Sagot :
Answer:
The value of the standard error for the point estimate is of 0.0392.
Step-by-step explanation:
Central Limit Theorem
The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean [tex]\mu[/tex] and standard deviation [tex]\sigma[/tex], the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean [tex]\mu[/tex] and standard deviation [tex]s = \frac{\sigma}{\sqrt{n}}[/tex].
For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.
For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean [tex]\mu = p[/tex] and standard deviation [tex]s = \sqrt{\frac{p(1-p)}{n}}[/tex]
In a randomly selected sample of 100 students at a University, 81 of them had access to a computer at home.
This means that [tex]n = 100, p = \frac{81}{100} = 0.81[/tex]
Give the value of the standard error for the point estimate.
This is s. So
[tex]s = \sqrt{\frac{0.81*0.19}{100}} = 0.0392[/tex]
The value of the standard error for the point estimate is of 0.0392.