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
and standard deviation
, the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean
and standard deviation
.
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
and standard deviation ![s = \sqrt{\frac{p(1-p)}{n}}](https://tex.z-dn.net/?f=s%20%3D%20%5Csqrt%7B%5Cfrac%7Bp%281-p%29%7D%7Bn%7D%7D)
In a randomly selected sample of 100 students at a University, 81 of them had access to a computer at home.
This means that ![n = 100, p = \frac{81}{100} = 0.81](https://tex.z-dn.net/?f=n%20%3D%20100%2C%20p%20%3D%20%5Cfrac%7B81%7D%7B100%7D%20%3D%200.81)
Give the value of the standard error for the point estimate.
This is s. So
![s = \sqrt{\frac{0.81*0.19}{100}} = 0.0392](https://tex.z-dn.net/?f=s%20%3D%20%5Csqrt%7B%5Cfrac%7B0.81%2A0.19%7D%7B100%7D%7D%20%3D%200.0392)
The value of the standard error for the point estimate is of 0.0392.