What is the formula for determining the length of a confidence interval?

In summary, the research institute is looking to establish a confidence interval for the quota of working people in a city, using the estimated quota $\hat{p}_n$ based on a sample size of $n>30$. The length of the confidence interval can be determined using the formula $L = 2 \cdot \frac{\sigma \cdot z}{\sqrt{n}}$, where $\sigma$ is the estimated standard deviation of the proportion and $z$ depends on the confidence level. If $1-\alpha=0.95$, the maximum length of the confidence interval is $L_{\text{max}}=0.03$, which can be achieved by setting $n\geq \frac{153664\hat{
  • #1
mathmari
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Hey! :eek:

A research institute wants to establish a confidence interval for the quota of working people in a city. Let $\hat{p}_n $ be the estimated quota, based on a sample of size $n$. It is assumed that $n> 30$.
How can one determine the length of the confidence interval?

Generally this is equal to $L = 2 \cdot \frac{\sigma \cdot z}{\sqrt{n}}$, right?

Do you have to use the $\hat{p}_n$ in the formula in this case? But how?

(Wondering)
 
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  • #2
Hey mathmari! (Wave)

The distribution of a proportion is a special case.
It has a z-distribution with an estimated standard deviation $\sigma_{\hat{p}_n} = \sqrt{\hat{p}_n(1-\hat{p}_n)}$.
And we should have $n \hat{p}_n > 10$ and $n (1 − \hat{p}_n) > 10$ to ensure it is sufficiently reliable.
 
  • #3
I like Serena said:
The distribution of a proportion is a special case.
It has a z-distribution with an estimated standard deviation $\sigma_{\hat{p}_n} = \sqrt{\hat{p}_n(1-\hat{p}_n)}$.
And we should have $n \hat{p}_n > 10$ and $n (1 − \hat{p}_n) > 10$ to ensure it is sufficiently reliable.

Ah ok!

So, the length is equal to $$L = 2 \cdot \frac{\sigma_{\hat{p}_n} \cdot z}{\sqrt{n}}= 2 \cdot \frac{ \sqrt{\hat{p}_n(1-\hat{p}_n)} \cdot z}{\sqrt{n}}$$ where $z$ depends on the confidence level $1-\alpha$, right? (Wondering)
Suppose that $1-\alpha=0.95$. I want to determine $n$ so that the length of confidence interval is not bigger that $L_{\text{max}}=0.03$.

We have that $z =1.96$.

Then $$L_{\text{max}}=0.03 \Rightarrow 2 \cdot \frac{ \sqrt{\hat{p}_n(1-\hat{p}_n)} \cdot 1.96}{\sqrt{n}}\leq 0.03\Rightarrow \frac{ \sqrt{\hat{p}_n(1-\hat{p}_n)} }{\sqrt{n}}\leq \frac{3}{392} \Rightarrow \frac{ \hat{p}_n(1-\hat{p}_n) }{n}\leq \frac{9}{153664}\Rightarrow n\geq \frac{153664\hat{p}_n(1-\hat{p}_n)}{9}$$ right? We cannot continue from here, can we? (Wondering)
 
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  • #4
Indeed. (Nod)
Btw, do we have $L_{\text{max}}=0.04$ or $L_{\text{max}}=0.03$?
 
  • #5
I like Serena said:
Indeed. (Nod)
Btw, do we have $L_{\text{max}}=0.04$ or $L_{\text{max}}=0.03$?

Oh, it is $L_{\text{max}}=0.03$. (Tmi)
 

What is the length of a confidence interval?

The length of a confidence interval is a statistical measure that represents the range of values within which the true population parameter is likely to fall. It is calculated by subtracting the lower limit from the upper limit of the interval.

How is the length of a confidence interval determined?

The length of a confidence interval is determined by several factors, including the level of confidence (typically 95% or 99%), the sample size, and the variability of the data. A larger sample size and lower variability will result in a shorter confidence interval.

What does the length of a confidence interval indicate?

The length of a confidence interval indicates the precision of the estimate of the population parameter. A shorter interval indicates a more precise estimate, while a longer interval represents a less precise estimate.

What impact does the sample size have on the length of a confidence interval?

The sample size has a direct impact on the length of a confidence interval. As the sample size increases, the length of the interval decreases, indicating a more precise estimate of the population parameter.

Can the length of a confidence interval be changed?

Yes, the length of a confidence interval can be changed by adjusting the level of confidence or increasing the sample size. However, it is important to note that these changes may also affect the accuracy of the interval. A larger sample size may result in a more precise estimate, but it also requires more resources and time to collect and analyze the data.

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