Creating a Confidence Interval for θ

In summary, if we know that \sqrt{n}\left(\widehat{\theta} - \theta\right) \sim \mathcal{N}\left(0, \frac{1}{F(\theta)}\right)and for a given value of \alpha, we can find u_{1-\frac{\alpha}{2}} from the standard normal distribution, then we can use the inequality -u < \sqrt{n F(\theta)} \left(\hat \theta - \theta\right) < uto construct a confidence interval for \theta with a margin of error of u_{1-\frac{\alpha}{2}}\frac{1}{\sqrt{nF\left(\widehat{\theta}\
  • #1
twoflower
368
0
Let's say we know this:

[tex]
\sqrt{n}\left(\widehat{\theta} - \theta\right) \sim \mathcal{N}\left(0, \frac{1}{F(\theta)}\right)
[/tex]

How do we get from this information to this expression of confidence interval for [itex]\theta[/itex]?

[tex]
\left( \widehat{\theta} \pm u_{1-\frac{\alpha}{2}}\frac{1}{\sqrt{nF\left(\widehat{\theta}\right)}}\right)
[/tex]

Where [itex]u_{1-\frac{\alpha}{2}}[/itex] is appropriate quantil of standard normal distribution.

Thank you.
 
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  • #2
If [tex] a [/tex] is the value from [tex] Z [/tex] (standard normal) with area [tex] {\alpha}/2[/tex] to its right, you know the value of

[tex]
\Pr\left(-u < \sqrt{n F(\theta)} \left(\hat \theta - \theta\right) < u)
[/tex]

because of your stated approximate normality result. That means the event

[tex]
-u < \sqrt{n F(\theta)} \left(\hat \theta - \theta\right) < u
[/tex]

has a known probability of occurring. What can you do with this inequality? (Try some work and include it with your next question if you are unsure.)
 

1. What is a confidence interval?

A confidence interval is a range of values that is likely to include the true value of a parameter, such as the population mean or proportion. It is calculated from a sample and is used to estimate the true value of the population parameter with a certain level of confidence.

2. How is a confidence interval calculated?

To calculate a confidence interval for θ, you need to know the sample mean, standard deviation, and sample size. The formula for a confidence interval depends on the distribution of the data and the desired level of confidence. For example, if the data is normally distributed and the desired level of confidence is 95%, the confidence interval can be calculated as sample mean ± 1.96 * (standard deviation / √n).

3. What does the confidence level represent?

The confidence level represents the probability that the true population parameter falls within the calculated confidence interval. For example, a confidence level of 95% means that if we were to take multiple samples and calculate a confidence interval for each sample, 95% of those intervals would contain the true population parameter.

4. How does sample size affect the confidence interval?

Generally, a larger sample size will result in a narrower confidence interval. This is because a larger sample size provides more information and reduces the variability of the estimate. However, the exact effect of sample size on the confidence interval depends on the distribution of the data and the desired level of confidence.

5. Can a confidence interval be used to make predictions about individual data points?

No, a confidence interval is used to make inferences about the population parameter, not individual data points. It is important to remember that the confidence interval only provides a range of values that is likely to include the true population parameter, and it does not guarantee that a specific data point will fall within that range.

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