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rannoudanames
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You managed to find the first part, that is the unbiased estimator for $\theta$, by noting:

$$E[X] = \theta + \frac{1}{2} \implies \hat{\theta} = \bar{X} - \frac{1}{2}$$

Now you want a confidence interval at the $\beta$ level.

This means that you want $$P_{\bar{X}}[a\leq\bar{X}-\frac{1}{2} \leq b] = 1-\beta$$

So what you really need is to find the distribution of $\bar{X}$.

Now you can find that $$t = \frac{\bar{X} - \mu}{(\frac{s}{\sqrt{n}})}$$

Where $t$ follows a $t-distribution$ with $n-1$ degrees of freedom. Where $s$ is your sample standard deviation (coming from your second moment).

Generally when you find a confidence interval you want it to be of minimum measure/cardinality/length, however in this case the distribution is symmetric, so you don't need to struggle to find the range as it is symmetric around your estimator.

I encourage you to find out why the above t distribution is true!

Hope this helps.

_EDIT

As noted by jbowman in the comments, we already know the standard deviation, in which case we can use :

$$ z = \frac{\bar{X} - \mu}{\frac{\sigma}{\sqrt{n}}}$$

Where $\sigma$ is the "known" standard deviation and $z$ follows a standard normal distribution

You managed to find the first part, that is the unbiased estimator for $\theta$, by noting:

$$E[X] = \theta + \frac{1}{2} \implies \hat{\theta} = \bar{X} - \frac{1}{2}$$

Now you want a confidence interval at the $\beta$ level.

This means that you want $$P_{\bar{X}}[a\leq\bar{X}-\frac{1}{2} \leq b] = 1-\beta$$

So what you really need is to find the distribution of $\bar{X}$.

Now you can find that $$t = \frac{\bar{X} - \mu}{(\frac{s}{\sqrt{n}})}$$

Where $t$ follows a $t-distribution$ with $n-1$ degrees of freedom. Where $s$ is your sample standard deviation (coming from your second moment).

Generally when you find a confidence interval you want it to be of minimum measure/cardinality/length, however in this case the distribution is symmetric, so you don't need to struggle to find the range as it is symmetric around your estimator.

I encourage you to find out why the above t distribution is true!

Hope this helps.

You managed to find the first part, that is the unbiased estimator for $\theta$, by noting:

$$E[X] = \theta + \frac{1}{2} \implies \hat{\theta} = \bar{X} - \frac{1}{2}$$

Now you want a confidence interval at the $\beta$ level.

This means that you want $$P_{\bar{X}}[a\leq\bar{X}-\frac{1}{2} \leq b] = 1-\beta$$

So what you really need is to find the distribution of $\bar{X}$.

Now you can find that $$t = \frac{\bar{X} - \mu}{(\frac{s}{\sqrt{n}})}$$

Where $t$ follows a $t-distribution$ with $n-1$ degrees of freedom. Where $s$ is your sample standard deviation (coming from your second moment).

Generally when you find a confidence interval you want it to be of minimum measure/cardinality/length, however in this case the distribution is symmetric, so you don't need to struggle to find the range as it is symmetric around your estimator.

I encourage you to find out why the above t distribution is true!

Hope this helps.

_EDIT

As noted by jbowman in the comments, we already know the standard deviation, in which case we can use :

$$ z = \frac{\bar{X} - \mu}{\frac{\sigma}{\sqrt{n}}}$$

Where $\sigma$ is the "known" standard deviation and $z$ follows a standard normal distribution

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rannoudanames
  • 770
  • 1
  • 6
  • 24

You managed to find the first part, that is the unbiased estimator for $\theta$, by noting:

$$E[X] = \theta + \frac{1}{2} \implies \hat{\theta} = \bar{X} - \frac{1}{2}$$

Now you want a confidence interval at the $\beta$ level.

This means that you want $$P_{\bar{X}}[a\leq\bar{X}-\frac{1}{2} \leq b] = 1-\beta$$

So what you really need is to find the distribution of $\bar{X}$.

Now you can find that $$t = \frac{\bar{X} - \mu}{(\frac{s}{\sqrt{n}})}$$

Where $t$ follows a $t-distribution$ with $n-1$ degrees of freedom. Where $s$ is your sample standard deviation (coming from your second moment).

Generally when you find a confidence interval you want it to be of minimum measure/cardinality/length, however in this case the distribution is symmetric, so you don't need to struggle to find the range as it is symmetric around your estimator.

I encourage you to find out why the above t distribution is true!

Hope this helps.