Finding a Uniformily Most Powerful Region

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In summary, a Uniformily Most Powerful Region (UMPR) is a subset of the parameter space that maximizes the power of a statistical test. It is determined by considering the distribution of the test statistic under the null and alternative hypotheses, and is defined as the region where the test statistic is most likely to be large when the alternative hypothesis is true. The advantages of using a UMPR include increased power in detecting true effects, especially with small sample sizes or rare events. However, limitations of using a UMPR include the assumption of a fully specified alternative hypothesis and the computational intensity in finding a UMPR for complex statistical models. In practice, a UMPR can be used to determine sample size, compare the power of different
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Homework Statement



Let [tex]X_1,...,X_{25}[/tex] be from a sample of 25 from a normal distribution [tex]N(\theta,100)[/tex]. Find a UMP region with [tex]\alpha = 0.1[/tex] for testing [tex]H_0:\theta=75 \ VS \ H_1:\theta>75[/tex]


Homework Equations





The Attempt at a Solution



So after performing the Likelihood ratio test, I determined the critial region to be [tex](\Sigma X_i => K)[/tex] where K is some constant.

Using the CLT [tex]\frac{\Sigma X_i - n\mu}{\sigma \sqrt{n}}[/tex]

[tex]P(\Sigma X_i => K) =P_{H_0}( \frac{\Sigma X_i - 25(75)}{10\sqrt{75}} => \frac{K - 25(75)}{10\sqrt{75}} )=0.1[/tex]

[tex]\frac{K - 1875}{86.6}=1.28[/tex] solving for K = 1985.848

Have I made a mistake anywhere?
 
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  • #2
Is this the correct UMP region?



Your approach to finding the UMP region for this hypothesis testing problem is correct. You have correctly used the likelihood ratio test and the central limit theorem to determine the critical region. Your calculation for the constant K also appears to be correct. Therefore, your answer for the UMP region is also correct. Good job!
 

FAQ: Finding a Uniformily Most Powerful Region

What is a Uniformily Most Powerful Region (UMPR)?

A UMPR is a subset of the parameter space that maximizes the power of a statistical test. In other words, it is the most sensitive region for detecting a true effect with a given sample size and significance level.

How is a UMPR determined?

A UMPR is determined by considering the distribution of the test statistic under the null and alternative hypotheses. The UMPR is then defined as the region where the test statistic is most likely to be large when the alternative hypothesis is true.

What are the advantages of using a UMPR?

Using a UMPR can increase the power of a statistical test, which means it can better detect true effects. This can be especially useful when working with small sample sizes or when testing for rare events.

Are there any limitations to using a UMPR?

One limitation of using a UMPR is that it assumes the alternative hypothesis is fully specified. If the alternative hypothesis is not fully specified, the power of the test may not be maximized. Additionally, finding a UMPR can be computationally intensive and may not always be feasible for complex statistical models.

How can a UMPR be used in practice?

A UMPR can be used to determine the sample size needed for a study to achieve a desired level of power. It can also be used to compare the power of different statistical tests or to select the most powerful test for a given research question.

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