Hypothesis Testing: Power Function

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Homework Help Overview

The discussion revolves around hypothesis testing, specifically focusing on the power function in statistics. Participants are examining how to express the power in terms of the alternative hypothesis parameter μ_a and the sample size n, given a test statistic involving the sample mean X bar.

Discussion Character

  • Exploratory, Conceptual clarification, Mathematical reasoning

Approaches and Questions Raised

  • Participants discuss the formulation of the power function and the implications of substituting X bar with μ_a + 4Z/sqrt(n). Questions arise regarding the meaning of Z and the reasoning behind certain substitutions. There is also exploration of how to express probabilities using the normal cumulative distribution function.

Discussion Status

The discussion is active, with participants providing guidance on expressing the power function correctly. There is acknowledgment of the need to clarify the relationship between μ and μ_a, and some participants suggest how to handle the notation and restrictions in the context of the problem.

Contextual Notes

Participants note that the problem specifies μ and n, but discussions clarify that μ should be interpreted as μ_a, with the condition μ_a > 13 being significant in the context of the hypothesis test.

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Homework Statement


http://www.geocities.com/asdfasdf23135/stat14.JPG

Homework Equations


Statistics: power function

The Attempt at a Solution


a) Test statistic: Z = (X bar - 13) / (4/sqrt n)
Let μ = μ_a > 13 be a particular value in H_a
Power(μ_a)
= 1- beta(μ_a)
= P(reject Ho | H_a is true)
= P(reject Ho | μ = μ_a)
= P(Z>1.645 | μ = μ_a)
= P(X bar > 13 + [(1.645 x 4)/(sqrt n)] | μ = μ_a)
I also know that X bar ~ N(μ, 16/n)
I am stuck here...How can I proceed from here and express the power in terms of μ and n?

Thank you very much!


[note: also discussing in other forum]
 
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Replace \overline{X} with \mu_a+4Z/\sqrt{n}, rearrange to get P(Z>\text{whatever}), then express in terms of \mu_a and n using the normal cdf \Phi(t).
 
Billy Bob said:
Replace \overline{X} with \mu_a+4Z/\sqrt{n}

Um...why can we replace X bar by mu_a + 4Z/sqrt n ? What is Z here? I don't understand this step...can you please explain a little bit more?

X bar has the distribution N(μ, 16/n). Here, we know the distribution of X bar, so I believe that we can express P(X bar > 13 + [(1.645 x 4)/(sqrt n)] when μ = μ_a) as an integral of the normal density and replace μ by μ_a, but how can I integrate it and get a compact answer?

Thank you very much!
 
Last edited:
Z is N(0,1), a standard normal r.v.

You can't integrate and get a compact answer. The way to get a compact answer is to express your answer using the cumulative density function \Phi(t) where \Phi(t)=P(Z\le t)=\frac{1}{\sqrt{2\pi}}\int_{-\infty}^t e^{-x^2/2}\,dx
 
Billy Bob said:
Z is N(0,1), a standard normal r.v.

You can't integrate and get a compact answer. The way to get a compact answer is to express your answer using the cumulative density function \Phi(t) where \Phi(t)=P(Z\le t)=\frac{1}{\sqrt{2\pi}}\int_{-\infty}^t e^{-x^2/2}\,dx

OK!

But in the end, I get a power function in terms of μ_a and n, not μ and n. What should I do?

Thanks!
 
But in the end, I get a power function in terms of μ_a and n, not μ and n. What should I do?

μ_a is right. When the question says μ and n, it really means μ_a and n, because μ_0=13.

Good job.
 
Billy Bob said:
μ_a is right. When the question says μ and n, it really means μ_a and n, because μ_0=13.

Good job.

So the power function is in terms of μ_a, and μ_a > 13 should be a particular value in H_a, right?
The question says μ and n, which to me looks like μ can be anything...
 
Just replace your μ_a in your power function with simply μ, write \mu\ge13 as the restriction and you have the answer.
 
Billy Bob said:
Just replace your μ_a in your power function with simply μ, write \mu\ge13 as the restriction and you have the answer.
Yes, but you mean μ>13 as a stirct inequality, right?
 
  • #10
Put that if you'd rather. Maybe it's better. I'd probably forget even to mention the restriction at all.
 
  • #11
Alright!

For part b,
Set Power(μ=15)=0.99
The left side should now be a function of n only, and using the standard normal table, we should be able to solve for n, right?
 
  • #12
Right!
 
  • #13
Thanks a lot!
 

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