Hypothesis Testing: Power Function

In summary: For part b,Set Power(μ=15)=0.99The 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?Yes, but you mean μ>13 as a stirct inequality, right?Put that if you'd rather. Maybe it's better. I'd probably forget even to mention the restriction at all.Alright!
  • #1
kingwinner
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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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  • #2
Replace [tex]\overline{X}[/tex] with [tex]\mu_a+4Z/\sqrt{n}[/tex], rearrange to get [tex]P(Z>\text{whatever})[/tex], then express in terms of [tex]\mu_a[/tex] and [tex]n[/tex] using the normal cdf [tex]\Phi(t)[/tex].
 
  • #3
Billy Bob said:
Replace [tex]\overline{X}[/tex] with [tex]\mu_a+4Z/\sqrt{n}[/tex]

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:
  • #4
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 [tex]\Phi(t)[/tex] where [tex]\Phi(t)=P(Z\le t)=\frac{1}{\sqrt{2\pi}}\int_{-\infty}^t e^{-x^2/2}\,dx[/tex]
 
  • #5
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 [tex]\Phi(t)[/tex] where [tex]\Phi(t)=P(Z\le t)=\frac{1}{\sqrt{2\pi}}\int_{-\infty}^t e^{-x^2/2}\,dx[/tex]

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!
 
  • #6
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.
 
  • #7
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...
 
  • #8
Just replace your μ_a in your power function with simply μ, write [tex]\mu\ge13[/tex] as the restriction and you have the answer.
 
  • #9
Billy Bob said:
Just replace your μ_a in your power function with simply μ, write [tex]\mu\ge13[/tex] 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!
 

1. What is a power function in hypothesis testing?

A power function in hypothesis testing is a mathematical function that calculates the probability of rejecting the null hypothesis when it is actually false. It is used to determine the sensitivity of a statistical test and its ability to detect a true effect or relationship.

2. How is power related to type II error?

Power and type II error are inversely related. As power increases, the probability of making a type II error decreases. This means that a test with high power is more likely to correctly reject the null hypothesis when it is actually false.

3. What factors affect the power of a statistical test?

The power of a statistical test is affected by several factors, including the sample size, effect size, alpha level, and variability in the data. A larger sample size, larger effect size, lower alpha level, and lower variability all increase the power of a test.

4. How is the power function graphically represented?

The power function is typically graphically represented as a curve, with the x-axis representing the different levels of the independent variable and the y-axis representing the probability of rejecting the null hypothesis. The shape of the curve can vary depending on the specific statistical test being used.

5. Why is it important to consider power in hypothesis testing?

Considering power in hypothesis testing is important because it allows researchers to determine the likelihood of detecting a true effect or relationship. A test with low power may fail to detect a true effect, leading to a type II error, while a test with high power is more likely to detect a true effect. Therefore, understanding power can help researchers make more accurate conclusions about their data.

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