Statistics - Joint PMF / Hypothesis Testing

In summary, the conversation discusses sampling a population with replacement and surveying the gender and quality of life. The null hypothesis states that the population proportions for each category are equal, while the alternative hypothesis states that they are not equal. The joint probability mass function and sets w_0 and w_A are discussed for this scenario. The second question asks for a vector that satisfies the null hypothesis and a hyperplane that does not contain the vector.
  • #1
brojesus111
39
0

Homework Statement



We sample a population 50 times with replacement, with all individual sampled equally likely. We survey the gender and quality of life. The counts are: Male: 13 high quality, 11 low | female: 18 high, 8 low.

Let P_M, P_F, P_H, P_L to denote these population proportions for each of the categories and let population proportions for the combinations be denoted by P_mh, P_ml, P_fh, P_fl. Paramaters are denoted by θ=(P_mh, P_ml, P_fh, P_fl).

Null hypothesis: P_mh = P_m * P_h, P_ml = P_m * P_l... same for females
Alternative hypothesis: P_mh =/= P_m * P_h, P_ml =/= P_m * P_l... same for females1. Data counts are denoted by N_mh, N_ml, N_fh, N_fl. What is the joint probability mass function for these counts?
2. Write down the sets w_0 and w_A so that null hypothesis is θ∈w_0 and alternative hypothesis is θ∈w_A.

Homework Equations


The Attempt at a Solution



1. So I know understand the problem wants us to find P[(N_mh, N_ml, N_fh, N_fl)=(n_mh, n_ml, n_fh, n_fl)]. But I'm a bit confused on the notation and how to answer this problem.

2. Once again I'm not sure I understand exactly how to approach this problem.

Any help is greatly appreciated.
 
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  • #2
The population falls into 4 groups, g1 to g4, say. According to the null hyp they have frequencies P_m * P_h, P_m * P_l etc. Call these p1 to p4. Suppose you take N samples from the population, and you get, in order, 2 1 2 4 4 3 2 1 4 ..., getting, in all, ni from group gi. What would the probability of that specific sequence be? How many sequences give the same total for each group?
 
  • #3
haruspex said:
The population falls into 4 groups, g1 to g4, say. According to the null hyp they have frequencies P_m * P_h, P_m * P_l etc. Call these p1 to p4. Suppose you take N samples from the population, and you get, in order, 2 1 2 4 4 3 2 1 4 ..., getting, in all, ni from group gi. What would the probability of that specific sequence be? How many sequences give the same total for each group?

So it's a multinomial distribution.

Any hints or tips on the second question?
 
  • #4
I think the key point about the second question is that θ is a vector of four unknowns, but:
- they must add up to 1
- the null hypothesis does not prescribe them all, only a relationship between them.
So I think it's looking for a vector, involving some free parameter, which generically describes elements of w_0.
 
  • #5
haruspex said:
I think the key point about the second question is that θ is a vector of four unknowns, but:
- they must add up to 1
- the null hypothesis does not prescribe them all, only a relationship between them.
So I think it's looking for a vector, involving some free parameter, which generically describes elements of w_0.

So I think w_0 is just a point and w_A is a hyperplane in 4 dimensions that is missing the point that is in w_0. My problem is how would I write that in the context of this problem?
 
Last edited:
  • #6
brojesus111 said:
So I think w_0 is just a point
No, that's exactly what I'm saying it isn't. The null hypothesis makes no claim about the gender ratio in the population. Therefore there is a continuum of values of the 4 vector which satisfy it. The answer will be something like {(f1(t), f2(t), f3(t), f4(t))} where fi(t) are functions (you need to determine) of some unknown parameter t. Or maybe there are two free parameters.
w_A will be everywhere in the 4-space except for that continuum.
 

Related to Statistics - Joint PMF / Hypothesis Testing

What is a joint PMF?

A joint PMF (Probability Mass Function) is a statistical tool used to describe the probability distribution of two or more random variables. It is a function that assigns probabilities to different combinations of values for the variables.

How is a joint PMF different from a marginal PMF?

A marginal PMF only looks at the probabilities for one variable at a time, while a joint PMF takes into account the probabilities for all variables simultaneously. In other words, a marginal PMF looks at the total probability of an event, while a joint PMF looks at the probability of each possible combination of events.

What is hypothesis testing?

Hypothesis testing is a statistical method used to determine whether a certain hypothesis or claim about a population is supported by the data. It involves comparing the observed data to what would be expected if the null hypothesis (the default assumption) were true. If the observed data is significantly different from what would be expected, the null hypothesis is rejected in favor of the alternative hypothesis.

What is a p-value and how is it used in hypothesis testing?

A p-value is a measure of the strength of evidence against the null hypothesis. It represents the probability of obtaining results at least as extreme as the observed data, assuming the null hypothesis is true. In hypothesis testing, a p-value is compared to a pre-determined significance level (usually 0.05). If the p-value is less than the significance level, the null hypothesis is rejected.

What is the difference between a one-tailed and two-tailed hypothesis test?

A one-tailed hypothesis test is used when the alternative hypothesis only predicts a change in one direction, while a two-tailed hypothesis test is used when the alternative hypothesis predicts a change in either direction. The choice between a one-tailed or two-tailed test depends on the specific research question and the predictions being made.

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