Inferential Statistics Problem

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Homework Help Overview

The discussion revolves around an inferential statistics problem where a party aims to determine the percentage of voters who supported them, with a specified confidence level and margin of error. Participants are exploring the mathematical formulation of the problem, particularly how to derive the initial inequality related to the estimated proportion of voters.

Discussion Character

  • Conceptual clarification, Mathematical reasoning, Assumption checking

Approaches and Questions Raised

  • Participants are discussing the transition from the initial inequality involving the proportion of voters to a rearranged form that incorporates standard deviation. Questions focus on understanding how to derive the first equation from the problem's context and the assumptions involved.

Discussion Status

Some participants have expressed understanding of the steps involved in the mathematical manipulation but seek clarification on the foundational reasoning behind the initial inequality. There is an ongoing exploration of how to approach similar problems in general, with hints of productive dialogue regarding the nature of statistical estimation.

Contextual Notes

Participants note the importance of consistent notation in the problem, specifically the use of either N or n to avoid confusion. There is also mention of the general properties of distributions and estimation procedures relevant to the problem context.

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Homework Statement



Through a sample, a party A wants to determine the percentage p of the voters, who voted for party A. They want to determine p with a 90% certainty to 5%. How many people do they need to survey?
Let X be the amount of party-A-voters from n surveyed people.

I don't need the solution for this, since it is already solved step-by-step in the textbook. I just don't understand the first step and am looking for an explanation.

They say that through the given information you can calculate this:

P(|\frac{X}{N}-p|\leq0.05)\geq0.90

Afterwards, they rearange it to

P(|\frac{X-np}{\sigma}|\leq\frac{0.05n}{\sigma})\geq0.90

and solve for z.

The last part I understand, but I don't understand how they infere from the information to the first statement.

I don't really have much experience in stats, and I have trouble understanding how to get from the information given to the first equation.

Thank you in advance! :)
 
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What happens to the inequality
<br /> \left| \frac{X}{N} - p \, \right| \le 0.05<br />

when you multiply both sides by the (positive, which is why the inequality doesn't change direction) value
<br /> \frac N \sigma<br />
 
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I understand how they get from the first to the second equation. I don't understand how they get the first equation from the information given.
 
Elpinetos said:
I understand how they get from the first to the second equation. I don't understand how they get the first equation from the information given.

The quantity ##X/N## is the measured proportion, while ##p## is the theoretical proportion. You want the difference between these two proportions to be ≤ 0.05 with high probability (that is, with probability at least 0.9). Also: use either the symbol ##N## or the symbol ##n##, but not both in the same problem (as you have done). That can cause confusion and loss of marks, etc.
 
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I see now, thank you.

What I still don't fully understand is how to make the first equation in such examples.
I now understand how they got to it in this particular example, but how do I even approach such a problem? Where do I start? I somehow wouldn't have thought of taking the distance between those two.
Can you help me with that? :)
 
Elpinetos said:
I see now, thank you.

What I still don't fully understand is how to make the first equation in such examples.
I now understand how they got to it in this particular example, but how do I even approach such a problem? Where do I start? I somehow wouldn't have thought of taking the distance between those two.
Can you help me with that? :)
Not sure if this helps, but it can be generalized a little.
There is some property of the distribution, s, that you want to estimate. (In the present case it's p.) You have a procedure for estimating it from sample data, and the procedure produces the estimate ##\hat s##. You want the probability of the estimate being within ε of s to exceed some threshold probability τ. Algebraically, ##P[|\hat s - s| < \epsilon] > \tau##.
In the OP, the assumption is that the estimate for p will be X/N.
 
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Thank you, this helps a lot :)

Any tips on how to best approach such problems? What should I look out for? How do I start best?
 
Elpinetos said:
Thank you, this helps a lot :)

Any tips on how to best approach such problems? What should I look out for? How do I start best?
I can't think of anything I can add to what I wrote before. It's a matter of understanding what the objective is (finding the probability distribution of the error in an estimate for a parameter of another distribution) and expressing that in algebra.
 
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