Examples of using Sample proportion in English and their translations into Thai
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Colloquial
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Ecclesiastic
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Ecclesiastic
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Computer
P2 with our sample proportions.
Our sample proportion is 0.591 times 1 minus 0.591.
So we can actually substitute this with a sample proportion.
Our sample proportion is equal to 0.38.
So let's look at the distribution of sample proportions.
So our sample proportion is 0.568. or 56.8%.
So the mean of the sampling distribution of the sample proportion.
So we could figure out a sample proportion over here for the men.
So you could view this is a sample mean or as a sample proportion.
So our sample proportion is 0.38, so let me write that over here.
If you do the same thing for the women, the sample proportion is going to be 0.591.
So you have your sample proportion, we're surveying a total of 2,000 people.
The sample of P1 minus the sample mean, or the sample proportion, of P2.
We got our sample proportion for the men minus-- let me do this in a neutral color.
We sampled 1,000 men, sampled 1,000 women, and we got a sample proportion for each of them.
The sample proportion for the men minus the sample proportion of the women.
Now, with that out of the way, we want to figure out the probability of getting a sample proportion that has a 0.38.
We got our sample proportion for the men minus our sample proportion for the women being 0.642 minus 0.591, that's 0.051.
And just to visualize it properly, let me draw the sampling distribution for the sample proportions.
You could even view this as the sample proportion of teachers who thought that the computers were a good teaching tool.
And actually so that we can think about a little more intelligent, let's just find out what our sample proportion even is.
So this mean is going to be-- this mean right here-- so the mean of our sample proportions is going to be the same thing as our population mean.
We know that there's a 95% chance that the true difference of the proportions is within 0.043 of the actual difference of our sample proportions that we got.
So just remind ourselves, this sample proportion we got we can view as just a sample from this distribution of all of the possible sample proportions. .
But we're going to think about the sampling distribution for the difference of this sample proportion and this sample proportion. .
So what we have is the square root, and then in parentheses, our sample proportion for the men is 0.642, and then we're going to multiply that times 1 minus 0.642, close parentheses.
So we're going to figure out the probability of actually getting our actual difference between our male sample proportion and our female sample proportion.
Now to figure out the probability of having a sample proportion of 0.38, we just have to figure out how many standard deviations that is away from our mean, or essentially calculate a Z-statistic for our sample, because a Z-statistic or a Z-score is really just how many standard deviations you are away from the mean.
We want to be confident that there's a 95% chance that this thing right here-- remember, when we took the two sample proportions and took their difference, it's like taking a sample from the sampling distribution of the statistic.
So what we can do is, we can figure out that we got when we took the proportion of men and we subtracted from that the proportion of women-- So this is our sample proportion of men who, at least in our poll, said they would vote for the candidate.