standard error for proportion is calculated as:
SE = sqrt [(p)(1-p) / n ]
so let us say that "p" is going to represent the decimal proportion of respondents who said YES.... so...
p = 20/25 = 4/5 = 0.8
And... we then are going to say that the complement of "p" which is "1-p" is going to represent the decimal proportion of respondents who said NO ... so... 1-p = 1 - 0.8 = 0.2
Lastly, the "n" in the formula for standard error is equal to 25 because "n" represents the sample size....
So now all you have to do is plug the values you found for "p" and for "1-p"... (remember "p = 0.8" and "1-p = 0.2")... and "n=25"....
Standard Error (SE) = sqrt [(p)(1-p) / n ]
............................ = sqrt [(0.8)(1-0.8) / 25 ]
............................ = sqrt [(0.8)(0.2) / 25 ]
............................ = sqrt [0.16 / 25]
............................ = sqrt (0.0064)
............................ = +/- 0.08
The mean of a proportion, p, is X/n; where X is the number of instances & n is the sample size; and its standard deviation is sqrt[p(1-p)]
It is 6.1, approx.
it is the test one tail
A small sample size and a large sample variance.
3 percent
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The proportion is approx 95%.
The mean of a proportion, p, is X/n; where X is the number of instances & n is the sample size; and its standard deviation is sqrt[p(1-p)]
0.0016
It is 6.1, approx.
it is the test one tail
A half.
A small sample size and a large sample variance.
3 percent
Yes
I dont really konw im doing this for the pnits srry
The sampling proportion may be used to scale up the results from a sample to that of the population. It is also used for designing stratified sampling.