The confidence interval will be Pi+-z*sp
z5%= 1.6449
Pi = x/n
Sp = Sqrt(Pi(1-Pi)/n)
Pi ~= 0.5694
Sp = Sqrt(.5694*0.4306/144) ~= 0.0413
Pi - 0.0679 < p < Pi + 0.0679
0.5016 < p < 0.6373
You can do this on your TI-83/84 with 1-PropZInt (Stat->Tests->A)
A confidence interval of x% is an interval such that there is an x% probability that the true population mean lies within the interval.
Confidence intervals represent an interval that is likely, at some confidence level, to contain the true population parameter of interest. Confidence interval is always qualified by a particular confidence level, expressed as a percentage. The end points of the confidence interval can also be referred to as confidence limits.
, the desired probabilistic level at which the obtained interval will contain the population parameter.
No, the confidence interval (CI) doesn't always contain the true population parameter. A 95% CI means that there is a 95% probability that the population parameter falls within the specified CI.
The confidence interval becomes wider.
There is a 95% probability that the true population proportion lies within the confidence interval.
What percentage of times will the mean (population proportion) not be found within the confidence interval?
You probably mean the confidence interval. When you construct a confidence interval it has a percentage coverage that is based on assumptions about the population distribution. If the population distribution is skewed there is reason to believe that (a) the statistics upon which the interval are based (namely the mean and standard deviation) might well be biased, and (b) the confidence interval will not accurately cover the population value as accurately or symmetrically as expected.
A confidence interval of x% is an interval such that there is an x% probability that the true population mean lies within the interval.
Wisdom
Estimated p = 75 / 250 = 0.3 Variance of proportion = p*(1-p)/n = 0.3(0.7)/250 =0.00084 S.D. of p is sqrt[0.00084] = 0.029 Confidence interval: phat-zval*sd = 0.3 - (1.96)(0.028983) phat-zval*sd = 0.3 + (1.96)(0.028983) Confidence interval is ( 0.2432 , 0.3568 )
Confidence intervals may be calculated for any statistics, but the most common statistics for which CI's are computed are mean, proportion and standard deviation. I have include a link, which contains a worked out example for the confidence interval of a mean.
The parameters of the underlying distribution, plus the standard error of observation.
Confidence intervals represent an interval that is likely, at some confidence level, to contain the true population parameter of interest. Confidence interval is always qualified by a particular confidence level, expressed as a percentage. The end points of the confidence interval can also be referred to as confidence limits.
THe answer will depend on whether the confidence interval is central or one-sided. If central, then -1.28 < z < 1.28 -1.28 < (m - 18)/6 < 1.28 -7.68 < m - 18 < 7.68 10.3 < m < 25.7
confidence interval estimate
No. For instance, when you calculate a 95% confidence interval for a parameter this should be taken to mean that, if you were to repeat the entire procedure of sampling from the population and calculating the confidence interval many times then the collection of confidence intervals would include the given parameter 95% of the time. And sometimes the confidence intervals would not include the given parameter.