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Consider a random sample of size 45 from a population with proportion 0.30 find the standard error of the distribution of sample proportions?
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Why can not 120 percent be the probabilty of some event?
COnsider some event A and the number of outcomes that are favourable to A. Then the probability of A is the number of such outcomes as a proportion of all possible outcomes (related to the trial or experiment). Defined as a proportion in this way, it can never be greater than 1. Converted to a percentage, that means it can never be greater than 100 percent.
How do you determine the sample size to estimate the proportion?
There is no simple answer. There are two main factors need to be taken into account. Consider the simple case of a dichotomous or binary variable.One consideration is the consequences of getting the proportion wrong. If you are estimating the proportion of males (and females) going to a cinema so as to design the correct number of toilets, a 5% risk of getting it wrong may be acceptable. You may have some disgruntled customers and, in any case, it may be possible to rebuild and re-designate some toilets. If, instead, you are estimating the proportion of people who have a serious adverse reaction to some medication, a 5% error rate is catastrophic! Not just for the patient but for the pharmaceutical company as well.Such risk assessment will determine the confidence level that you require from the estimate. Suppose now that for the study under consideration, a 5% risk of getting it wrong is acceptable. That is, you want to be 95% confident that the true (but unknown proportion) is within 1.96 standard errors of your estimate.If the true proportion is around 50%, then a sample size of just under 100 will suffice. However, if you are trying to estimate the proportion of a rare characteristic - whose true incidence in the population is 0.5% - then for the same degree of confidence in the estimate you will need a sample of over 19,000.
How Many 2 meter Tall People Are In The World?
To determine the number of 2-meter tall people in the world, we would need to consider the average height distribution of the global population. The probability of an individual being exactly 2 meters tall would be quite low, as this height is significantly above the global average. However, with the current global population estimated at around 7.9 billion, we could estimate the number of individuals around 2 meters tall to be a very small fraction of that total.
When testing for differences between two means the sample population are?
You are testing the difference between two means of independent sample and the population variance are not known. from those population you take two samples of two different size n1and n2. what degrees of freedom is appropriate to consider in this case
What does unimodal mean?
Please consider the probability density function graphs for the beta distribution, given in the link. For alpha=beta=2, the density is unimodal, which is to say, it has a single maximum. In contrast, for alpha=beta=0.5, the density is bimodal; it has two maxima.
