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.
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.
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.
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
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.
I dont really konw im doing this for the pnits srry
The proportion of the population that holds one opinion compared to those with opposing opinions or no opinion is referred to as the opinion ratio or distribution. It provides insight into how prevalent a particular view is within the population and can help understand the diversity of opinions on a given issue. Analyzing this ratio can be useful for decision-making and forming strategies that consider various viewpoints.
Arithmetic population density does not provide insights into the distribution of the population within a given area. It does not account for variations in population concentration and can mask disparities in population distribution within a region. Additionally, it does not consider factors such as age distribution, cultural diversity, or economic characteristics of the population.
That would depend on the specific problem. The "rule of three" (i.e., solving proportions) can help for many standard problems; i.e., you consider a proportion, where the percentage has a denominator of 100. Here are some examples:1) What's 17% of 2000? The proportion to solve is: 17/100 = x/2000 2) 500 is what percentage of 2000? The proportion to solve is: x/100 = 500/2000 3) 500 is 10% of what number? The proportion to solve is: 500/x = 10/100
Consider a binomial distribution with 10 trials What is the expected value of this distribution if the probability of success on a single trial is 0.5?
Check the item you are making and consider individual body proportions.
One disadvantage of physiological density is that it may not account for variations in population distribution within a country or region. It also does not consider factors such as land use patterns or resource availability, which can impact population carrying capacity. Additionally, it may not provide a complete picture of population pressure on the environment.
Population patterns are of interest to demographers, and to all those who have to consider future implications of population change. The most primary patterns are those of sex (how many women/men their ages, and their distribution throughout NZ. And the same questions as to plumbers, doctors, x-ray technicians, and so on. Do we have enough mining technicians to serve the expected growth of lignite mining in Southland? These are the sort of patterns of availability and distribution that are of importance in planning the future of New Zealand.
Perhaps consider;Distribution; to allocate, to share out, to pass out.
The law of reciprocal proportions doesn't account for all chemical reactions, as it specifically applies to binary compounds with fixed ratios of elements. It also doesn't consider the possibility of isotope variations or complex stoichiometries involving more than two elements. Additionally, the law may not be applicable to reactions involving ions or compounds with variable oxidation states.
not sure if they consider much of anything. They're just bugs.
Is a population. Consider the definition of evolution.Evolution is the change in allele frequency over time in a population of organisms.