what is multistage sampling?
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Good question.  You know those techie types.. they will put a fancy name on anything to get us to buy it.
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It sounds like random sampling... why?\nI am not sure what you mean, if it were simply, say, the first 100 names in the Manchester telephone system's number data base, then it would not be random because only a relatively few people whose last name began with an A would be included.\nNow, if all those numbers were mixed up in a hat, and the first 100 drawn were to be used, then the sample is random because anyone in the number data base could theoretically been chosen.\nLet's have a little fun with this. \nIt is impossible to sample, say all 110,000 chocolate bars made by a company in one month. However, quaity control wants to make sure each has a certain number of nuts in them\nSo as the bars are completed at end of the assembly line, once a week, a quality control person randomly grabs 100 bars and counts the nuts in it. Again, any of the 110,000 could have been chosen, but the person merely grabs 100 of them.\nThrough inferential statistics the company can estimate with high accuracy if all the candy bars produced have the right amount of nuts by merely counting nuts in 100 bars. The key: ALL bars had an equal chance of being selected; this is what made the sampling random.
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Create a new database, using the Database Wizard.  There, it will give you different samples.  There is probably several ready-to-run examples there to use...
1128687
You must have a scanner that takes multipule scans(try dell).
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A representative sample should have the same characteristics of the population from which it is drawn.  For example, if you wanted to find out the average height of all Americans, a sample that included mostly basketball players would NOT be representative, nor would a sample that included only women. Instead, a representative sample would include people of various races, both sexes, and all ages--so it would have the same variety of heights that can be found in the whole population.\n\nStatistical theory says that the type of sample most likely to be representative is a random sample, a sample where random numbers or games of chance assure that every member in the population has an equal chance of being selected.  Since this is often impractical to do, other types of "good" (usually representative) samples include systematic samples (where you number off and, for instance, call every 100th name in the phone book) and stratified samples (where you carefully include various sub-groups, like people of all different races in your sample).\n\nAmong the least representative samples are internet polls, where anyone who wants to can answer.  Usually only those with strong opinions on a subject bother responding to such polls.
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www.startsampling.com
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If you are doing a paired-sample t-test, the rule of thumb would be a minimum of 30 participants, assuming the responses are normally distributed on both the pre- and post-tests.\n\nMy understanding is that in computer simulations drawing lots of different samples from a virtual population, n=30 is the smallest sample that has an acceptable probability of approximating a normal distribution.\n\nIf you want to do any regression or factor analyses, you should have a least n=100.
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A convenience sample is a group of people surveyed that are chosen by the surveyer because they were easy to get (convenient), such as the surveyer's family or friends.  They may have a skewed response because they aren't random.  A random sample consists of people chosen without anything in common, such as picking the first name on each page of a phone book.
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You cannot sample a population of 8-10. In order to get results that reflect the target population as precisely as needed, you would have to use the entire population of 8 or 10, but that would be an incredibly small sample and could not be a sample used for a college research paper.\n\nYou will find a research sample size calculator at this site:\nhttp://www.researchinfo.com/docs/calculators/samplesize.cfm\n\ns
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its a group of points taken from a population that covers a random area of the population.. \n\nso a random sample is suposed to be used to model the population without having to measure every single data point\n\n\nbut this is not done in television haha.. they take whatever sample they want, and call it random.. not right Fox news.. not right!
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This problem is solvable.\nProbability of event A is written P(A).\n\nP(A)= Total # of outcomes that correspond to event A (divided by -or- over) Total # of outcomes in sample\n\nAnswer:\nP(A) = 12/20 = 3/5
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you can identify a known region, design a primer in that region, sequence the different sample from that region, then compare the sequence of those regions to look for polymorphs
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How theoretical is it- can you type in a sample problem?
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A simple random sample is a specific kind of random sample.  A random sample uses randomization to pick your sample.  That can be done in a number of ways.  A simple random sample is basically like selecting names from a hat.  Every subject in the population has an equally likely chance of being in the sample, and every possible sample has an equally likely chance of being selected..  Other possible ways to take a random sample are systematic sampling (where you randomly select a starting point, and then pick every nth subject), cluster sampling (where subjects are separated into clusters, then all of the subjects in a random sample of clusters are put in the sample), and stratified sampling (where subjects are put into strata, and then a random sample is taken from each strata).\n\nNon random samples do not use randomization.  Online polls are an example of a nonrandom sample.  The person taking the sample does not take an effort to pick the sample; people choose themselves to be in the sample.\n\nYour example is a simple random sample.  It's just like drawing from a hat.\n\nIn short, simple random samples are random samples, but random samples are not necessarily simple random samples.
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Google for "computer spc abstraction". You can select from several. It depends on which "SPC" you find interesting.
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I think that you would need at least 2 values in each cell so that the within groups SD can be estimated, so if each of the two ways has two factors then you would need a sample of at least 8, so that there is two for each cell.  Could be wrong though.
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Simulation technique is used to generate a no of random no so that these can be used to generate different scenarios and circumstances.\nMonte Carlo is one of the techniques to generate these nos.\nfind more on the following link \nhttp://www.decisioneering.com/monte-carlo-simulation.html