48/5=9.6 so about 9
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I disagree with this answer. I believe the correct answer is the number of ways of choosing five items from 48, assuming that they are distinct.
This is a precise number 48C4 = 48! / ( 4! ( 48 - 4 )! ) = 194580
Incidentally you can obtain a value for this expression using the Wolfram-Alpha website, given in the link, by entering the search term 48 choose 4.
There are 16,007,560,800 or just over 16 billion samples.
Number of samples = 42C4 = 42*41*40*39/24 = 111930
There are 324,632 possible samples.
You wouldn't want to use the same item twice, so just divide 38/5 = 7+ ... you can get 7 samples of size 5.
There are two equivalent ways of defining a simple random sample from a larger population. One definition is that every member of the population has the same probability of being included in the sample. The second is that, if you generate all possible samples of the given size from the population, then each such sample has the same probability of being selected for use.
There are 16,007,560,800 or just over 16 billion samples.
Number of samples = 42C4 = 42*41*40*39/24 = 111930
There are 324,632 possible samples.
You wouldn't want to use the same item twice, so just divide 38/5 = 7+ ... you can get 7 samples of size 5.
Approximately 1.364*1060
There are two equivalent ways of defining a simple random sample from a larger population. One definition is that every member of the population has the same probability of being included in the sample. The second is that, if you generate all possible samples of the given size from the population, then each such sample has the same probability of being selected for use.
Stratified Random Sampling: obtained by separating the population into mutually exclusive (only belong to one set) sets, or stratas, and then drawing simple random samples (a sample selected in a way that every possible sample with the same number of observation is equally likely to be chosen) from each stratum.
7*6*5/(3*2*1) = 35
Describe how more complex probability sampling techniques could provide samples more representative of a target population than simple random sampling Illustrate your answer with an information technology example.
Sometimes a population consists of a number of subsets (strata) such that members within any particular strata are alike while difference between strata are more than simply random variations. In such a case, the population can be split up into strata. Then a stratified random sample consists of simple random samples, with the same sampling proportion, taken within each stratum.
Simple random sampling.
at a large university a simple random sample of 5 female proffesors is seleted and a simple random sample of 10 male professors is selected. the two samples are combine to give an overall sample of 15 professor. the overall sample is?