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18 Cards in this Set
- Front
- Back
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what is a population? |
• the entirety : the entire group of individuals about which wewant information population is entirelty |
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what is a sample? |
the part of the population that we actuallyexamine in order to get informationis what we have |
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what is a parameter |
is a number that describes a population. Aparameter has a fixed value, which we do not usually know. something in entirety, so the estimate of the population mean is a parameter and population SD |
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what is a statistic |
a number that describes a sample- mean and a standard deviation |
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what is statistical inference |
is inferring conclusions about a population fromsample data. We often use sample statistics to estimate parameters |
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what do you need for a sampling distribution: |
A sample statistic will have adifferent value from every sample. need to think about sample statistic distribution by repeating the sampling many time to get a sampling distribution of the statistical value. depends how many subsamples are obtained The sampling distribution depends onhow samples were obtained. |
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why would you perform a simple random samples? |
avoid biased which will lead to bias conclusions |
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what is a simple random sample? |
•of size n is a set of nindividuals drawn in such a way that every possible set of n individualshas an equal chance of being chosen. |
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how do you perform random sampling? |
• Constructing an SRS (simple random sample) : we could numberevery individual in the population and draw n numbers out of a hat. using a random number generator |
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what is the problem with available data? |
all data may suffer from all kinds of bias- specific place, ones like. |
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what is available data? |
data is collected from different people for different places |
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what happens when you take sub samples? |
with enough subsamples, more likely to get a normal distribution of subsample means and smaller SD |
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how could you make a small population/ size a normal distribution? |
by taking enough samples |
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what are the general properties of the sample distributions (mean sample) ? |
The mean is equal to the populationmean• The standard deviation is equal tothe population standard deviation divided by the square root of the sample size The standard deviation of the samplemean is sometimes called the standard error The distribution is approximatelynormal if the sample size is large enough, for almost any shape ofpopulation distribution, the sample size would have to be at least 30 (n=30 and possibly more) |
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what is the central limit theorem |
If we draw an SRS of size nfrom any population with mean μ and standarddeviation σ, then if n is large enough it will be normal the sampling distribution of thesample mean is approximately normal with mean μ and standard deviation σ/√n. |
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what does the central limit theorem tell us? |
•We often denote the sample mean by x^- (sample mean The CentralLimit Theorem tells us how good an estimate the sample mean will be of thepopulation mean: it is unbiased, and the larger the sample, the lessvariable it will be sample mean can be used as an estimate for a population |
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what is the central limit theorem ? |
thearithmetic mean of a sufficiently large number of iterations of independentrandom variables, each with a well-defined expected value and finite variance,will be approximately normally distributed, regardless of the underlyingdistribution. |
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how would you find the sampling distribution of the sample mean using the central limit theorem? |
divide the mean by the square root of the standard deviation |
