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- Thread starter iPhysicz
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statdad

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Chebyshev's Theorem, on the other hand, IS a theorem - there is a proof of the result: the only requirement is that the distribution have a finite variance. Chebyshev's theorem holds for any distribution, symmetric or skewed. It's most important use (IMO) is not in data description but in more theoretical settings.

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I just can't grasp how to figure out proportions of measurements below a certain number. For instance: Set data has mean of 75 and standard deviation of 5. No info about size of data set or shape of distribution (therefore use chebyshev's).

1. What can you say about proportions of measurements between 60 and 90. (I got 89%).

2. Between 65 and 85. ( I got 75%)

3. Above 90? This is where I get stuck! Can someone please help me?

1. What can you say about proportions of measurements between 60 and 90. (I got 89%).

2. Between 65 and 85. ( I got 75%)

3. Above 90? This is where I get stuck! Can someone please help me?

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- #4

statdad

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The only improvement I would make on these numbers is to sayI just can't grasp how to figure out proportions of measurements below a certain number. For instance: Set data has mean of 75 and standard deviation of 5. No info about size of data set or shape of distribution (therefore use chebyshev's).

1. What can you say about proportions of measurements between 60 and 90. (I got 89%).

2. Between 65 and 85. ( I got 75%)

3. Above 90? This is where I get stuck! Can someone please help me?

Remember from part (1) of your question that

of the data. It's possible that all of it missing data is above 90, so the only conclusion to make is

- #5

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ah ha. Well that seems almost to easy. Thanks statdad, I really appreciate the help!

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