Help with Calculating Central Moments of E[(x^3 + 7x^2)^3]

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In summary, central moments are statistical measures used to describe the shape, location, and variability of a probability distribution. To calculate central moments, one must first find the mean of the distribution, raise each value to a power, multiply it by its corresponding probability, and then sum all of these values together. This can also be used to calculate central moments of a polynomial distribution using the formula E[(x - μ)^k]. Central moments differ from raw moments in that they are calculated relative to the mean of the distribution, while raw moments are calculated relative to the origin. Finally, calculating central moments can provide valuable insights into a distribution's shape, central tendency, and variability, making it a useful tool for making inferences and drawing conclusions about a
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tuanle007
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can someone help me with this problem?

E[(x^3 + 7x^2)^3]
what is the central moments?

mij=E[(x-xbar)^i * (y-ybar)^j]
 
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Welcome to PF

If you don't specify what exactly you want to know, where your problem lies, I cannot help you - and don't want to.

A good thing to start would be to tell something about the distribution of what you called X ...or Y ...
 

Related to Help with Calculating Central Moments of E[(x^3 + 7x^2)^3]

1. What are central moments in statistics?

Central moments are a set of statistical measures that describe the shape, location, and variability of a probability distribution. They are calculated by taking the moments of a random variable relative to its mean.

2. How do I calculate central moments?

To calculate central moments, you first need to find the mean of the distribution. Then, you raise each value in the distribution to a power and multiply it by the corresponding probability. Finally, you sum all of these values together to get the central moment.

3. How do I calculate central moments of a polynomial distribution?

To calculate central moments of a polynomial distribution, you can use the formula E[(x - μ)^k] where x is the random variable, μ is the mean, and k is the desired moment. For the example given, k would be 3.

4. What is the difference between central moments and raw moments?

Central moments are calculated relative to the mean of a distribution, while raw moments are calculated relative to the origin (usually 0). This means that central moments are more useful for describing the shape and variability of a distribution, while raw moments are more useful for calculating other statistical measures like skewness and kurtosis.

5. How can calculating central moments help me understand a distribution?

Central moments provide information about the shape, location, and variability of a distribution. They can help you determine if a distribution is symmetric or skewed, if it has a central tendency, and how spread out the data is. This information can be useful for making inferences and drawing conclusions about a population based on a sample.

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