Is My Proof for Convergence to the Mean Valid?

In summary, the conversation is about a handwritten proof attached as a .jpg file. The person is unsure if their proof is legitimate but believes it is fine because the size of 1/n is less than epsilon/kM and k is fixed. They ask for someone to review the proof and confirm its validity.
  • #1
SiddharthM
176
0
Convergence in the Mean. I attached a picture of a handwritten proof because I don't know how to use Latex and there is too much hullabaloo to type it out without the notation.

I'm wondering if my proof is legit - i make the size of 1/n be less than epsilon/kM...i'm not sure...hmmmmmmm, I think it's fine though because that k is fixed.

Let me know if my proof is satisfactory.

a .jpg file is attached with the proof.
 

Attachments

  • Convergence in the mean.jpg
    Convergence in the mean.jpg
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  • #2
"n > 2kM/e" would be more direct than the reciprocals.
 
  • #3
so it's correct?
 
  • #4
I looked at it quickly and didn't see any obvious errors.
 
  • #5
can someone please take a serious look at this and tell me whether or not the proof is fine?
 

FAQ: Is My Proof for Convergence to the Mean Valid?

What is "Convergence to the Mean"?

Convergence to the Mean is a statistical principle that suggests that over time, extreme or outlier values in a data set will become less extreme and closer to the average or mean value.

Why is "Convergence to the Mean" important?

Convergence to the Mean is important because it helps us understand and predict the behavior of data over time. It allows us to make more accurate predictions and identify any potential trends or patterns in the data.

How does "Convergence to the Mean" occur?

Convergence to the Mean occurs due to the law of large numbers, which states that as the sample size increases, the average of the sample will approach the true population mean. This means that with more data, extreme values will become less significant and the overall average will be a better representation of the data.

Can "Convergence to the Mean" be applied to any type of data?

Yes, "Convergence to the Mean" can be applied to any type of data, as long as the data follows a normal distribution. However, it may not be as applicable to data that follows a non-normal distribution.

What are some potential limitations of "Convergence to the Mean"?

One potential limitation of "Convergence to the Mean" is that it assumes the data is independent and identically distributed. In real-world data, this may not always be the case. Additionally, it may not always hold true for small sample sizes or if the data has a strong trend or pattern. It is important to consider these limitations when applying this principle to a data set.

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