Weibull distribution derivation

In summary, the speaker is looking for a proof of the Weibull distribution but has not been able to find a reasonable one. They are curious about the origin of the distribution and wondering if there is a problem with their question. They are advised to read Weibull's original paper, where he discovered the distribution empirically to fit various observations in failure analysis. The speaker is asked to clarify what exactly they are trying to prove.
  • #1
mertcan
344
6
hi, I am looking for a proof of weibull distribution, I have searched a lot but nothing is reasonable for the proof, so ıs there derivation of this distribution ? What is the origin of this distribution ?
 
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  • #2
I am asking because of my curiosity, initially I would like to express that there are lots of posts have been created under this highlight after my post, and I see plenty of replies exist for those posts, but still I do not have any responses to my question. Is there a problem with my question, did I create my post under wrong highlight ?
 
  • #3
You can read Weibull's original paper, he hit upon his distribution empirically because it seemed to fit various observations in failure analysis. What exactly are you looking to prove?
 

1. What is the Weibull distribution?

The Weibull distribution is a continuous probability distribution that is commonly used in reliability engineering and survival analysis. It is used to model time-to-failure of a system or the time until an event occurs.

2. How is the Weibull distribution derived?

The Weibull distribution is derived from the exponential distribution by introducing a shape parameter, which allows for a more flexible and accurate representation of real-world data. The derivation involves manipulating the probability density function of the exponential distribution and solving for the new shape parameter.

3. What are the key properties of the Weibull distribution?

The Weibull distribution has three key properties: shape, scale, and location. The shape parameter determines the shape of the curve, the scale parameter determines the spread of the curve, and the location parameter shifts the curve along the x-axis. These properties allow the Weibull distribution to be fitted to a wide range of data.

4. What are the applications of the Weibull distribution?

The Weibull distribution has many practical applications in various fields, including reliability engineering, survival analysis, finance, and meteorology. It is commonly used to model the lifetime of electronic components, the failure rate of mechanical systems, the duration of disease survival, and the occurrence of extreme weather events.

5. What are the limitations of the Weibull distribution?

While the Weibull distribution is a versatile and widely used distribution, it is not suitable for all types of data. It assumes that the failure rate is constant over time, which may not be the case for some systems. Additionally, the Weibull distribution is not a good fit for data with multiple failure modes or data that exhibit non-monotonic behavior. In these cases, alternative distributions may be more appropriate.

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