## Distributions

### Distributions

What are the different type of distributions that we need to know for exam.

Any detailing? Continuous, Normal, lognormal,uniform,discrete etc...

Any detailing? Continuous, Normal, lognormal,uniform,discrete etc...

### Re: Distributions

hi Dipti ,

Here is a link related to this topic (Courtesy : Deep Fried Brain ). Please have a look :

http://www.deepfriedbrainproject.com/20 ... jects.html

Thanks

Here is a link related to this topic (Courtesy : Deep Fried Brain ). Please have a look :

http://www.deepfriedbrainproject.com/20 ... jects.html

Thanks

Regards ,

Ranjit

Ranjit

### Re: Distributions

Thankyou Ranjit!!!

### Re: Distributions

So far on the mock test and other forums I haven't seen any probability questions. Also PMBOK and Rita does not provide any details on these areas. Experts please advice

### Re: Distributions

Please disregard my previous post...posted it in the wrong thread. Sorry for the confusion

### Re: Distributions

Do we any thing called Logarithmic distribution.? For below question both A and B looks invalid example to me. But as per explanation ONLY A is invalid example.

Probability distributions are frequently used in Perform Quantitative Risk Analysis. Which of these is not a valid example of such a distribution?

A. Sigma distribution

B. Logarithmic distribution

C. Triangular distribution

D. Beta distribution

Answer A –

Explanation - The response 'Sigma distribution' is not a valid distribution. Continuous probability

distributions represent the uncertainty in values, such as durations of schedule activities and

costs of project components. Triangular, Beta, Logarithmic, Normal and Uniform

distributions are other examples of commonly used distributions. [PMBOK 5th edition, Page

337] [Project Risk Management]

Probability distributions are frequently used in Perform Quantitative Risk Analysis. Which of these is not a valid example of such a distribution?

A. Sigma distribution

B. Logarithmic distribution

C. Triangular distribution

D. Beta distribution

Answer A –

Explanation - The response 'Sigma distribution' is not a valid distribution. Continuous probability

distributions represent the uncertainty in values, such as durations of schedule activities and

costs of project components. Triangular, Beta, Logarithmic, Normal and Uniform

distributions are other examples of commonly used distributions. [PMBOK 5th edition, Page

337] [Project Risk Management]

### Re: Distributions

I think for PMP Exam , we no need to go very deep into the details of all forms of distributions. Here is a link to the Logarithmic distribution for quick reference:

http://mathworld.wolfram.com/Logarithmi ... ution.html

http://mathworld.wolfram.com/Logarithmi ... ution.html

Regards ,

Ranjit

Ranjit

### Re: Distributions

PMBOK or Rita does not say any thing about it

### Re: Distributions

In PMBOK5 we have triangular and beta distributions(Pg-337) as part of Tools & Technique in Quantitative Risk Analysis.

IMO Beta distribution is more applicable.

Regards

Shankar

IMO Beta distribution is more applicable.

Regards

Shankar

### Re: Distributions

Shankar,

Cheers,

Bhabani

**its asking not a valid example**. So certainly Beta Distribution is not a correct answer. Risk Analysis certainly does not use the Sigma Distribution neither does Logarithmic distribution.Cheers,

Bhabani

### Re: Distributions

My bad Bhabani.

Agreed.

Regards

Shankar

Agreed.

Regards

Shankar

### Re: Distributions

Logarithmic distribution is log normal of the distribution

### Re: Distributions

@coolpmp69,

Could you kindly provide an example

Could you kindly provide an example

### Re: Distributions

@ bs_pani

If you see skewedness of data especially in the right tail in the normal distribution then it is due to large number of positive outliers. In such case you would plot a log normal distribution of the same data so that you would get a closer to normal curve (of course Log normal distribution is not symmetrical like normal distribution ) as log value of any number/outlier is smaller their actual value.

Example: real estate properties value, oil prices, stock prices etc.

Hope this helps

If you see skewedness of data especially in the right tail in the normal distribution then it is due to large number of positive outliers. In such case you would plot a log normal distribution of the same data so that you would get a closer to normal curve (of course Log normal distribution is not symmetrical like normal distribution ) as log value of any number/outlier is smaller their actual value.

Example: real estate properties value, oil prices, stock prices etc.

Hope this helps

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