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- This topic has 1 reply, 2 voices, and was last updated 1 year ago by John Moffat.

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- October 24, 2022 at 8:41 am #669791
As standard deviation is mentioned in the technical articles do I have to practice numerical questions on this topic or just having thorough understanding of the topic is enough for the examination?

Moreover can you confirm me on standard error of mean? From what I have understood, it is the difference between mean of population and mean of sample. Tell me if i am wrong.

Moreover, while formulating the regret table if the existing contribution is already negative lets say -60 and the maximum contribution that could have been earned was 100 then the lost contribution will be 160 right. Or 40???

Also for the sake of comfort is it okay to assume the existing decision or decision taken in advance lets say supply level be shown in the top of the page and outcome ie demand level will be in the bottom. ( i mean each decision will have its own column and each outcome will have its own row) i know how to formulate the payable table but some questions really confuse me as they list the decision and outcome in the opposite manner from what i have understood so For the sake of comfort can I make this generalisation.

October 24, 2022 at 9:45 am #669809Calculation of the standard deviation is examinable in Paper MA. That means that in theory it is also examinable in Paper PM, but that is extremely unlikely and at most an understanding is all that is required.

The standard error is looking at the difference between the mean of the sample and the mean of the population (although it is not simply subtracting one from the other),. However this is not examined in Paper PM (the article you refer to was an article meant for four papers and not all of it is examinable in each of the four papers).

As far as the regret table is concerned, then what you have written is correct. It does not matter which way round you produce the table (i.e. which as rows and which as columns) – it is understanding what happens that matters.

This is all explained in detail in my free lectures on decision making under uncertainty.

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