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    Hey im trying to do some stats practise for summer and need help going through this question im trying to do:

    The diameter of cable can be assumed to have a normal distribution with mean 0.9cm and standard deviation 0.015cm

    *the cable is considered defective if the diameter differs frm the mean by more than 0.025cm. Calculate the probability that a randomly selected cable is defective?

    * the company incurs a penalty of £20 for every defective cable and each cable whose diamter differs from the mean by more than 0.04cm incurs a further penalty of £10. calculate expected penalty per cable
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    Remember for the first one than it can be too fat or too thin, and so the probability you want is
    2P(T<0.875)

    For the next part, construct a table for the possiblities: £0, £20, £30, and calculate the probability for each. The most important thing is that the probability of £20 is not as simple as normal.

    After doing this, use expectation theory to find the expected penalty, where \displaystyle E(P)=\sum_i{P_ip_i}. The notation meaning p_i is the probability of penalty P_i occurring.
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    (Original post by coffeym)
    Remember for the first one than it can be too fat or too thin, and so the probability you want is
    2P(T<0.875)

    For the next part, construct a table for the possiblities: £0, £20, £30, and calculate the probability for each. The most important thing is that the probability of £20 is not as simple as normal.

    After doing this, use expectation theory to find the expected penalty, where \displaystyle E(P)=\sum_i{P_ip_i}. The notation meaning p_i is the probability of penalty P_i occurring.
    ok i really dont get the first part
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    (Original post by Amiro)
    ok i really dont get the first part
    P(T < 0.875) + P(T > 0.925). Both of which are the same since the distribution is symmetrical.
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    (Original post by generalebriety)
    P(T < 0.875) + P(T > 0.925). Both of which are the same since the distribution is symmetrical.
    do i square root standard deviation to get vairance
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    No, variance = (standard deviation)^2
 
 
 
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