Uniform discrete probablity distributions

In summary: This is the result of the first test. What changes for the subsequent tests?In summary, the random variable X represents the number of tests required to determine the faulty component in an electric circuit with 5 components. The probability of the first test yielding the faulty component is 1/5, and for each subsequent test, the probability decreases as the number of potential faulty components decreases. This means that the sum of all probabilities for X must equal 1, as the faulty component is guaranteed to be found within the 5 tests.
  • #1
desmond iking
284
2

Homework Statement


An electric circuit has 5 components. It is known that one of the componenets is faulty. To detremine which one is faulty, all 5 componenets are tested one by one until the faulty component is found, The random variable X represents the number of test required to determine the faulty unit.


Homework Equations





The Attempt at a Solution


here's my working:
I let X = number of tests conducted until the faulty unit is found
P(X= 1) = 1/5
P(X=2) = P(good) x P( faulty ) = 4/5 x 1/5 = 0.16
P(X=3) = P(good) x P(good) x P( faulty ) = 4/5 x 4/5 x 1/5 = 0.128
P(X=4) = P(good) x P(good) x P(good) x P( faulty ) = 4/5 x 4/5 x 4/5 x 1/5 = 64/625
P(X=5) = P(good) x P(good) x P(good) x P(good) x P( faulty ) = 4/5 x 4/5 x 4/5 x 4/5 x 1/5 = 256/3125


But for the discrete probablity distributions , sum of all probability must be equal to 1 ... my sum of probability isn't equal to 1 , which is indeed wrong. why can't i do in this way?
 
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  • #2
0.16 + 0.128+ 1/5 + 64/625 +256/3125 not equal to 1
 
  • #3
desmond iking said:

Homework Statement


An electric circuit has 5 components. It is known that one of the componenets is faulty. To detremine which one is faulty, all 5 componenets are tested one by one until the faulty component is found, The random variable X represents the number of test required to determine the faulty unit.


Homework Equations





The Attempt at a Solution


here's my working:
I let X = number of tests conducted until the faulty unit is found
P(X= 1) = 1/5
P(X=2) = P(good) x P( faulty ) = 4/5 x 1/5 = 0.16
P(X=3) = P(good) x P(good) x P( faulty ) = 4/5 x 4/5 x 1/5 = 0.128
P(X=4) = P(good) x P(good) x P(good) x P( faulty ) = 4/5 x 4/5 x 4/5 x 1/5 = 64/625
P(X=5) = P(good) x P(good) x P(good) x P(good) x P( faulty ) = 4/5 x 4/5 x 4/5 x 4/5 x 1/5 = 256/3125


But for the discrete probablity distributions , sum of all probability must be equal to 1 ... my sum of probability isn't equal to 1 , which is indeed wrong. why can't i do in this way?

Because after observing the first one to be non-defective the remaining four are known to have one defective. That changes probabilities to 1/4 now.
 
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  • #4
Ray Vickson said:
Because after observing the first one to be non-defective the remaining four are known to have one defective. That changes probabilities to 1/4 now.

do you mean the P(x=2) should be 4/5 x 1/4 = 4/20 ?
 
  • #5
desmond iking said:
do you mean the P(x=2) should be 4/5 x 1/4 = 4/20 ?

Yes. What do you think this implies for the remaining probabilities?
 
  • #6
desmond iking said:
do you mean the P(x=2) should be 4/5 x 1/4 = 4/20 ?

You tell me.
 

FAQ: Uniform discrete probablity distributions

What is a uniform discrete probability distribution?

A uniform discrete probability distribution is a statistical distribution in which all possible outcomes have an equal probability of occurring. This means that in a set of discrete data, each value has the same chance of being selected as any other value.

How is a uniform discrete probability distribution different from other types of distributions?

A uniform discrete probability distribution is different from other types of distributions because it has a constant probability for each possible outcome. Other distributions, such as normal distributions, have varying probabilities for different outcomes.

What is an example of a situation where a uniform discrete probability distribution might be used?

A common example of a uniform discrete probability distribution is rolling a fair die. In this case, each number (1-6) has an equal chance of being rolled, making it a uniform distribution.

How is a uniform discrete probability distribution represented mathematically?

A uniform discrete probability distribution can be represented mathematically using the formula P(x) = 1/n, where n is the number of possible outcomes and P(x) is the probability of any given outcome.

What is the importance of understanding uniform discrete probability distributions in scientific research?

Uniform discrete probability distributions are important in scientific research because they allow us to model and analyze data that has a constant probability for each possible outcome. This can help us make predictions and draw conclusions about a population based on a sample of data.

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