Probability of Samples Containing Less than 1.6 mg of Suspended Particles?

In summary, the masses of suspended particles in a sample of water taken from a lake can be assumed to be a random variable with a normal distribution. With a mean of 2.17 and a variance of 0.979, the probability of a sample containing less than 1.6 mg of suspended particles is 0.28227. Given that 4 samples are known to contain less than 1.8 mg, the probability that at least 3 of those samples contain less than 1.6 mg is 0.071. However, this does not take into account the fact that the samples are already known to have less than 1.8 mg. Adjusting for this, the correct probability is 0.071
  • #1
thereddevils
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Homework Statement



The masses of suspended particles in a sample of water taken from a lake can be assumed to be a random variable which is normally distributed with mean 2.17 and variance 0.979. Find the probability that out of 4 samples of lake water known to contain less than 1.8 mg of suspended particles , at least 3 samples contain less than 1.6 mg of suspended particles.

Homework Equations





The Attempt at a Solution



probability of a sample less than 1.6 mg, P(X<1.6)=0.28227

X-B(4, 0.28227)

P(X>=3)=P(X=3)+P(X=4)=0.071

Probability of all 4 samples contain less than 1.8 mg of suspended particles = (0.3542)^4
= 0.015

So the required probability = 0.071/0.015 = 4.7

obviously that's wrong. Would appreciate if someone can point me to my errors.
 
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  • #2
For one, you're using the variable X to represent two different random variables.

You basically have the right idea, but you want to take into account the fact that the samples are known to have less than 1.8 mg of suspended particles right from the start. Instead of using p=P(X<1.6) for the binomial distribution, use p=P(X<1.6|X<1.8).
 
  • #3
vela said:
For one, you're using the variable X to represent two different random variables.

You basically have the right idea, but you want to take into account the fact that the samples are known to have less than 1.8 mg of suspended particles right from the start. Instead of using p=P(X<1.6) for the binomial distribution, use p=P(X<1.6|X<1.8).

thanks Vela.
 

1. What is conditional probability?

Conditional probability is a measure of the likelihood of an event occurring given that another event has already occurred. It is used to calculate the probability of one event happening given that another related event has already occurred.

2. How is conditional probability calculated?

Conditional probability is calculated by dividing the probability of the joint occurrence of two events by the probability of the condition event. This can be expressed as P(A|B) = P(A and B) / P(B), where A and B are two events.

3. What is the difference between conditional and unconditional probability?

Unconditional probability is the probability of an event occurring without any prior knowledge or consideration of other events. Conditional probability, on the other hand, takes into account the occurrence of another related event when calculating the probability of an event.

4. How is conditional probability used in real life?

Conditional probability is used in various fields such as weather forecasting, stock market analysis, and medical diagnosis. For example, in weather forecasting, the probability of rain can be calculated based on the current weather conditions and the probability of rain in similar past conditions.

5. What is the relation between conditional probability and Bayes' Theorem?

Bayes' Theorem is a mathematical formula that uses conditional probability to calculate the probability of an event based on prior knowledge or evidence. It provides a way to update the probability of an event as new information becomes available. Conditional probability is a key component of Bayes' Theorem.

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