Probability with normal distributions

In summary, the probability of Smith returning to A after Jones arrives at B is equal to the probability that Smith's time is greater than Jones's time, which can be calculated as P(4 + X > Y) where X follows a normal distribution with mean 5 minutes and standard deviation 1 minute, and Y follows a normal distribution with mean 15 minutes and standard deviation 2 minutes. This assumes that the time it takes to get from B to A has the same distribution as the time to get from A to B.
  • #1
thereddevils
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Homework Statement



The time taken by Smith to travel from A to B is a random variable which follows the normal distribution , with mean 5 minutes and standard deviation 1 minute. The time taken by Jones to travel from A to B follows the normal distribution with mean 15 minutes and SD 2 minutes and is independent of the time taken by Smith. Smith and Jones start to move from A to B at the same time. Smith takes only 4 minutes to reach B and then he returns to A. Determine, up to 3 significant figures the probability that Smith will return to A after Jones arrive at B.

Homework Equations





The Attempt at a Solution



Do i calculate the probability that Jones take less than 4 mins to reach B? If so, the probability is simply 0 so this is wrong.

Or do i take this as a conditional probability question since Jones is known to have arrived at B? If so, i am stucked here.
 
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  • #2


Smith's time is 4 + X where X ~ N(5,1).
Jones's time is Y where Y ~ N(15,2).
You want to find P(4 + X > Y).

This is assuming the time it takes to get from B to A has the same distribution as the time to get from A to B.
 
  • #3


awkward said:
Smith's time is 4 + X where X ~ N(5,1).
Jones's time is Y where Y ~ N(15,2).
You want to find P(4 + X > Y).

This is assuming the time it takes to get from B to A has the same distribution as the time to get from A to B.

thanks awkward, that seems to be the probability that smith has returned to A once jones reaches B.

i interpreted it as the prob that smith is about to move from B once jones has reached B but this probability is 1 so i will take your suggesttion. thanks!
 

Related to Probability with normal distributions

What is a normal distribution?

A normal distribution is a probability distribution that follows a bell-shaped curve, with the majority of data points falling near the mean and fewer data points at the extremes. It is commonly used to model natural phenomena such as height, weight, and test scores.

What is the central limit theorem?

The central limit theorem states that the sum of a large number of independent and identically distributed random variables will tend towards a normal distribution, regardless of the underlying distribution of the variables. This makes the normal distribution a useful tool for analyzing data and making predictions.

How do you calculate the probability of a specific value in a normal distribution?

To calculate the probability of a specific value in a normal distribution, you can use the standard normal distribution table or a statistical software. You will need to know the mean and standard deviation of the distribution, and then use the z-score formula to convert the value to a standard normal distribution value. The resulting value can be looked up in the table or calculated using the software.

What is a z-score?

A z-score, also known as a standard score, is a measure of how many standard deviations a data point is away from the mean of a normal distribution. It is calculated by subtracting the mean from the data point and dividing by the standard deviation. A positive z-score indicates a value above the mean, while a negative z-score indicates a value below the mean.

Why is the normal distribution important in scientific research?

The normal distribution is important in scientific research because many natural phenomena and human traits follow a normal distribution. This allows scientists to make predictions and draw conclusions based on data that can be modeled using the normal distribution. Additionally, the central limit theorem allows researchers to use the normal distribution to analyze data and make inferences, even if the underlying distribution of the data is not normal.

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