Normal distribution head-scratcher

Z=(5-0)/(sqrt(2)*21)=0.052In summary, the question is asking for the probability of the difference between two randomly selected students being more than 5 marks in an exam that follows a normal distribution with a mean of 63 and a standard deviation of 21. To solve this, we can use the properties of independent random variables and find the probability of a new random variable Y (equal to X1-X2) being greater than 5. Using Z=(Y-mean)/std, we can then look up the value of 0.052 in a z-table to find the probability.
  • #1
Mglafas
3
0

Homework Statement



A study investigates the performance of students in an exam, which is known to follow a normal distribution, with a mean of 63 and a standard deviation of 21.

What is the probability that the difference between two randomly selected
students is more than 5 marks?

Homework Equations



P(|x1-x2|>5)


Any ideas? Many thanks :)
 
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  • #2
Mglafas said:

Homework Statement



A study investigates the performance of students in an exam, which is known to follow a normal distribution, with a mean of 63 and a standard deviation of 21.

What is the probability that the difference between two randomly selected
students is more than 5 marks?

Homework Equations



P(|x1-x2|>5)Any ideas? Many thanks :)

Try using

Y=X1-X2 as a new random variable with mean 0 and standard deviation of sqrt(2)*21

P(Y>5)
Z=(Y-mean)/std

P(Z>.052) = 1-P(Z<.052)

so now you can find the values in a z-table.

I haven't thought of the other case in which X2>X1, but this is at least how you should think about the question.

With such a high deviation intuitively you should be getting a high probability.
 
Last edited:
  • #3
korican04 said:
Try using

Y=X1-X2 as a new random variable with mean 0 and standard deviation of sqrt(2)*21

P(Y>5)
Z=(Y-mean)/std

P(Z>.052) = 1-P(Z<.052)

so now you can find the values in a z-table.

I haven't thought of the other case in which X2>X1, but this is at least how you should think about the question.

With such a high deviation intuitively you should be getting a high probability.

How did you get 0.052? Mean=0, SD= sqrt(42)?
 
  • #4
For normal distributions you can use the properties if X1 and X2 are two independent random variables then Y=aX1+bX2 is a random variable with normal distribution and mean a*u1+b*u2 and variance a^2*sigma1^2+b^2*sigma2^2

So in our case Y=X1-X2, mean = 1*63+-1*63=0
variance = 1^2*(21^2)+(-1)^2*(21^2)=21^2+21^2=2*21^2
std=sqrt(2)*21

P(Y>5)
You can't look this up. so you look up Z which is (Y-mean)/std.
 

1. What is a normal distribution?

A normal distribution is a type of probability distribution that is characterized by a symmetrical bell-shaped curve. It is often used to describe the distribution of a continuous variable in a population.

2. How is a normal distribution calculated?

A normal distribution is calculated using the mean and standard deviation of a dataset. The mean determines the center of the curve, while the standard deviation determines the spread or variability of the data points around the mean.

3. What is the 68-95-99.7 rule in a normal distribution?

The 68-95-99.7 rule, also known as the empirical rule, states that in a normal distribution, approximately 68% of the data falls within one standard deviation of the mean, 95% falls within two standard deviations, and 99.7% falls within three standard deviations.

4. How is a normal distribution different from other distributions?

A normal distribution differs from other distributions in that it is symmetrical and has a bell-shaped curve. It also has specific characteristics, such as a mean, median, and mode that are all equal, and a certain percentage of data falling within one, two, and three standard deviations from the mean.

5. What is the significance of a normal distribution in statistics?

A normal distribution is significant in statistics because many natural phenomena and real-world data tend to follow this pattern. It allows for easier interpretation of data and the ability to make predictions and estimates based on the properties of the normal distribution.

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