Statistics - normal distribution

In summary: I get it now!In summary, the problem involves finding the probability of two randomly picked components differing by more than 0.3mm, given that the components are produced by two different plants with different distributions for their diameters. By creating a new random variable representing the difference in diameters and using the appropriate mean and standard deviation, the correct solution can be obtained.
  • #1
cohkka
6
0

Homework Statement


A plant manufactures 500 components a day with the diameter being random variable:
N(8.02, 0.1^2)mm

What is the probability of two randomly picked components differing by more than 0.3mm?2. Solution
I know that the solution is 0.966

The Attempt at a Solution


I thought that I needed to find the probability of the diameter differing from the mean by more than 0.3mm:
P(X < 7.72) + P(X > 8.32)
= 1 - P(7.72 < X < 8.32)
= 1 - { Phi[(8.32-8.02)/0.1] - Phi[(7.72-8.02)/0.1] }
= 1 - { Phi[3] - Phi[-3] }
= 1 - { Phi[3] - (1 - Phi[3]) }
= 1 - {0.99865 - (1-0.99865)}
= 1 - 0.9973
= 0.0027

This obviously is far from the correct answer and so I realize I must be completely on the wrong track but don't know how else one might approach this problem. I thought that the question might instead be asking for the probability of the difference of the two diameters being greater than 0.3mm so
P(a < X < a+0.3) but have no idea how you would go about finding the probability of an unknown value.

As you can probably tell, I am really confused by this so any help would be much appreciated! Thank you!
 
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  • #2
You have two random variables: X1, the diameter of the first ball, and X2, the diameter of the second ball. Now form a third random variable Y=X1-X2, which is the difference in their diameters. Do you see how to solve the problem now?
 
  • #3
I think I might have got it now...

So for this new variable Y=X1-X2, I have to also calculate a new standard deviation which is sqrt(0.12 + 0.12) = 0.1*sqrt(2)

So P(X>0.3) = 1 - P(X<0.3)
= 1 - Phi[0.3/(0.1*sqrt(2))]
= 1 - Phi[2.1213...]
= 1 - 0.983
= 0.0169
P(X<-0.3) = 0.0169
P(-0.3<X<0.3) = 1 - 2*0.0169
= 0.9661 (I guess the solution must have just been given for the difference being less than 0.3mm??)


Is this right? I understand why this would be correct but then when I apply this to the next part of the question, I do not get the right answer.
Now another plant B produces components with diameter being random variable N(7.95,0.082)

What now is the probability of the diameter of two randomly picked components differing by more than 0.3mm if one is produced by plant B and the other by the first plant?

I went about this in the same way, calculating a standard deviation for this distribution:
sqrt(0.12 + 0.082) = [sqrt(41)]/50

Using the mean as zero and this standard deviation, I get P(-0.3<X<0.3)=0.9808, whereas the solution I am given is 0.9662...where am I going wrong

Sorry for the long post!
 
  • #4
This is where you're going wrong:

cohkka said:
Using the mean as zero and this standard deviation
 
  • #5
Ahhhhhh THANK YOU SO, SO MUCH! I have been struggling with this question for so long!
 

FAQ: Statistics - normal distribution

What is a normal distribution?

A normal distribution is a type of probability distribution that is characterized by a bell-shaped curve. It is also known as a Gaussian distribution, and it is often used to describe the distribution of continuous variables in a population.

What is the mean and standard deviation in a normal distribution?

The mean, or average, of a normal distribution is located at the peak of the bell-shaped curve. The standard deviation measures the spread of the data around the mean. In a normal distribution, approximately 68% of the data falls within one standard deviation of the mean, 95% within two standard deviations, and 99.7% within three standard deviations.

How is the normal distribution used in statistics?

The normal distribution is used in statistics to make predictions about a population based on a sample. It is also used to calculate probabilities and to determine the likelihood of obtaining a certain value or range of values. Many statistical tests and models assume a normal distribution of the data.

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% within two standard deviations, and 99.7% within three standard deviations. This rule is used to estimate the spread of data in a normal distribution.

What is the difference between a normal distribution and a skewed distribution?

In a normal distribution, the data is evenly distributed around the mean, resulting in a symmetrical bell-shaped curve. In a skewed distribution, the data is not evenly distributed and the curve is asymmetrical. Skewed distributions can be either positively skewed (tail to the right) or negatively skewed (tail to the left).

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