Calculating Probabilities for X and Y Normal Distributions

In summary: I suppose the way I try to deal with it is from a standpoint of a teacher, or a coach. You try to guide, but not give away - make it so they have to think and work things through to connect the dots. Any student who has been in a classroom for a while knows the feeling of having a teacher that provides "overkill" solutions, and those that give you nothing. I try to walk the middle ground, but it is always a difficult line to walk."I agree with the 'not giving away' part, but he did already have the correct answer---or at least, the correct numerical inputs---so I did not feel guilty about laying out some more detail."Probably true - actually, just plain "
  • #1
joemama69
399
0

Homework Statement



X refers to score distribution in Math and Y refers to score distribution in Stat in a certain degree course exam. It is known that X~N(mean = 62, sigma=7) while Y~N(mean = 68, sigma=10). If X and Y are independent, find (i) P[X+Y>120]; (ii) P[X<Y]; (iii) P[X+Y>140]; (iv)P[100 < X+Y < 150].

Homework Equations





The Attempt at a Solution



P(X+Y>120)...

So I have to combine X & Y into one Norm Distribution by find there combined E(x) & V(x)

E(X+Y)=E(X)+E(Y)=62+68=130

V(X+Y)=V(X)+V(Y)=7^2 + 10^2 = 149, Standard Dev = sqrt(149)

So where Z = X + Y > 120, Z~N(130,sqrt(149))

P(X+Y>120) = P(Z>120)=1-PHI((130-120)/sqrt(149))

Is this correct so far...


P(X<Y)=P(X-Y<0)

E(X-Y)=E(X)-E(Y) = 62-68=-6

V(X-Y) = V(X)+V(Y)=sqrt(149)

W~N(-6,sqrt(149))

P(X-Y<0)=P(W<0)=PHI((-6-0)\sqrt(149))

How does this look. The rest are basically the same I think if I got these two conceptualy correct. any issues?
 
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  • #2
I haven't checked down to the arithmetic level but the approach looks good. One thing that helped me keep things straight notationally was this: When dealing with the first part, where you are working with the sum of two random variables, let [itex] S = X + Y [/itex]. Then the work you have shows that [itex] S [/itex] has a normal distribution with a particular mean and standard deviation, and I just found it easier to write
[itex] P(S > 120) [/itex] instead of the way you had it. Similar for the difference.

After a final glance I do see one item I believe you should reconsider. Do you REALLY want to write
[tex]
V(X-Y) = V(X) + V(Y) = sqrt(149)
[/tex]

(I am pointing my comment at what you have after the second equal sign) - remember in those steps you are finding a variance.
 
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  • #3
statdad said:
I haven't checked down to the arithmetic level but the approach looks good. One thing that helped me keep things straight notationally was this: When dealing with the first part, where you are working with the sum of two random variables, let [itex] S = X + Y [/tex]. Then the work you have shows that [itex] S [/itex] has a normal distribution with a particular mean and standard deviation, and I just found it easier to write
[itex] P(S > 120) [/itex] instead of the way you had it. Similar for the difference.

After a final glance I do see one item I believe you should reconsider. Do you REALLY want to write
[tex]
V(X-Y) = V(X) + V(Y) = sqrt(149)
[/tex]

(I am pointing my comment at what you have after the second equal sign) - remember in those steps you are finding a variance.

The equation ##V(X-Y) = V(X) + V(y)## is true if X and Y are independent, so what he wrote is correct. In fact, if ##a## and ##b## are constants, we have
[tex] V(aX + bY) = a^2 V(X) + b^2 V(Y)[/tex] for independent (or uncorrelated) X and Y, whether normally-distributed or not. It is a general result.
 
  • #4
Ray Vickson said:
The equation ##V(X-Y) = V(X) + V(y)## is true if X and Y are independent, so what he wrote is correct. In fact, if ##a## and ##b## are constants, we have
[tex] V(aX + bY) = a^2 V(X) + b^2 V(Y)[/tex] for independent (or uncorrelated) X and Y, whether normally-distributed or not. It is a general result.

I am aware of all this. You missed my point, however: it is likely my comment wasn't clear. The OP is attempting to calculate the variance of X - Y: the written equation was (my comments added)
[tex]
V(X-Y) = \underbrace{V(X) + V(Y)}_{\text{Okay here}} = \overbrace{\text{sqrt}(149)}^{\text{My concern}}
[/tex]

For emphasis, compare the ending of the above work to the ending of the line in which the variance of [itex] V(X + Y) [/itex] was correctly calculated.

Marginally related comment: apologies for missing the fact that I screwed up a tag in one of my earlier posts.
 
  • #5
statdad said:
I am aware of all this. You missed my point, however: it is likely my comment wasn't clear. The OP is attempting to calculate the variance of X - Y: the written equation was (my comments added)
[tex]
V(X-Y) = \underbrace{V(X) + V(Y)}_{\text{Okay here}} = \overbrace{\text{sqrt}(149)}^{\text{My concern}}
[/tex]

For emphasis, compare the ending of the above work to the ending of the line in which the variance of [itex] V(X + Y) [/itex] was correctly calculated.

Marginally related comment: apologies for missing the fact that I screwed up a tag in one of my earlier posts.

Just to be clear: you (correctly) want him to write '149' instead of 'sqrt(149)'. To my shame, I missed that the first time around!
 
  • #6
"Just to be clear: you (correctly) want him to write '149' instead of 'sqrt(149)'. To my shame, I missed that the first time around!"

No big deal. As I noted, I was not entirely clear in my original note. That comes, I am afraid, from reviewing student papers and wanting to point out issues for them without giving the whole thing away.
 
  • #7
statdad said:
"Just to be clear: you (correctly) want him to write '149' instead of 'sqrt(149)'. To my shame, I missed that the first time around!"

No big deal. As I noted, I was not entirely clear in my original note. That comes, I am afraid, from reviewing student papers and wanting to point out issues for them without giving the whole thing away.

I agree with the 'not giving away' part, but he did already have the correct answer---or at least, the correct numerical inputs---so I did not feel guilty about laying out some more detail.
 
  • #8
Probably true - actually, just plain "yup" on that. Finding a way to guide someone along giving support while pointing out subtle, tertiary level errors, is always a difficult task for me.
 

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1. What is a normal distribution?

A normal distribution, also known as a Gaussian distribution, is a type of probability distribution that is commonly observed in natural phenomena such as height, weight, and IQ. It is symmetrical and bell-shaped, with the majority of data falling near the mean and gradually decreasing towards the tails.

2. How is a normal distribution characterized?

A normal distribution is characterized by two parameters: its mean (μ) and standard deviation (σ). The mean determines the center of the distribution, while the standard deviation measures the spread or variability of the data.

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

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

4. Can a normal distribution have negative values?

Yes, a normal distribution can have negative values. However, the mean does not necessarily have to be at the center of the distribution for it to be considered normal.

5. How is the normal distribution useful in statistics?

The normal distribution is useful in statistics because it is a common and well-understood distribution that can be used to model many natural phenomena. It allows for the calculation of probabilities and the use of statistical tests to make inferences about a population based on a sample.

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