Probability - continuous random variables

In summary: Overall, the conversation is about solving two problems related to probability and integration. The first question asks about the probability of at most one smoker having a nicotine level higher than 500, and the second question involves finding the constant c and the marginal probability density functions for a given function.
  • #1
Kate2010
146
0

Homework Statement



Ok, I have 2 questions:

1. Nicotine levels in smokers can be modeled by a normal random variable with mean 315 and variance 1312. What is the probability, if 20 smokers are tested, that at most one has a nicotine level higher than 500?

2. fX,Y (x,y) = xe-x-y 0<x<y<[tex]\infty[/tex]
Find c.
Find the marginal probability density functions.

Homework Equations





The Attempt at a Solution



1. I have worked out that each smoker individually has a 0.079 probability of having a nicotine level higher than 500, but I'm not sure about the at most one section. Would I need to work out 1-P(at least 2 have a nicotine level higher than 500) where P(at least 2 have a nicotine level higher than 500) = P(2 have a nicotine level higher than 500) + P(3 do) + P(4 do) + ... + P(20 do). In this case would it be a geometric sum with a = 0.0792, r = 0.079 and n = 19?

2. It is the limits of integration that I am finding confusing here. I know to find c I need to do the double integral equal to one, but is it [tex]\int[/tex][tex]^{infinity}_{0}[/tex][tex]\int[/tex][tex]^{y}_{0}[/tex] fX,Y (x,y) dx dy (i.e. integrating x between 0 and y and y between 0 and infinity)?

Similarly for the marginal distributions would the limits when integrating with respect to y be 0 and infinity and x be 0 and y?
 
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  • #2
Kate2010 said:

Homework Statement



Ok, I have 2 questions:

1. Nicotine levels in smokers can be modeled by a normal random variable with mean 315 and variance 1312. What is the probability, if 20 smokers are tested, that at most one has a nicotine level higher than 500?

2. fX,Y (x,y) = xe-x-y 0<x<y<[tex]\infty[/tex]
Find c.
Find the marginal probability density functions.

Homework Equations





The Attempt at a Solution



1. I have worked out that each smoker individually has a 0.079 probability of having a nicotine level higher than 500, but I'm not sure about the at most one section. Would I need to work out 1-P(at least 2 have a nicotine level higher than 500) where P(at least 2 have a nicotine level higher than 500) = P(2 have a nicotine level higher than 500) + P(3 do) + P(4 do) + ... + P(20 do). In this case would it be a geometric sum with a = 0.0792, r = 0.079 and n = 19?

P(at most 2) = P(0) + P(1). And if your p = .079 is correct and X is the number with nicotine over 500, then isn't X binomial(20,p)?

2. It is the limits of integration that I am finding confusing here. I know to find c I need to do the double integral equal to one, but is it [tex]\int[/tex][tex]^{infinity}_{0}[/tex][tex]\int[/tex][tex]^{y}_{0}[/tex] fX,Y (x,y) dx dy (i.e. integrating x between 0 and y and y between 0 and infinity)?

Similarly for the marginal distributions would the limits when integrating with respect to y be 0 and infinity and x be 0 and y?

Your limits are correct. Click on the expression below to see how to render it in tex:

[tex]\int_0^\infty \int_0^y f(x,y)dxdy[/tex]
 
  • #3
Kate2010 said:
the limits when integrating with respect to y be 0 and infinity and x be 0 and y?


I didn't notice this final question. For the marginals your limits are determined by the non-zero region. So when integrating in the x direction you will go from 0 to y and when integrating in the y direction, it will go from x to infinity. Your nonzero domain is in the first quadrant above the line y = x.
 
  • #4
Thanks a lot, really helpful :)
 
  • #5
Ah ok, so fX(x) = [tex]\int_x^\infty f(x,y) dxdy[/tex] not fX(x) = [tex]\int_0^\infty f(x,y) dxdy[/tex]?
 
  • #6
Kate2010 said:
Ah ok, so fX(x) = [tex]\int_x^\infty f(x,y) dxdy[/tex] not fX(x) = [tex]\int_0^\infty f(x,y) dxdy[/tex]?
Probably just a typo, but you shouldn't have dx in the integrals because you're only integrating over y. And yes, the limits on your first integral is what LCKurtz meant.
 

1. What is a continuous random variable?

A continuous random variable is a type of random variable that can take on any numerical value within a specific range. Unlike discrete random variables, which can only take on certain values, continuous random variables can take on an infinite number of values. Examples of continuous random variables include height, weight, and time.

2. How is probability calculated for continuous random variables?

The probability of a continuous random variable is calculated using a probability density function (PDF). This function describes the likelihood of a particular value occurring within a given range. To find the probability of a specific value, the PDF must be integrated over the range of that value. This integration results in the area under the curve of the PDF, which represents the probability of that value occurring.

3. What is the difference between discrete and continuous random variables?

The main difference between discrete and continuous random variables is the type of values they can take on. Discrete random variables can only take on specific, distinct values, while continuous random variables can take on any value within a specific range. Additionally, the probability of a specific value occurring for a discrete random variable is a single value, while for a continuous random variable, it is a range of values.

4. What is the central limit theorem and how does it relate to continuous random variables?

The central limit theorem states that if a large enough sample is taken from a population, the sample mean will be approximately normally distributed, regardless of the underlying distribution of the population. This theorem is important for continuous random variables because it allows us to use the normal distribution to make inferences about the population mean, even if the population is not normally distributed.

5. Can continuous random variables be used in real-life situations?

Yes, continuous random variables are commonly used in real-life situations. In fact, many natural phenomena, such as human height and weight, follow a continuous distribution. Continuous random variables are also used in statistical analysis and modeling, as they allow for more precise and accurate predictions than discrete random variables.

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