Finding the Probability Density Function

In summary: Finally, we know that cos(θ) is equal to y/r, so we can replace it in our equation:p(y) = (1/Pi)*|y/r|/rAnd there we have it! The PDF for y is p(y) = (1/Pi)*|y/r|/r. This means that the probability density is proportional to |y|, which suggests that the PDF for y is a triangular distribution. This makes sense, as the needle is equally likely to come to rest at any angle between 0 and Pi, which means that the y-coordinate will also be equally likely to fall anywhere between 0 and r.In summary, to find the PDF for the y-coordinate of
  • #1
sarujin
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Homework Statement


A dial indicator has a needle that is equally likely to come to rest at an angle between 0 and Pi. Consider the y-coordinate of the needle point (projection on the vertical axis). What is the probability density function (PDF) p(y)?


Homework Equations


I know the integral of p(y) over all space has to equal 1. The y-coordinate of the dial is of course radius*sine(theta).


The Attempt at a Solution



The first part of the question asked for the PDF for the angle, which wasn't too difficult. Knowing the integral over all space had to equal 1 and that the probability was a constant I could see that p(theta)=1/Pi . I just can't find any recipe on how to come up with the PDF for the y coordinate, in most cases it is given! I can see that it must be zero at both y=0 and y=r, which suggests to me it is probably sine or sine^2 but I cannot prove it.

Thanks a bunch.
 
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  • #2




Thank you for your question. I can provide some insight into how to approach this problem. First, let's define some variables:

- θ: the angle at which the needle comes to rest, between 0 and Pi
- r: the radius of the dial indicator
- y: the y-coordinate of the needle point (projection on the vertical axis)

Now, we know that the probability of the needle coming to rest at a particular angle θ is given by p(θ) = 1/Pi. This means that the probability density function for θ is a constant. But how can we find the PDF for y?

To find the PDF for y, we can use the transformation method. This method allows us to find the PDF for a new variable (in this case, y) by transforming the PDF of a known variable (in this case, θ). The transformation we will use is y = r*sin(θ). This means that for a given value of θ, the corresponding value of y can be found by multiplying r by the sine of θ.

Now, let's think about the range of values for y. We know that the needle can come to rest at any angle between 0 and Pi, so the range of values for y will be between 0 and r (since the maximum value of sin(θ) is 1). This means that the PDF for y will be zero for y < 0 and y > r, just as you mentioned.

To find the PDF for y, we can use the transformation formula:

p(y) = p(θ)/|dy/dθ|

where |dy/dθ| is the absolute value of the derivative of y with respect to θ. In this case, we have:

p(y) = p(θ)/|dr*cos(θ)|

Since p(θ) is a constant, we can replace it with 1/Pi. And since cos(θ) is also a constant, we can take it out of the absolute value sign. This gives us:

p(y) = (1/Pi)/|dr|*|cos(θ)|

Now, we know that dr is just a constant value (the radius of the dial indicator), so we can take it out of the absolute value sign as well. This gives us:

p(y) = (1/Pi)*|cos(θ)|
 

Related to Finding the Probability Density Function

1. What is a probability density function (PDF)?

A probability density function (PDF) is a function that describes the relative likelihood of a continuous random variable taking on a certain value. It is often used to model and analyze the probability of continuous events, such as the heights of people or the time it takes for a car to travel a certain distance.

2. How is a probability density function different from a probability mass function (PMF)?

A probability density function (PDF) is used for continuous random variables, whereas a probability mass function (PMF) is used for discrete random variables. The PDF gives the probability of a random variable taking on a specific value, while the PMF gives the probability of a discrete outcome occurring.

3. How do you find the probability density function for a given data set?

To find the probability density function (PDF) for a given data set, you can use the following steps:

  1. Calculate the mean and standard deviation of the data.
  2. Use a statistical software or a graphing calculator to create a histogram of the data.
  3. Find the area under each bar in the histogram and divide it by the total area under the histogram. This will give you the relative frequency of each bar.
  4. Divide the relative frequency of each bar by the width of the bar. This will give you the probability density for each bar.

4. What are some real-world applications of probability density functions?

Probability density functions have numerous applications in various fields, including:

  • Financial analysis: to model stock prices and interest rates
  • Physics and engineering: to model the distribution of energy or particles in a system
  • Environmental science: to model the distribution of pollutants in a certain area
  • Machine learning and data science: to model and analyze patterns in data

5. Can a probability density function have a negative value?

No, a probability density function (PDF) cannot have a negative value. The PDF represents the relative likelihood of a random variable taking on a certain value, so it must always be non-negative. However, the area under the PDF curve can be greater than 1, indicating a higher probability of the variable taking on a range of values.

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