Continuous random variable - transformation using sin

In summary, the conversation discusses the distribution of a random variable Y, which represents the length of an image observed on a photograph. The length of the pin in the photograph is represented by a random variable X, which is at an angle to the normal to the film. The conversation then focuses on determining the distribution of Y, given that all angles X are equally likely. It is found that the distribution of Y is (2/pi)(1/4)(1/sqrt(1-y^2/4)) for values between 0 and 4, and 0 otherwise. The conversation also mentions determining the expected value of Y, but does not provide a solution for this.
  • #1
Kate2010
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0

Homework Statement



There is a pin of length 4 which appear on a photograph, and the length of the image observed is y, an observation on the random variable Y. The pin is at an angle x, 0[tex]\leq[/tex]x[tex]\leq[/tex][tex]\pi[/tex]/2, to the normal to the film, this is an observation on the r.v. X.

1. If all angles X are equally likely then derive the distribution of Y.

2. What is E(Y)?

Homework Equations





The Attempt at a Solution



X is uniform so fX(x) = 2/[tex]\pi[/tex] , 0[tex]\leq[/tex]x[tex]\leq[/tex][tex]\pi[/tex]/2 and fX (x) = 0 otherwise.

So FX (x) = 0, x<0
= 2x/[tex]\pi[/tex], 0[tex]\leq[/tex]x[tex]\leq[/tex][tex]\pi[/tex]/2
=1, x>[tex]\pi[/tex]/2

Y=4SinX

FY(y)= P(Y[tex]\leq[/tex]y)
=P(4sinX [tex]\leq[/tex]y)
=P([tex]\pi[/tex] - arcsin(y/4) [tex]\leq[/tex] X [tex]\leq[/tex] 2[tex]\pi[/tex] - arcsin(y/4)) (*)
= FX (2[tex]\pi[/tex] - arcsin(y/4)) - FX ([tex]\pi[/tex] - arcsin(y/4)
= 2 + (2/[tex]\pi[/tex])arcsin(y/4)

So fY(y) = (2/[tex]\pi[/tex])(1/4)(1/[tex]\sqrt{1-y2 /4}[/tex]) for 0[tex]\leq[/tex]y[tex]\leq[/tex]4 and 0 otherwise.

Is this ok so far? I'm very unsure of the stage marked by (*).
 
Last edited:
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  • #2
E(Y)=\int_{0}^{4}yfY(y)dy=\int_{0}^{4}y(2/\pi)(1/4)(1/\sqrt{1-y2 /4}) dyI'm not sure how to do this integral, so any help is greatly appreciated!
 

1. What is a continuous random variable?

A continuous random variable is a type of variable in statistics that can take on an infinite number of possible values within a given range. It is typically represented by a function that can be measured and analyzed using mathematical tools.

2. How is a continuous random variable transformed using sin?

To transform a continuous random variable using sin, the original variable is first multiplied by a constant (usually a frequency), and then the sine function is applied to the result. This allows for the creation of a new distribution with a different shape and characteristics.

3. What are the benefits of transforming a continuous random variable using sin?

Transforming a continuous random variable using sin can help to normalize skewed data, improve the fit of a model, and better understand the relationship between variables. It can also make the data more suitable for certain statistical analyses.

4. Are there any limitations to using sin transformation on continuous random variables?

While transforming a continuous random variable using sin can have many benefits, it is important to note that it may not always be appropriate for all data sets. For example, if the data is already normally distributed, the transformation may not have any significant impact. Additionally, the results of the transformation may be difficult to interpret in some cases.

5. How does the frequency used in sin transformation affect the resulting distribution?

The frequency used in sin transformation can greatly impact the resulting distribution. A higher frequency can create a more pronounced transformation, resulting in a distribution that is more different from the original. Conversely, a lower frequency may have a more subtle effect on the distribution. It is important to carefully consider the appropriate frequency to use based on the goals of the analysis.

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