Non 1-1 transformation of continuous random variable

In summary, the question asks to find the probability of sin(X) being greater than 1/2, where X is exponentially distributed with mean s. The solution involves using the given equations for the pdf and cdf of X, and substituting X with arcsin(y) and \pi - arcsin(y) to find the probability. The next step would be to differentiate the cdf to find the pdf and use it to find the desired probability.
  • #1
Kate2010
146
0

Homework Statement



X is exponentially distributed with mean s.
Find P(Sin(X)> 1/2)

Homework Equations



fX(x) = se-sx, x[tex]\geq[/tex] 0
0, otherwise

FX(x) = 1 - e-sx, x[tex]\geq[/tex] 0
0 otherwise

The Attempt at a Solution



Let Y = sin X

FY (y) = P(Y[tex]\leq[/tex] y)
= P(sinX [tex]\leq[/tex] Y)
= P(X [tex]\leq[/tex] arcsin(y), X[tex]\geq[/tex] [tex]\pi[/tex] - arcsin(y)) {This is where I become slightly unsure}
=FX(arcsin(y)) - FX([tex]\pi[/tex] - arcsin(y))
=1-e-s(arcsin(y)) - (1-e-s([tex]\pi[/tex] - arcsin(y)))

From here I can differentiate to find the pdf and then use that to find sinX < 1/2.
 
Physics news on Phys.org
  • #2
Any ideas anyone?
 

FAQ: Non 1-1 transformation of continuous random variable

What is a non 1-1 transformation of a continuous random variable?

A non 1-1 transformation of a continuous random variable is a mathematical function that maps one continuous random variable to another, but is not a one-to-one mapping. This means that multiple values of the original variable can be mapped to the same value of the transformed variable.

Why would you use a non 1-1 transformation of a continuous random variable?

A non 1-1 transformation can be useful in situations where the original variable has a skewed or non-normal distribution, but the transformed variable has a more symmetric distribution. This can make it easier to apply statistical tests or make assumptions about the data.

How does a non 1-1 transformation affect the shape of a distribution?

A non 1-1 transformation can change the shape of a distribution in various ways, depending on the specific mathematical function used. Some transformations may make the distribution more symmetric, while others may make it more skewed. In general, the transformation can change the location, spread, and shape of the distribution.

Are there any limitations to using a non 1-1 transformation of a continuous random variable?

Yes, there are some limitations to using a non 1-1 transformation. If the transformed variable is not normally distributed, it may still violate assumptions of certain statistical tests. Additionally, the interpretation of the transformed variable may be more complex and less intuitive than the original variable.

What are some common examples of non 1-1 transformations of continuous random variables?

Some common examples of non 1-1 transformations include taking the logarithm, square root, or inverse of a variable. Other examples include power transformations (e.g. squaring, cubing) or trigonometric transformations (e.g. sine, cosine).

Back
Top