Relating probability distributions huh?

In summary, probability distributions are mathematical functions used to describe the likelihood of an outcome or event occurring. They can be related in various ways and there are several common types, including the normal, binomial, Poisson, and exponential distributions. In research, they are used for data analysis, predictions, and testing hypotheses. Additionally, probability distributions can be applied to real-world situations in fields such as finance and social sciences.
  • #1
Dafydd
12
0

Homework Statement



Problem description:
A variable X has expected value 0.002 in meters. Consider X - 0.002, scale to millimeter, and we get Y.

Tasks:
a) Express Y as a function of X
b) Relate the probability distributions FX and FY
c) Relate the probability density functions fX and fY

Homework Equations



[tex] F(x) = \operatorname P ( X \leq x ) = \int_{-\infty}^x f(t) \, \mathrm{d}t [/tex]

[tex] \int_{-\infty}^{\infty} f(x) \, \mathrm{d}x = 1[/tex]

[tex] f(x) = F'(x) \geq 0 [/tex]

The Attempt at a Solution



a) Y = 1000X - 2
(I think)

b) I have no clue. I mean, I could make a wild guess, but I don't see any reason to.

c) I suppose we get fX and fY by differentiating FX and FY... somehow.

I also don't really know what it means to "relate" these things. Is it to express the one in terms of the other, so that for example if a*b = c then relating a to b I either say that a = c/b or that b = c/a? Or what?
 
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  • #2
Dafydd said:

Homework Statement



Problem description:
A variable X has expected value 0.002 in meters. Consider X - 0.002, scale to millimeter, and we get Y.

Tasks:
a) Express Y as a function of X
b) Relate the probability distributions FX and FY
c) Relate the probability density functions fX and fY

Homework Equations



[tex] F(x) = \operatorname P ( X \leq x ) = \int_{-\infty}^x f(t) \, \mathrm{d}t [/tex]

[tex] \int_{-\infty}^{\infty} f(x) \, \mathrm{d}x = 1[/tex]

[tex] f(x) = F'(x) \geq 0 [/tex]

The Attempt at a Solution



a) Y = 1000X - 2
(I think)

That looks good.

b) I have no clue. I mean, I could make a wild guess, but I don't see any reason to.

c) I suppose we get fX and fY by differentiating FX and FY... somehow.

Start by looking at the cumulative distribution function for Y:

[tex]G(y) = P(Y\le y) = P(1000 X - 2 \le y) = P(X \le\ ??)\ ...[/tex]

You should be able to calculate this in terms of fX and it's derivative with respect to y will give you the density function for y.
 

Related to Relating probability distributions huh?

1. What are probability distributions?

Probability distributions are mathematical functions that describe the likelihood of a certain outcome or event occurring. They are used to model and analyze data in various fields, such as statistics, economics, and physics.

2. How are probability distributions related?

Probability distributions can be related in various ways, such as by being derived from each other, having similar properties, or being used to model similar types of data. For example, the normal distribution can be derived from the binomial distribution, and both are commonly used to model continuous data.

3. What are some common types of probability distributions?

Some common types of probability distributions include the normal distribution, binomial distribution, Poisson distribution, and exponential distribution. Each of these distributions has its own unique properties and is used to model different types of data.

4. How are probability distributions used in research?

In research, probability distributions are used to model and analyze data, make predictions, and test hypotheses. They are also used to estimate the likelihood of a certain outcome or event occurring, and to measure the uncertainty or variability of data.

5. Can probability distributions be applied to real-world situations?

Yes, probability distributions can be applied to real-world situations and are commonly used in fields such as finance, engineering, and social sciences. For example, the normal distribution is often used to model stock prices, while the Poisson distribution is used to model the number of customer arrivals in a store.

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