Constant times a random variable question

In summary, The distribution of a randomly variable X may change when multiplied by a constant a, but it will still belong to the same family of distributions as X up to a scaling factor. This can be proven using moment generating functions, where the parameters of the distribution will be affected by the scaling and translation of the random variable.
  • #1
jimmy1
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A random variable X follows a certain distribution. Now say I multiply the random variable X by a constant a. Does the new random variable aX follow the same distribution as X?
 
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  • #2
Well, let's take it through the basics. If X ~ FX, then FX(x) = Prob{X < x} for x < X < [itex]\bar x[/itex]. Let Y = aX for ax < Y < a[itex]\bar x[/itex]; then FY(y) = Prob{Y < y} = Prob{aX < y} = Prob{X < y/a} = FX(y/a). Therefore FY(y) = FX(y/a) for ax < Y < a[itex]\bar x[/itex].

Is FY the identical distribution as FX? No. Does it belong to the same family as FX? Yes, up to a scaling factor.
 
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  • #3
Note: in the previous post I assumed a > 0. If a < 0, then FY may not even belong to the same family as FX.

The case of a = 0 is trivial, but you should bear that in mind, too.
 
  • #4
we know that if X is normally distributed, then so cX for any nonzero real number c.
also X + d is normally distributed, for any real number d.
can anyone please show me the proof?thanks
 
  • #5
jimmy1 said:
A random variable X follows a certain distribution. Now say I multiply the random variable X by a constant a. Does the new random variable aX follow the same distribution as X?

If you want a systematic way to figure this out, use moment generating functions and if the structure of the mgf is the same as the unscaled distributions mgf, then you know that the distribution doesn't change and you can see how the parameters of the distribution have changed.
 
  • #6
mheena said:
we know that if X is normally distributed, then so cX for any nonzero real number c.
also X + d is normally distributed, for any real number d.
can anyone please show me the proof?thanks

Like I said to the previous poster, use moment generating functions. Let U = cX + d. Use E[e^(tU)] be the mgf of U and you will find that this will give the mgf of a normal distribution with different parameters which will tell you what scaling and translating a normal does to its parameters. (Translating adds to the mean, scaling changes the variance by multiplying it by c^2).
 

1. What is a "constant times a random variable"?

A constant times a random variable is a mathematical expression involving a fixed number and a variable that takes on different numerical values with a certain probability. The resulting value of the expression will also be a random variable.

2. How is a constant times a random variable used in science?

In science, a constant times a random variable is often used to model real-world phenomena that involve random variation. It allows scientists to quantify the uncertainty or randomness in their data and make predictions based on probability.

3. What is the expected value of a constant times a random variable?

The expected value of a constant times a random variable is equal to the constant multiplied by the expected value of the random variable. This can be calculated by multiplying each possible value of the random variable by its corresponding probability and summing them together.

4. Can a constant times a random variable have a negative value?

Yes, a constant times a random variable can have a negative value. The resulting value of the expression will depend on the value of the random variable, which can take on both positive and negative values.

5. What is the difference between a constant times a random variable and a random variable times a constant?

The difference between a constant times a random variable and a random variable times a constant is the order in which the constant and variable are multiplied. In terms of expected value, the two expressions will be equal. However, the resulting random variable may have different probability distributions, depending on the specific values of the constant and the random variable.

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