A straight forward question on variances

  • Thread starter michonamona
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In summary, the variance of w, where w=a+b(e+z) and z is a random variable distributed by some density function g(z), is equal to b^2 * Var(z). This is because the variance of a constant plus a random variable is equal to the variance of the random variable, and the variance of a constant times a random variable is equal to the constant squared times the variance of the random variable. Using these properties and given information, the variance of w can be calculated as b^2 * Var(z) = b^2 * sigma^2.
  • #1
michonamona
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Homework Statement



suppose you have the following function:

w=a+b(e+z)

a, b and e are constants and z is a random variable distributed by some density function g(z).

What is the variance of w?

i.e. var(w)

Homework Equations



Suppose E(z) = 0 (expectation of z is 0) and var(z)=[tex]\sigma^{2}[/tex]

The Attempt at a Solution



The solution is var(w)= [tex]b^{2} \sigma^{2}[/tex], but I don't understand why. I appreciate your input.

M
 
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  • #2
Two facts. First, the variance of a constant plus a random variable is equal to the variance of the random variable. This makes sense if you think of variance as a measure of spread, since adding a constant to every observation doesn't change the spread. Second, the variance of a constant times a random variable is equal to the constant squared times the variance of the random variable. That is, if x is your random variable, var(ax) = a2var(x). Do you understand how the answer follows?
 
  • #3
michonamona said:

Homework Statement



suppose you have the following function:

w=a+b(e+z)

a, b and e are constants and z is a random variable distributed by some density function g(z).

What is the variance of w?

i.e. var(w)

Homework Equations



Suppose E(z) = 0 (expectation of z is 0) and var(z)=[tex]\sigma^{2}[/tex]

The Attempt at a Solution



The solution is var(w)= [tex]b^{2} \sigma^{2}[/tex], but I don't understand why. I appreciate your input.

M
Let's use caps for random variables to help keep them separate from constants.
From the given information,
E(Z) = 0 and Var(Z) = [itex]\sigma^2[/itex]

Also, by definition, Var(Z) = E(Z2) - [itex]\mu^2[/itex].
Since E(Z) = [itex]\mu[/itex] = 0, then Var(Z) = E(Z2).

You're also given that W = a + b(Z + e). Using the properties of expectation, it can be seen that E(W) = a + bE(Z) + be = a + be, since E(Z) = 0.

With all that out of the way, we can tackle Var(W).

Var(W) = E(W2) - (E(W))2.

If you replace W with a + b(Z + e) in the first term on the right, and work things through, you get the result you're supposed to get.
 
  • #4
If X is a random variable and [itex] Y = c + dX [/itex], then

[tex]
Vary(Y) = d^2 Var(X)
[/tex]
 
  • #5
Thanks guys! I appreciate your help.
 

FAQ: A straight forward question on variances

1. What is a variance in statistics?

A variance is a measure of how spread out a set of data points are. It is calculated by taking the average of the squared differences between each data point and the mean of the data set.

2. How is a variance different from a standard deviation?

A variance is the squared value of the standard deviation. While the variance gives an idea of the spread of the data, the standard deviation is a more commonly used measure as it is in the same units as the original data.

3. Why is it important to calculate variances?

Calculating variances allows us to understand the spread or variability of a data set. It can help identify outliers or unusual patterns in the data and can also be used to make comparisons between different data sets.

4. What are some common uses of variances in research?

Variances are commonly used in hypothesis testing, where they are compared to a known value to determine if there is a significant difference between two groups or conditions. They are also used in quality control to monitor the consistency of a process or product.

5. How can variances be reduced in a data set?

Variances can be reduced by increasing the sample size, as this will decrease the effect of outliers. Additionally, ensuring the data is accurately measured and recorded can also help reduce variances. Normalizing or transforming the data can also help reduce variances in certain cases.

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