Are Functions of Independent Random Variables Always Independent?

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In summary, the problem asks for a random variable F that is a function of both X and Y, which have normal distributions with parameters (5,25) and (2,16) respectively. The goal is for F to have a F-distribution with parameters (1,2). The attempt at a solution involves using the definition of the F-distribution and finding a suitable function involving X and Y. However, it is unsure if the chosen functions for X and Y are independent, so further investigation is needed.
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Homework Statement


Suppose the random variable X has a N(5,25) dsitribution and Y has a N(2,16) distribution and that X and Y are independent. Find a random variable F that is a function of both X and Y such that F has a F-distribution with parameters (1,2), i.e. F(1,2).


Homework Equations


Definition: If X~chi square(n), Y~chi square(m), and X and Y are independent, then (X/n)/(Y/m)~F(n,m)


The Attempt at a Solution


Does F=[(X-5)/5]^2 / {([(X-5)/5]^2 + [(Y-2)/4]^2])/2} work?
The only trouble I am seeing is that (X-5)/5]^2 and [(X-5)/5]^2 + (Y-2)/4]^2] might not be independent. So are they independent? If so, how can I prove it? If not, what else can I do?


Any stat guy here?
I appreciate for any help!
 
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  • #2
In other words, we know that if X and Y are independent, then g(X) and h(Y) are independent, but are a function of X (f1(X)) and a function of X and Y (f2(X,Y)always independent?
 

1. What is the F-distribution?

The F-distribution, also known as the Fisher-Snedecor distribution, is a probability distribution that is used to test the hypothesis that the variances of two populations are equal. It is a continuous probability distribution that is right-skewed, and its shape depends on the degrees of freedom of the two populations being compared.

2. How is the F-distribution different from other probability distributions?

The F-distribution is different from other probability distributions, such as the normal or binomial distribution, because it is used specifically for comparing the variances of two populations. Other distributions may be used for different purposes, such as testing means or proportions.

3. What is the relationship between the F-distribution and ANOVA?

The F-distribution is closely related to ANOVA (Analysis of Variance), which is a statistical test used to compare the means of three or more groups. In ANOVA, the F-statistic is calculated by dividing the between-group variability by the within-group variability, and this F-statistic follows the F-distribution.

4. How is the F-distribution used in hypothesis testing?

The F-distribution is commonly used in hypothesis testing to determine whether there is a significant difference in the variances of two populations. This is typically done by calculating the F-statistic and comparing it to a critical value from the F-distribution. If the calculated F-statistic is greater than the critical value, it can be concluded that the variances are significantly different.

5. Can the F-distribution be used for any sample size?

The F-distribution can be used for any sample size, as long as the underlying populations follow a normal distribution. However, it is more commonly used for larger sample sizes, as the F-distribution becomes more symmetric and similar to the normal distribution as the sample size increases.

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