Comparing random variables with a normal distribution

In summary, the conversation discusses the calculation of the probability that seven apples, with a combined weight of X~N(1050, 2800), will weigh more than a cabbage with weight Y~N(1000, 502). The solution involves finding the distribution of -Y and using it to find the distribution of X, and then using a normal distribution chart to calculate the desired probability. The final probability is found to be 0.7549.
  • #1
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Homework Statement



You have 7 apples whose weight (in gram) is independent of each other and normally distributed, N([itex]\mu[/itex]= 150, [itex]\sigma[/itex]2 = 202).
You also have a cabbage whose weight is independent of the apples and N(1000, 502)

What is the probability that the seven apples will weigh more than the cabbage?

Homework Equations



Let X represent the weight of the seven apples combined, and Y the weight of the cabbage.
X~N(1050, 2800)
Y ~N(1000, 502)

The Attempt at a Solution



I have an easy time calculating the probability that a random variable will yield a number within a specific interval. For example I know how to get the probability that the 7 apples will weigh more that 1000g,
p(X > 1000) =
[itex]\varphi[/itex](X > (1000 - [itex]\mu[/itex]X)/[itex]\sigma[/itex]x) =
[itex]\Phi[/itex](0.94) = 0.8264, which I got from a chart for [itex]\Phi[/itex](x).

I am completely lost however on how to calculate the probability that a certain random variable will yield a bigger number that another random variable, both normally distributed but with different parameters: p(X > Y).

Thank you.
 
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  • #2
Let [itex]X_i[/itex] be the distribution of the apples. And let Y be the distribution of the cabbage.

You know that [itex]X_i\sim N(150,20^2)[/itex] and [itex]Y\sim N(1000,50^2)[/itex].

Can you find out the distribution of -Y??
Can you use this to find out the distribution of [itex]X:=X_1+X_2+X_3+X_4+X_5+X_6+X_7-Y[/itex]??
Can you use this to find [itex]P(X\geq 0)[/itex]??
 
  • #3
Thank you :)
I like how stuff is obvious when someone tells you.

-y ~ N(-1000, 502)

[itex]X[/itex]i ~ N(1050 - 1000, 2800 + 2500)

P( [itex]X[/itex][itex]\geq[/itex]0) = [itex]\Phi[/itex]([itex]\stackrel{50}{\sqrt{5300}}[/itex]) = [itex]\Phi[/itex](0.69) = 0.7549

Thanks again :D
 

1. What is a normal distribution?

A normal distribution is a probability distribution that is commonly used in statistical analysis. It is characterized by a bell-shaped curve, with most of the data falling towards the middle and tailing off towards the edges. The mean, median, and mode of a normal distribution are all equal.

2. How do you compare random variables with a normal distribution?

To compare random variables with a normal distribution, you can use statistical tests such as the Z-test or the t-test. These tests determine whether the data follows a normal distribution and if there is a significant difference between the means of two samples.

3. What is the central limit theorem and how does it relate to comparing random variables with a normal distribution?

The central limit theorem states that the sum of a large number of independent random variables will tend towards a normal distribution, regardless of the underlying distribution of the individual variables. This is why the normal distribution is often used as a reference for comparing random variables.

4. Can a random variable have a normal distribution?

Yes, a random variable can have a normal distribution. In fact, many real-world phenomena can be approximated by a normal distribution, making it a useful tool for statistical analysis.

5. What are some limitations of comparing random variables with a normal distribution?

One limitation is that the normal distribution assumes that the data is continuous and symmetrical, which may not always be the case. Additionally, if the data does not follow a normal distribution, the results of the comparison may not be accurate.

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