Prob and stats continuous random variable question

In summary: ie, the probability of at least 4 radios lasting more than 15 years is \sum^{8}_{n=4}\binom{8}{n}(0.368)^n(0.632)^{8-n}\ =\ .3327
  • #1
Proggy99
51
0

Homework Statement


Let X denote the lifetime of a radio, in years, manufactured by a certain company. The density function of X is given by

[tex]f(x)=\left\{\stackrel{\frac{1}{15}e^\frac{-x}{15}\ \ \ \ if\ 0\ \leq\ x\ <\ \infty}{0\\\\elsewhere}[/tex]

What is the probability that, of eight such radios, at least four last more than 15 years?


Homework Equations




The Attempt at a Solution


P(x[tex]\geq15[/tex]) = F(15) = [tex]\frac{1}{15}\int^{\infty}_{15}e^\frac{-x}{15}dx[/tex] = [tex]-\left[e^\frac{-x}{15}\right]^{\infty}_{15}[/tex]
=[tex]-\left[e^{-\infty}\ -\ e^{-1}\right][/tex] = .368

So each radio has a .368 chance of lasting more than 15 years

Next I used a Poisson variable equation using t = 8 and .368

[tex]\lambda[/tex] = 8 * .368 = 2.944

therefore [tex]\frac{e^{-2.944}2.944^{n}}{n!}[/tex]

to find the probability of at least 4 of 8 radios
[tex]\sum^{8}_{n=4}\frac{e^{-2.944}2.944^{n}}{n!}\ =\ .3366[/tex]

Now the book answer says .3327 and I have pretty much ruled out rounding differences. That is pretty close, but this is the first time the book and my answer have not been pretty much the same. Can anyone see anything I am doing wrong in my above solution? Any help would be greatly appreciated. Thanks
 
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  • #2
Proggy99 said:
So each radio has a .368 chance of lasting more than 15 years

Next I used a Poisson variable equation using t = 8 and .368

[tex]\lambda[/tex] = 8 * .368 = 2.944

therefore [tex]\frac{e^{-2.944}2.944^{n}}{n!}[/tex]

to find the probability of at least 4 of 8 radios
[tex]\sum^{8}_{n=4}\frac{e^{-2.944}2.944^{n}}{n!}\ =\ .3366[/tex]

Hi Proggy99! :smile:

not sure what a Poisson variable equation is :redface:

but the radios are independent, so can't you just use ordinary combinatorial theory, with .368n?
 

1. What is a continuous random variable?

A continuous random variable is a type of variable in statistics that can take on any value within a certain range. This is different from a discrete random variable, which can only take on specific, separate values.

2. How is a probability distribution calculated for a continuous random variable?

The probability distribution for a continuous random variable is calculated by determining the area under the curve of a probability density function. This function shows the relative likelihood of different values occurring for the variable.

3. Are there any assumptions that need to be made when working with continuous random variables?

Yes, there are a few assumptions that need to be made when working with continuous random variables. One is that the variable is normally distributed, meaning the data falls into a bell-shaped curve. Another is that the variable is independent, meaning it is not affected by other variables in the data set.

4. How is the mean and standard deviation calculated for a continuous random variable?

The mean for a continuous random variable can be calculated by multiplying each possible value by its probability and adding all of the products together. The standard deviation can be calculated by taking the square root of the variance, which is found by subtracting the mean from each value, squaring the differences, and taking the average of those squared differences.

5. What is the central limit theorem and how does it relate to continuous random variables?

The central limit theorem states that for a large enough sample size, the distribution of sample means will be approximately normal, regardless of the distribution of the underlying population. This means that even if the individual values of a continuous random variable are not normally distributed, the mean of a large enough sample will be. This allows for certain statistical tests to be performed on the data with more accuracy.

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