Why Does Calculating P(|Y-5| >= 3) Involve Y=7 in a Binomial Distribution?

In summary, we are given a binomial distribution with n=11 and p=0.3 and we need to find the probability that |Y-5| >= 3. After trying two different approaches, it is determined that the correct answer is 0.3170, with the key being to consider the probability that Y > 8 rather than Y >= 8.
  • #1
Bkid701
2
0
IF Y~B(11, 0.3), find (|Y-5| >= 3)

I got the answer(0.3170) but i don't understand the logic behind this part where i am confused.

can someone explain the working(second working) where i somehow got it blindly correct?


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my working at first:

|Y-5| >= 3
Y >= 8
Y <=2

so P( 2 >= Y >= 8)

P(Y=8) = 0.9994
1 - 0.9994 = 0.0006

P(Y=2) = 0.3127

so P( 2 >= Y >= 8) = 0.3133

However this answer is wrong
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My second working:

my working at first:

|Y-5| >= 3
Y >= 8
Y <=2

so P( 2 >= Y >= 8)

P(Y=7) = 0.9957
1 - 0.9957 = 0.0043

P(Y=2) = 0.3127

so P( 2 >= Y >= 8) = 0.3170

This is the correct working but i don't understand why Y is = 7...
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  • #2
Bkid701 said:
|Y-5| >= 3
Y >= 8
Y <=2

so P( 2 >= Y >= 8)
Much clearer to write P( 2 >= Y | Y >= 8)
P(Y=8) = 0.9994
That's the probability that Y <= 8. If you subtract that from 1 you'll have the probability that Y > 8.
 
  • #3
Thank you for your help. I'm getting the idea of it now.

Cheers
 

FAQ: Why Does Calculating P(|Y-5| >= 3) Involve Y=7 in a Binomial Distribution?

What is binomial distribution?

Binomial distribution is a probability distribution that describes the likelihood of a certain number of successes in a fixed number of independent trials with two possible outcomes: success or failure.

How is binomial distribution different from other probability distributions?

Unlike other probability distributions, binomial distribution deals with discrete data and is characterized by a fixed number of trials, each with a binary outcome (success or failure). Other distributions, such as normal distribution, involve continuous data and can have an infinite number of possible outcomes.

What are the formula and parameters of binomial distribution?

The formula for binomial distribution is P(x) = nCx * p^x * (1-p)^(n-x), where n is the number of trials, x is the number of successes, and p is the probability of success in each trial. The parameters of binomial distribution are n and p.

How is binomial distribution used in real life?

Binomial distribution is used in various fields such as statistics, genetics, and finance to model and analyze data that involves a fixed number of trials with two possible outcomes. For example, it can be used to predict the success rate of a new drug, the number of defective products in a batch, or the outcome of a sports game.

Can binomial distribution be approximated by other distributions?

Yes, binomial distribution can be approximated by other distributions such as normal distribution when the number of trials is large and the probability of success is close to 0.5. This is known as the central limit theorem and is often used in statistical analysis.

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