Binomial probability, similar to lottery problems.

In summary, the problem involves drawing 5 counters from a bag containing 10 green counters and 20 red counters without replacement. Each green counter scores one point and each red counter scores zero points. The task is to find the probability of scoring 0, 1, 2, 3, 4, or 5 points. The solution involves determining the number of ways to draw a certain number of green counters and dividing it by the total number of ways to draw 5 counters from a total of 30.
  • #1
alexburns1991
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0

Homework Statement


An opaque bag contains 10 green counters, and 20 red ones. One counter is drawn at random and not replaced: green scores one, red scores zero. Five counters are drawn.

Find the probability of scoring 0, 1, 2, 3, 4, 5 points.


Homework Equations





The Attempt at a Solution


I found it pretty straighforward to work out with replacement, as it is just the simple binomial probability. But when the counters aren't replaced, surely the order counts, so i tried replacing nCr with nPr though this gave me the completely wrong answer. i know this resembles the lottery problem, but i don't understand what to do when there are only 2 distinguishable groups.
 
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  • #2
If you score n points, that means you drew n green and 5-n red counters. What you want to do is figure out the number of ways you can do that and divide by the total number of ways you can draw five items from thirty.
 
  • #3


The binomial probability formula can still be used in this situation, even without replacement. The only difference is that the probability of success (drawing a green counter) will change with each draw, as there are fewer counters in the bag after each draw.

To find the probability of scoring 0 points, you would use the formula P(0) = (20/30) * (19/29) * (18/28) * (17/27) * (16/26) = 0.2334. This is because with each draw, the probability of drawing a red counter increases slightly.

Similarly, to find the probability of scoring 1 point, you would use the formula P(1) = (10/30) * (20/29) * (19/28) * (18/27) * (17/26) = 0.4017. And for 2 points, P(2) = (10/30) * (9/29) * (20/28) * (19/27) * (18/26) = 0.2527.

The rest of the probabilities can be calculated in the same way. The key is to adjust the probability of success for each draw, based on the number of counters left in the bag.
 

FAQ: Binomial probability, similar to lottery problems.

What is binomial probability?

Binomial probability is a mathematical concept that calculates the likelihood of a specific outcome occurring in a given number of trials, where each trial has a binary outcome (either success or failure). It is often used in situations where there are only two possible outcomes, such as in lottery problems.

How is binomial probability different from other types of probability?

Binomial probability is different from other types of probability because it specifically deals with a fixed number of trials, each with a binary outcome. Other types of probability, such as normal distribution or Poisson distribution, can deal with a range of outcomes and are not limited to just two possible outcomes.

How do you calculate binomial probability?

To calculate binomial probability, you need to know the number of trials, the probability of success in each trial, and the number of successes desired. The formula for binomial probability is P(x) = (n choose x) * 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.

Can binomial probability be used to predict lottery outcomes?

No, binomial probability cannot be used to predict lottery outcomes. This is because lottery outcomes are usually random and do not follow a specific pattern or fixed number of trials. Binomial probability can only be used in situations where the number of trials and probability of success are known.

In what other real-life situations can binomial probability be applied?

Binomial probability can be applied to many real-life situations, such as predicting the success of a marketing campaign, predicting the likelihood of a drug trial succeeding, or predicting the probability of a sports team winning a game. It can also be used in genetics to determine the probability of certain traits being passed down from parents to offspring.

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