Calculating Probability for Non-consecutive Lockers in a Discrete Model

In summary: There are 9 ways to place the 1's so no two are consecutive. There are 10 ways to place the 1's so two are consecutive. So the probability that no two chosen lockers are consecutive is 10-9 or .09.
  • #1
Abstract3000
1
0
Hello,
I have a question I am trying to figure out how it works and I am so confused I need a break down of what is exactly going on with this problem

the Question.
"Concern three persons who each randomly choose a locker among 12 consecutive lockers"

What is the probability that no two lockers are consecutive?

The answer given that confuses me even more:
-----------------------------------------------------------------------
1st to find ways exactly 2 lockers are consecutive

X X _ _ _ _ _ _ _ _ _ _ 9 Ways
_ X X _ _ _ _ _ _ _ _ _ 8 Ways
_ _ X X _ _ _ _ _ _ _ _ 8 Ways

5 others w/8

_ _ _ _ _ _ _ _ X X _ _ 8
_ _ _ _ _ _ _ _ _ X X _ 8
_ _ _ _ _ _ _ _ _ _ X X 9

# = 9X2 + 9X8 = 90
Next # Ways w/3 consecutive = 10 start in 1,2,...,10

== P(no 2 consec) = 1 - 100/C(12,3) ~ .545 = 5.45X10^10-1
--------------------------------------------------------------------------

This is the teachers solution and I have absolutely no idea on how he got all the number he did I am really lost (on all of it, the solution makes no sense in what he has written down I don't get where he gets the 8's the 9's or the 10 from), anyone understand this and have the time to break it down for me?

Thanks!
 
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  • #2
Re: Discrete Probability Quetion

Abstract3000 said:
"Concern three persons who each randomly choose a locker among 12 consecutive lockers"
What is the probability that no two lockers are consecutive?

This is the teachers solution and I have absolutely no idea on how he got all the number he did I am really lost (on all of it, the solution makes no sense in what he has written down I don't get where he gets the 8's the 9's or the 10 from), anyone understand this and have the time to break it down for me?

I do not follow that solution either. But here is a model.
Think of a string [tex]111000000000[/tex], the 1's represent the chosen lockers and the 0's empty.
[tex]100100000100[/tex] in that model no two chosen lockers are consecutive.
But in [tex]001000001100[/tex] in that model two chosen lockers are consecutive.

So how many ways can we rearrange the string [tex]111000000000[/tex] so no two 1's are consecutive?

[tex]\_\_0\_\_0\_\_0\_\_0\_\_0\_\_0\_\_0\_\_0\_\_0\_\_[/tex] Note that the nine 0's create ten places that we can place the 1's so no two are consecutive.

Here is the caculation.
 
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FAQ: Calculating Probability for Non-consecutive Lockers in a Discrete Model

1. What is discrete probability?

Discrete probability is a branch of mathematics that deals with the study of random events that have a finite or countably infinite number of possible outcomes. It involves calculating the chance of a specific outcome occurring in a given situation.

2. What is the difference between discrete and continuous probability?

Discrete probability deals with events that have a finite or countably infinite number of possible outcomes, whereas continuous probability deals with events that have an infinite number of possible outcomes. In other words, discrete probability deals with situations where the outcome can be counted, while continuous probability deals with situations where the outcome is measured.

3. How do you calculate the probability of an event in discrete probability?

The probability of an event in discrete probability is calculated by dividing the number of favorable outcomes by the total number of possible outcomes. This can be represented as P(event) = favorable outcomes / total outcomes.

4. What are some examples of discrete probability problems?

Some examples of discrete probability problems include tossing a coin, rolling a die, drawing cards from a deck, and choosing a random number from a set of numbers.

5. How is discrete probability used in real life?

Discrete probability is used in various fields such as statistics, finance, computer science, and engineering. It is used to make predictions, analyze data, and make decisions based on probabilities. Some real-life examples include predicting stock market trends, analyzing customer behavior, and designing algorithms for data encryption.

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