Easy discrete probability problems

In summary, the conversation discusses probability calculations for selecting microprocessors from a lot of 100, where 10 are defective. The correct probability for obtaining no defective microprocessors is 90C4/100C4, and for obtaining at most one defective microprocessor is the sum of the probability for getting none and the probability for getting one, which is 90C4/100C4 + 10C1*90C3/100C4. The book's answer for the first question is incorrect.~~~
  • #1
Townsend
232
0
And when I say easy I mean easy for a lot of you but not necessarily for most people.

1) Four microprocessors are randomly selected from a lot of 100 microprocessors among which 10 are defective. Find the probability of obtaining no defective microprocessors.

The answer the book gives is [tex] \frac{_{90} C _{10}}{_{100} C _{10}}[/tex], which I think is wrong and it does not really make much sense. I think the correct answer is [tex] \frac{_{90}C_{4}}{_{100}C_{4}}[/tex].

2) Four microprocessors are randomly selected from a lot of 100 microprocessors among which 10 are defective. Find the probability of obtaining at most one defective microprocessor.

Well our sample space is still [tex]_{100}C_4[/tex] but the number of outcomes is different. If it was to be exactly one defective microprocessor then the number of outcomes would be [tex]_{10}C_1[/tex], the number of ways to select a defective microprocessor, times [tex]_{90}C_3[/tex], the number of ways to pick three nondefective processors. But when we say "at most one" we mean to say one or none. So can we just add the propability of getting none to the propability of getting one?
 
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  • #2
Townsend said:
And when I say easy I mean easy for a lot of you but not necessarily for most people.

1) Four microprocessors are randomly selected from a lot of 100 microprocessors among which 10 are defective. Find the probability of obtaining no defective microprocessors.

The answer the book gives is [tex] \frac{_{90} C _{10}}{_{100} C _{10}}[/tex], which I think is wrong and it does not really make much sense. I think the correct answer is [tex] \frac{_{90}C_{4}}{_{100}C_{4}}[/tex].

2) Four microprocessors are randomly selected from a lot of 100 microprocessors among which 10 are defective. Find the probability of obtaining at most one defective microprocessor.

Well our sample space is still [tex]_{100}C_4[/tex] but the number of outcomes is different. If it was to be exactly one defective microprocessor then the number of outcomes would be [tex]_{10}C_1[/tex], the number of ways to select a defective microprocessor, times [tex]_{90}C_3[/tex], the number of ways to pick three nondefective processors. But when we say "at most one" we mean to say one or none. So can we just add the propability of getting none to the propability of getting one?
CORRECT for both your solutions. (Book is wrong on #1.)


~~
 
  • #3


1) The answer given in the book is actually correct. The formula used is for the probability of selecting a specific number of objects from a larger set, without replacement. In this case, we are looking for the probability of selecting 4 non-defective microprocessors from a lot of 100, which is represented by 90C4/100C4. This is because the number of ways to select 4 non-defective microprocessors is the same as the number of ways to select 4 objects from a set of 90 (the non-defective microprocessors).

2) The correct approach for this problem would be to calculate the probability of selecting exactly one defective microprocessor and adding it to the probability of selecting none. So the final answer would be:

P(at most one defective) = P(exactly one defective) + P(none defective)
= (10C1 * 90C3)/100C4 + (90C4)/100C4
= (10 * 11780)/3921225 + 118755/3921225
= 0.3114 + 0.0303
= 0.3417

Therefore, the probability of selecting at most one defective microprocessor is 0.3417.
 

FAQ: Easy discrete probability problems

What is discrete probability?

Discrete probability is a branch of mathematics that deals with the likelihood of events that have a finite or countable number of outcomes. It is used to analyze and predict the occurrence of events based on the probability of their outcomes.

What are some examples of discrete probability problems?

Examples of discrete probability problems include coin tosses, rolling dice, and drawing cards from a deck. Each of these situations has a finite number of outcomes and can be analyzed using discrete probability.

How is discrete probability different from continuous probability?

Discrete probability deals with events that have a finite or countable number of outcomes, while continuous probability deals with events that have an infinite number of possible outcomes. For example, the probability of rolling a specific number on a die is an example of discrete probability, while the probability of landing on a specific point on a dartboard is an example of continuous probability.

What is the difference between probability and odds?

Probability and odds are two ways of expressing the likelihood of an event occurring. Probability is the measure of the likelihood of an event occurring, while odds is the ratio of the probability of an event occurring to the probability of it not occurring. For example, the probability of rolling a 4 on a die is 1/6, while the odds of rolling a 4 are 1:5.

How can discrete probability be used in real-life situations?

Discrete probability can be used in various real-life situations, such as predicting the outcome of a sporting event, analyzing the results of a survey, or determining the probability of winning a game of chance. It can also be used in decision-making processes, such as in finance and economics, to evaluate risks and make informed choices.

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