Binomial Probability Problems: Finding Probability for Glasses and DMF Teeth

In summary: Sorry about that. Yea, I changed it to .5, I guess I was trying to look too deep into the problem. Sorry about that.
  • #1
eMac
17
0
Problem 1: About 50% of all persons age 3 and older wear glasses or contact lenses. For a randomly selected group of five people find the probability that:
a. exactly three wear glasses or contact lenses
b. at least one wears them
c. at most one wears them

For this problem I set n=5, p=.25, and q(1-p)=.75

For (a) I used y=3, I set up a combination of (5 choose 3) * ((.25)^3) * ((.75)^2)

For (b) and (c) I'm confused as to what I should choose for (y).

Problem 2: If 25% of 11-year old children have no decayed, missing, or filled (DMF) teeth, find the probability that in a sample of 20 children there will be:

a. exactly 3 with no DMF teeth
b. 3 or more with no DMF teeth
c. fewer than 3 with no DMF teeth
d. exactly 5 with no DMF teeth

I set n=20, p=.25, and q(1-p)=.75

I'm not sure if I am setting up these right or not.
 
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  • #2
eMac said:
b. at least one wears them
compute 1.0 minus the probability that zero wear them.
c. at most one wears them
Add the probability that zero wear them and the probability that exactly 1 wears them.
 
  • #3
eMac:

Why are you using p=0.25, if 50% wear glasses?
 
  • #4
Bacle said:
eMac:

Why are you using p=0.25, if 50% wear glasses?

Because it said 50% of people over the age of 3. So 50% don't have it and then 50% of the 50% left don't have it, thus .25. At least I think.
 
  • #5
Stephen Tashi said:
compute 1.0 minus the probability that zero wear them.
Add the probability that zero wear them and the probability that exactly 1 wears them.

Thank you, this helped.
 
  • #6
I'm glad it helped. As Bacle pointed out, I think you should re-examine your reasoning about the using 0.25 in the first problem. Your thinking would only be correct if 50% of the population were less than 3 years old. The problem isn't phrased precisely, but it's probably best to assume none of the 5 people is less than 3 years old.
 
  • #7
Stephen Tashi said:
I'm glad it helped. As Bacle pointed out, I think you should re-examine your reasoning about the using 0.25 in the first problem. Your thinking would only be correct if 50% of the population were less than 3 years old. The problem isn't phrased precisely, but it's probably best to assume none of the 5 people is less than 3 years old.

Yea, I changed it to .5, I guess I was trying to look too deep into the problem.
 

What is Binomial Probability?

Binomial Probability is a mathematical concept that calculates the likelihood of a specific number of successes in a given number of trials, where each trial has only two possible outcomes (success or failure).

What is the formula for calculating Binomial Probability?

The formula for calculating Binomial Probability is P(x) = nCx * p^x * (1-p)^(n-x), where n is the total number of trials, x is the number of successes, and p is the probability of success in each trial.

What is the difference between Binomial Probability and Normal Probability?

Binomial Probability is used when there are only two possible outcomes in each trial, while Normal Probability is used when there are more than two possible outcomes. Additionally, Binomial Probability assumes that each trial is independent, while Normal Probability does not.

How is Binomial Probability used in real life?

Binomial Probability is commonly used in fields such as statistics, finance, and science to determine the likelihood of a specific outcome in a given number of trials. For example, it can be used to calculate the probability of a certain number of heads in a series of coin tosses or the probability of a certain number of successful drug trials in a medical study.

What are some limitations of Binomial Probability?

Binomial Probability relies on certain assumptions, such as independent trials and a fixed probability of success, which may not always hold true in real-life situations. Additionally, it can only be used for discrete data and may not be applicable to continuous data.

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