Predicting Absenteeism: Comparing Binomial Distribution in Two Classes

In summary, the probability that the large class will have more absent students than the small class is given by the following equation:P = (1-0.3*0.3)^N
  • #1
aaaa202
1,169
2

Homework Statement


In a class with 20 and one with 10 students each student has a probability of 0.3 to not show up on a particular day. On a given day, which class is most likely to have the highest ratio of absent students? This was in my exam, unfortunately I did not know how to do it.

Homework Equations


The probability that n students are absent on a given day in one class is binomially distributed.
I heard some say that since the relative standard deviation goes like 1/√N the class with fewer pupils is most likely to have most absent, but I am not sure about this.

The Attempt at a Solution


You could do a whole lot of counting, but I don't think that's the point. Besides it would be a very tedious proces. I did it with a class of 1 and 2 students and counting even then took time.
 
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  • #2
hi aaaa202! :smile:

(i haven't actually tried this :redface:, but …)

i think i'd calculate Pk, the probability that exactly k students out of 30 are absent

and then for each k the probability Qk that less than one third of k from 30 come from the 10

(and then multiply and add)
 
  • #3
aaaa202 said:

Homework Statement


In a class with 20 and one with 10 students each student has a probability of 0.3 to not show up on a particular day. On a given day, which class is most likely to have the highest ratio of absent students? This was in my exam, unfortunately I did not know how to do it.

Let N be the number of students absent from the large class, and M the number of students absent from the small class. The ratio of absent students will be larger for the large class if N > 2M, equal if N = 2M, and smaller if N < 2M. So you need to work out the probabilities of those three events.
 

Related to Predicting Absenteeism: Comparing Binomial Distribution in Two Classes

1. What is a binomial distribution?

A binomial distribution is a probability distribution that describes the possible outcomes of a repeated experiment with two possible outcomes (usually labeled as "success" and "failure") and a fixed number of trials. It is often used to model real-world situations such as coin flips or product defects.

2. What are the key characteristics of a binomial distribution?

The key characteristics of a binomial distribution include a fixed number of trials, two possible outcomes, a constant probability of success for each trial, and independent trials (the outcome of one trial does not affect the outcome of another).

3. How is a binomial distribution different from a normal distribution?

A binomial distribution is discrete, meaning that the possible outcomes are counted and can only take on certain values, while a normal distribution is continuous, meaning that the possible outcomes can take on any value within a range. Additionally, the shape of a binomial distribution is skewed, while a normal distribution is symmetric.

4. What is the formula for calculating the probability of a specific outcome in a binomial distribution?

The formula for calculating the probability of a specific outcome in a binomial distribution is P(X = k) = nCk * p^k * (1-p)^(n-k), where n is the number of trials, k is the number of successes, and p is the probability of success for each trial. nCk represents the number of ways to choose k successes from n trials.

5. How is a binomial distribution used in real-life scenarios?

A binomial distribution can be used to model real-life scenarios such as the success rate of a new medical treatment, the probability of a student passing an exam, or the chances of a sports team winning a game. It can also be used in quality control to determine the likelihood of a certain number of defects in a batch of products.

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