The probability of X = 0 for a binomial random variable with n = 4 and p = 0.45 is approximately 0.0897.
To compute the probability of X = 0 for a binomial random variable, we can use the probability mass function (PMF) formula:
[tex]P(X = k) = C(n, k) * p^k * (1 - p)^(n - k)[/tex]
Where:
- P(X = k) is the probability of X taking the value k.
- C(n, k) is the binomial coefficient, given by C(n, k) = n! / (k! * (n - k)!).
- n is the number of trials.
- p is the probability of success on each trial.
- k is the desired number of successes.
In this case, we have n = 4 and p = 0.45. We want to find P(X = 0), so k = 0. Plugging in these values, we get:
[tex]P(X = 0) = C(4, 0) * 0.45^0 * (1 - 0.45)^(4 - 0)[/tex]
The binomial coefficient C(4, 0) is equal to 1, and any number raised to the power of 0 is 1. Thus, the calculation simplifies to:
[tex]P(X = 0) = 1 * 1 * (1 - 0.45)^4P(X = 0) = 1 * 1 * 0.55^4P(X = 0) = 0.55^4[/tex]
Calculating this expression, we find:
P(X = 0) ≈ 0.0897
Therefore, the probability of X = 0 for the binomial random variable is approximately 0.0897.
To know more about binomial random variable refer here:
https://brainly.com/question/31311574#
#SPJ11
when using bayes theorem, why do you gather more information ?
When using Bayes' theorem, you gather more information because it allows you to update the prior probability of an event occurring with additional evidence.
Bayes' theorem is used for calculating conditional probability. The theorem gives us a way to revise existing predictions or probability estimates based on new information. Bayes' Theorem is a mathematical formula used to calculate conditional probability. Conditional probability refers to the likelihood of an event happening given that another event has already occurred. Bayes' Theorem is useful when we want to know the probability of an event based on the prior knowledge of conditions that might be related to the event. In Bayes' theorem, the posterior probability is calculated using Bayes' rule, which involves multiplying the prior probability by the likelihood and dividing by the evidence. For example, let's say that you want to calculate the probability of a person having a certain disease given a positive test result. Bayes' theorem would allow you to update the prior probability of having the disease with the new evidence of the test result. The more information you have, the more accurately you can calculate the posterior probability. Therefore, gathering more information is essential when using Bayes' theorem.
To know more about probability, visit;
//brainly.com/question/31828911
#SPJ11
