Using the binomial distribution, it is found that:
a) The possible values of X are between 0 and 20.
b) 0.0596 = 5.96% probability that exactly 3 experience insomnia as a side effect.
c) 0.9842 = 98.42% probability 3 or fewer experience insomnia.
d) 0.639 = 63.9% probability that between 1 and 4 experience insomnia.
e) Since [tex]4 > E(X) + 2.5\sqrt{V(X)}[/tex], it would be unusual to find 4 or more patients experience insomnia as a side effect.
For each patient, there are only two possible outcomes, either they experience insomnia, or they did not. The probability of a patient having experienced insomnia is independent of any other patient, which means that the binomial distribution is used to solve this question.
Binomial probability distribution
Probability of exactly x successes on n trials, with p probability.
[tex]P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}[/tex]
[tex]C_{n,x} = \frac{n!}{x!(n-x)!}[/tex]
The expected value is:
[tex]E(X) = np[/tex]
The standard deviation is:
[tex]\sqrt{V(X)} = \sqrt{np(1 - p)}[/tex]
If a measure is more than 2.5 standard deviations above the mean, it is considered unusual.
In this problem:
Item a:
The possible values of X are always between 0 and n, thus in this case, it is between 0 and 20.
Item b:
This probability is P(X = 3), thus:
[tex]P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}[/tex]
[tex]P(X = 3) = C_{20,3}.(0.05)^{3}.(0.95)^{17} = 0.0596[/tex]
0.0596 = 5.96% probability that exactly 3 experience insomnia as a side effect.
Item c:
This probability is [tex]P(X \leq 3)[/tex], given by:
[tex]P(X \leq 3) = P(X = 0) + P(X = 1) + P(X = 2) + P(X = 3)[/tex]
Then:
[tex]P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}[/tex]
[tex]P(X = 0) = C_{20,0}.(0.05)^{0}.(0.95)^{20} = 0.3585[/tex]
[tex]P(X = 1) = C_{20,1}.(0.05)^{1}.(0.95)^{19} = 0.3774[/tex]
[tex]P(X = 2) = C_{20,2}.(0.05)^{2}.(0.95)^{18} = 0.1887[/tex]
[tex]P(X = 3) = C_{20,3}.(0.05)^{3}.(0.95)^{17} = 0.0596[/tex]
[tex]P(X \leq 3) = P(X = 0) + P(X = 1) + P(X = 2) + P(X = 3) = 0.3585 + 0.3774 + 0.1887 + 0.0596 = 0.9842[/tex]
0.9842 = 98.42% probability 3 or fewer experience insomnia.
Item d:
This probability is:
[tex]P(1 \leq X \leq 4) = P(X = 1) + P(X = 2) + P(X = 3) + P(X = 4)[/tex]
Then
[tex]P(X = x) = C_{n,x}.p^{x}.(1-p)^{n-x}[/tex]
[tex]P(X = 1) = C_{20,1}.(0.05)^{1}.(0.95)^{19} = 0.3774[/tex]
[tex]P(X = 2) = C_{20,2}.(0.05)^{2}.(0.95)^{18} = 0.1887[/tex]
[tex]P(X = 3) = C_{20,3}.(0.05)^{3}.(0.95)^{17} = 0.0596[/tex]
[tex]P(X = 4) = C_{20,4}.(0.05)^{4}.(0.95)^{16} = 0.0133[/tex]
[tex]P(1 \leq X \leq 4) = P(X = 1) + P(X = 2) + P(X = 3) + P(X = 4) = 0.3774 + 0.1887 + 0.0596 + 0.0133 = 0.639[/tex]
0.639 = 63.9% probability that between 1 and 4 experience insomnia.
Item e:
[tex]E(X) = np = 20(0.05) = 1[/tex]
[tex]\sqrt{V(X)} = \sqrt{np(1 - p)} = \sqrt{20(0.5)(0.95)} = 0.975[/tex]
[tex]E(X) + 2.5\sqrt{V(X)} = 1 + 2.5(0.975) = 3.44[/tex]
Since [tex]4 > E(X) + 2.5\sqrt{V(X)}[/tex], it would be unusual to find 4 or more patients experience insomnia as a side effect.
A similar problem is given at https://brainly.com/question/24863377