Margie is practicing for an upcoming tennis tournament. Her first serve is good 20 out of 30 times on average. Margie wants to know the estimated probability that her first serve will be good at least four of the next six times she serves. How could she design a simulation for this scenario

Answer :

Probability that Margie's first serve will be good at least four of the next six times she serves is 0.68

What is Binomial Distribution in Probability?

Assume that n independent replications of a random experiment with exactly two outcomes are performed. Success has a probability of [tex]p[/tex], while failure has a probability of [tex]q[/tex]. Assume that out of these n times, we will experience success [tex]x[/tex] times and failure [tex]n-x[/tex] times. There are [tex]^nC_x[/tex] different ways in which we can succeed. If, [tex]X[/tex] has a binomial distribution, then,

[tex]P(X=x)=p(x)[/tex]

                  [tex]=[/tex] [tex]^nC_xp^xq^{n-x}[/tex]

for [tex]x[/tex] = 0, 1,..., n. Here,  [tex]q=1-p[/tex]. A binomial variate is any such random variable [tex]X[/tex]. A group of n separate Bernoullian trials is known as a binomial trial. Binomial distribution requirements:

  • There are just two outcomes, success and failure, for each trial.
  • The quantity "[tex]n[/tex]" of trials is finite.
  • The trials run separately from one another.

For each trial, the odds of success [tex]p[/tex], or failure [tex]q[/tex], remain constant.

Given,

Probability of good serves = Success

⇒ [tex]p=\frac{20}{30}[/tex]

⇒[tex]p=\frac{2}{3}[/tex]

Failure = [tex]q[/tex]

⇒ [tex]q=1-p[/tex]

⇒ [tex]q=1-\frac{2}{3}[/tex]

⇒ [tex]q=\frac{1}{3}[/tex]

Number of trials [tex]= n[/tex]

[tex]n=6[/tex]

Probability of at least four good serve = [tex]P(4 < X < 6)[/tex]

According to the binomial distribution,

Therefore,

[tex]P(4 \leq X \leq 6)=P(X=4)+P(X=5)+P(X=6)[/tex]

                      [tex]=[/tex] [tex]^6C_4(\frac{2}{3}) ^4(\frac{1}{3})^2+ ^6C_5(\frac{2}{3}) ^5(\frac{1}{3})^1+^6C_6(\frac{2}{3}) ^6(\frac{1}{3})^0[/tex]

                      [tex]=[/tex] [tex]15\times\frac{16}{729} +6\times\frac{32}{729} +1\times\frac{64}{729}[/tex]

                      [tex]=[/tex] [tex]\frac{496}{729}[/tex]

                      [tex]= 0.68[/tex]

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