In this finale quiz, we'll apply what we know about **random variables** and **probability distributions** to real-world problems.

Since these applications are inspired by real-life scenarios, they're more challenging than the problems we looked it in the last two quizzes.

The final problem in particular requires calculus; it may be skipped without loss.

**Poisson variable**:
\[ P(X=n) = \frac{\lambda^{n}}{n!} e^{-\lambda}, \ n = 0 , 1 , 2 , \ldots, \ \ \ \ \lambda = k t \]
What is the expected number of failures
\[E[X] = \sum\limits_{n \in X\text{'s range}} n P(X = n) \ ?\]

**Hint:** The exponential function is defined by the infinite sum
\[ e^{x} = \sum\limits_{n=0}^{\infty} \frac{x^n}{n!}.\]

**variance**
\[ \text{Var}[X] = E\big[X^2\big] - \big( E[X]\big)^2\]
for the number of display breakdowns from the last problem, which is distributed as
\[ P(X=n) = \frac{\lambda^{n}}{n!} e^{-\lambda}, \ n = 0 , 1 , 2 , \ldots, \ \ \text{where} \ e^{x} = \sum\limits_{n=0}^{\infty} \frac{x^n}{n!}.\]

Let's say a particular trait, like hair or eye color, is determined by one pair of genes. Let D be the dominant gene, and R the recessive.

The possible gene combinations are DD (pure dominance), DR or RD (hybrid), or RR (pure recessive). Children with at least one copy of the dominant gene exhibit the dominant trait.

A child receives one gene from each parent, and assuming they're both hybrids, each of the child's genes is equally likely to be a D or an R.

If a pair of hybrid parents have a total of T children, of which N exhibit the dominant trait, what is \( P(N = n ) ?\)

**Good to Know:** The number of ways to choose \( k \) objects from a set of \( n \) is \[{n \choose k} = \frac{n!}{k!(n-k)!},\]
where \( n! = n(n-1)(n-2) \cdots 1\) for \(\ n \geq 1\) and \(0! = 1.\)

In a very large sample of families with eight children, what's the average number of children *not* showing the appearance of the dominant trait?

**Hint:** For a binomial random variable \[ P(N=n) = { T \choose n }p^n \left( 1-p \right)^{T-n},\ E[N] = T p.\]

It's reported that the probability of winning a certain state lottery is about \( \frac{1}{8}.\) Let \( X \) be the number of tickets purchased up to and including the first win.

What is \( P( X = k ) \) for \( k = 1, 2, 3, \dots ? \)

What's the probability of winning this lottery in five tries or less?

**Good to Know:** \(\displaystyle \sum\limits_{j=0}^{k} p^{j} = \frac{1-p^{k+1}}{1-p} , \ 0 < p < 1\)

The distribution \( P(X=k) = \left( \frac{7}{8} \right)^{k-1} \frac{1}{8}\) is an example of the **geometric distribution** because of its relationship with the geometric sum
\[ \sum\limits_{j=0}^{\infty} p^{j} = \frac{1}{1-p} , \ 0 < p < 1.\]
If the lotto from the last problem pays out $1,000 on a win but costs $1 per ticket, what's the average payout if you play to the first win?

**Hint:** The expected value of a function of a random variable is
\[ E\big[f(X)\big] = \sum\limits_{ k \in X \text{'s range}} f(k) P(X = k ), \]
and
\[ \sum\limits_{j=0}^{\infty} j p^{j} = \frac{p}{(1-p)^2}, \ 0 < p < 1.\]

**exponentially**:
\[ f(t) = a e^{-a t} , \ t \geq 0, \ \text{where } a > 0 \text{ is constant}. \]
What's the probability that a carbon-14 atom lasts at least as long as \( \frac{1}{a} ?\)

In this quiz, we sampled some applications of continuous and discrete random variables and their probability distributions.

The rest of the course will explore these ideas and other applications in greater depth.

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