Chapter 3 Flashcards

1
Q

What is a random variable?

A

A function that assigns numerical values to outcomes of a random experiment.

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2
Q

What is an example of a discrete random variable?

A

The number of heads in three coin tosses.

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3
Q

What makes a random variable discrete?

A

It has a countable range of possible values.

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4
Q

What is the Probability Mass Function (PMF)?

A

A function that gives the probability of each value a discrete random variable can take.

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5
Q

What is an example of a PMF?

A

For a fair die, P(X=1) = 1/6, P(X=2) = 1/6, etc.

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6
Q

What is the sum of probabilities in a PMF?

A

The total probability must sum to 1.

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7
Q

What is an independent random variable?

A

Two variables are independent if knowing one gives no information about the other.

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8
Q

What is the formula for independence of two discrete random variables?

A

P(X=x, Y=y) = P(X=x)P(Y=y) for all values of X and Y.

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9
Q

What is the Bernoulli distribution?

A

A distribution with two possible outcomes, success (1) and failure (0), with probability p and 1-p.

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10
Q

What is an example of a Bernoulli distribution?

A

Flipping a coin where heads = 1 and tails = 0.

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11
Q

What is the Binomial distribution?

A

A distribution modeling the number of successes in n independent Bernoulli trials.

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12
Q

What are the parameters of a Binomial distribution?

A

n (number of trials) and p (probability of success per trial).

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13
Q

What is the formula for the Binomial PMF?

A

P(X=k) = (n choose k) * p^k * (1-p)^(n-k).

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14
Q

What is the Negative Binomial (Pascal) distribution?

A

A distribution that models the number of trials needed to observe m successes.

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15
Q

What is the Poisson distribution used for?

A

Modeling the number of events occurring in a fixed interval of time or space.

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16
Q

What is the Poisson PMF formula?

A

P(X=k) = (e^(-λ) * λ^k) / k!.

17
Q

What is the relationship between the Binomial and Poisson distributions?

A

Poisson is a limiting case of the Binomial when n is large and p is small.

18
Q

What is the Cumulative Distribution Function (CDF)?

A

A function that gives the probability that a random variable is less than or equal to a value.

19
Q

What is the expectation (expected value) of a random variable?

A

The long-term average value of the variable over many trials.

20
Q

What is the formula for expectation?

A

E(X) = Σ x * P(X=x).

21
Q

What is an example of expectation?

A

For a fair die, E(X) = (1+2+3+4+5+6)/6 = 3.5.

22
Q

What is the variance of a random variable?

A

A measure of how spread out the values are around the mean.

23
Q

What is the formula for variance?

A

Var(X) = E[(X - E(X))^2] = E(X^2) - (E(X))^2.

24
Q

What does a higher variance indicate?

A

The values of X are more spread out from the mean.

25
Q

What is the standard deviation?

A

The square root of the variance, measuring spread in the same units as X.

26
Q

What is the Law of the Unconscious Statistician (LOTUS)?

A

A theorem stating E[g(X)] = Σ g(x) * P(X=x).

27
Q

What is an example of applying LOTUS?

A

If X is a die roll, E(X^2) = Σ x^2 * P(X=x) = (1^2+2^2+…+6^2)/6.

28
Q

What is the sum of independent random variables?

A

E(X+Y) = E(X) + E(Y) and Var(X+Y) = Var(X) + Var(Y) if X and Y are independent.

29
Q

What is an application of the Poisson distribution?

A

Modeling the number of customers arriving at a store per hour.

30
Q

What is a key property of the Binomial distribution?

A

If X~Binomial(n, p) and Y~Binomial(m, p), then X+Y ~ Binomial(n+m, p).