QTA 3 - COMMON UNIVARIATE RANDOM VARIABLES Flashcards

1
Q

What are the key properties among the following distributions: Uniform, Bernoulli, Poisson, Normal, Lognormal, Chi-squared, Student’s t, and F?

A

They have distinct characteristics and applications in modeling different types of data.

Each distribution is utilized in specific contexts, such as modeling binary events, counts of events, or continuous data.

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2
Q

What is a Bernoulli distribution?

A

A discrete distribution for random variables that produces one of two values: 0 or 1.

It models binary outcomes like success/failure.

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3
Q

What parameter does the Bernoulli distribution depend on?

A

The probability of success, denoted as p.

The distribution is expressed as Y ~ Bernoulli(p).

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4
Q

What is the mean of a Bernoulli random variable Y?

A

E[Y] = p.

This is calculated as p * 1 + (1 - p) * 0.

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5
Q

What is the variance of a Bernoulli random variable Y?

A

V[Y] = p(1 - p).

Where q = 1 - p is the failure probability.

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6
Q

What is the probability mass function (PMF) of a Bernoulli distribution?

A

f(y) = p^y * (1 - p)^(1 - y).

This function only produces two values: p when y = 1 and (1 - p) when y = 0.

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7
Q

What is a binomial distribution?

A

It describes the sum of n independent Bernoulli random variables.

It models the total number of successes in n trials.

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8
Q

What parameters define a binomial distribution?

A

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

The binomial distribution is expressed as Y = B(n, p).

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9
Q

What is the mean of a binomially distributed random variable Y?

A

E[Y] = n * p.

This represents the expected number of successes in n trials.

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10
Q

What is the variance of a binomially distributed random variable Y?

A

V[Y] = n * p * (1 - p).

This accounts for the variability in the number of successes.

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11
Q

What is the skewness of a binomial distribution dependent on?

A

The probability p; small values produce right-skewed distributions.

This affects how the distribution is shaped.

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12
Q

What is a Poisson distribution used for?

A

To model counts of events over fixed time spans.

Examples include loan defaults or customer arrivals.

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13
Q

What is the single parameter of a Poisson distribution?

A

The hazard rate, denoted as Ξ».

This represents the average number of events per interval.

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14
Q

What are the mean and variance of a Poisson random variable Y?

A

Both are equal to Ξ».

This property simplifies calculations for Poisson processes.

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15
Q

What is the PMF of a Poisson random variable?

A

P(Y = n) = (Ξ»^n * e^(-Ξ»)) / n!.

This defines the probability of observing n events.

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16
Q

What is a uniform distribution?

A

A distribution where any value within the range [a, b] is equally likely to occur.

It is the simplest continuous random variable.

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17
Q

What is the PDF of a uniform distribution?

A

f(y) = 1 / (b - a) for a ≀ y ≀ b.

It is zero outside this range.

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18
Q

What is the mean of a uniform random variable Y ~ U(a, b)?

A

E[Y] = (a + b) / 2.

This represents the midpoint of the distribution’s support.

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19
Q

What is the variance of a uniform random variable Y ~ U(a, b)?

A

V[Y] = (b - a)^2 / 12.

This shows the distribution’s spread.

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20
Q

What is the normal distribution often referred to as?

A

Gaussian distribution or bell curve.

It is widely used in risk management.

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21
Q

What does the normal distribution play a key role in?

A

The Central Limit Theorem (CLT).

This is crucial for hypothesis testing.

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22
Q

What are the mean and variance of a normal distribution Y ~ N(ΞΌ, σ²)?

A

E[Y] = ΞΌ and V[Y] = σ².

These parameters fully describe the distribution.

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23
Q

What is the confidence interval for a 95% confidence level in a normal distribution?

A

ΞΌ - 1.96Οƒ to ΞΌ + 1.96Οƒ.

This interval contains approximately 95% of the data.

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24
Q

True or False: The normal distribution is infinitely divisible.

A

True.

This property allows for flexible modeling of random processes.

25
Q

What is the formula for a 90% confidence interval?

A

πœ‡ β€” 1.645𝜎 to πœ‡ + 1.645𝜎

26
Q

What is the formula for a 95% confidence interval?

A

πœ‡ β€” 1.96𝜎 to πœ‡ + 1.96𝜎

27
Q

What is the formula for a 99% confidence interval?

A

πœ‡ β€” 2.58𝜎 to πœ‡ + 2.58𝜎

28
Q

What is a standard normal distribution?

A

A normal distribution standardized to have a mean of zero and a standard deviation of one, denoted as 𝑋~𝑁(0,1)

29
Q

What does Ρ„(𝑧) denote?

A

The standard normal PDF

30
Q

What does Ξ¦(𝑧) denote?

A

The standard normal CDF

31
Q

What is the formula for converting an observed value 𝑦 to its z value?

A

z = (𝑦 β€” πœ‡) / 𝜎

32
Q

What are the conditions for a normal distribution to approximate a binomial random variable?

A
  • 𝑛𝑝 β‰₯ 10
  • 𝑛(1 β€” 𝑝) β‰₯ 10
33
Q

When can a Poisson distribution be approximated by a normal distribution?

A

When πœ† is large, specifically when πœ† β‰₯ 1000

34
Q

What defines a log-normally distributed variable?

A

A variable π‘Œ is log-normally distributed if the natural logarithm of π‘Œ is normally distributed

35
Q

What is the relationship between π‘Œ and 𝑋 in a log-normal distribution?

A

If 𝑋 = 𝑙𝑛(π‘Œ), then π‘Œ is log-normally distributed if and only if 𝑋 is normally distributed

36
Q

What is the mean of a log-normally distributed variable π‘Œ?

A

E[π‘Œ] = e^(ΞΌ + 1/2Οƒ^2)

37
Q

What is the variance of a log-normally distributed variable π‘Œ?

A

V[π‘Œ] = e^(2ΞΌ + Οƒ^2) - e^(2ΞΌ + Οƒ^2)

38
Q

What is the characteristic of a log-normal distribution?

A

It is positively skewed

39
Q

What is the PDF of a log-normal distribution?

A

𝑓(𝑦) = (1 / (π‘¦Οƒβˆš(2Ο€))) * e^(-(ln(𝑦) - ΞΌ)^2 / (2Οƒ^2))

40
Q

What is the chi-squared distribution frequently used for?

A

Testing hypotheses about model parameters

41
Q

How is a chi-squared random variable defined?

A

As the sum of the squares of 𝜈 independent standard normal random variables

42
Q

What is the mean of a chi-squared distribution?

A

E[π‘Œ] = 𝜈

43
Q

What is the variance of a chi-squared distribution?

A

V[π‘Œ] = 2𝜈

44
Q

What is the Student’s t distribution used for?

A

Testing hypotheses using small samples

45
Q

What is the mean of a Student’s t distribution?

A

E[π‘Œ] = 0

46
Q

What is the variance of a Student’s t distribution?

A

V[π‘Œ] = 𝜈 / (𝜈 - 2), finite if 𝜈 > 2

47
Q

What is the F distribution commonly used for?

A

Testing hypotheses about model parameters

48
Q

What is the mean of an F distribution?

A

E[π‘Œ] = 𝜈2 / (𝜈2 - 2), finite when 𝜈2 > 2

49
Q

What is the variance of an F distribution?

A

V[π‘Œ] = (2𝜈2(𝜈1 + 𝜈2 - 2)) / (𝜈1(𝜈2 - 2)^2), finite for 𝜈2 > 4

50
Q

What is the exponential distribution used for?

A

Modeling time until an event occurs, with a single parameter 𝛽

51
Q

What is the mean of an exponentially distributed variable?

A

E[π‘Œ] = 𝛽

52
Q

What is the variance of an exponentially distributed variable?

A

V[π‘Œ] = 𝛽^2

53
Q

What does the beta distribution model?

A

Continuous random variables with outcomes between 0 and 1

54
Q

What are the parameters of a beta distribution?

A

Ξ± and 𝛽

55
Q

What is the mean of a beta-distributed random variable?

A

E[π‘Œ] = Ξ± / (Ξ± + 𝛽)

56
Q

What is the variance of a beta-distributed random variable?

A

V[π‘Œ] = (α𝛽) / ((Ξ± + 𝛽)^2(Ξ± + 𝛽 + 1))

57
Q

What are mixture distributions?

A

Distributions built using two or more component distributions

58
Q

What is a characteristic of mixing components with different means and variances?

A

Produces a distribution that is both skewed and heavy-tailed