Common Probability Distributions

Question 1

probability distribution

Answer 1

describes the probabilities of all the possible outcomes for a random variable.

Question 2

discrete random variable

Answer 2

number of possible outcomes can be counted, and for each possible outcome, there is a measurable and positive probability.
example number of days it will rain in a given month, because there is a countable number of possible outcomes, ranging from zero to the number of days in the month.

Question 3

continuous random variable

Answer 3

the number of possible outcomes is infinite, even if lower and upper bounds exist.
ex actual amount of rainfall

Question 4

discrete uniform random variable

Answer 4

probabilities for all possible outcomes for a discrete random variable are equal.

Question 5

binomial random variable

Answer 5

number of “successes” in a given number of trials, whereby the outcome can be either “success” or “failure.”

Question 6

Bernoulli random variable

Answer 6

binomial random variable for which the number of trials is 1

Question 7

node

Answer 7

Each of the possible values along a binomial tree

Question 8

univariate distributions

Answer 8

distribution of a single random variable

Question 9

multivariate distribution

Answer 9

multivariate distribution

Question 10

standard normal distribution

Answer 10

normal distribution that has been standardized so that it has a mean of zero and a standard deviation of 1.To standardize an observation from a given normal distribution, the z-value of the observation must be calculated.

Question 11

Monte Carlo simulation

Answer 11

repeated generation of one or more risk factors that affect security values, in order to generate a distribution of security values. For each of the risk factors, the analyst must specify the parameters of the probability distribution that the risk factor is assumed to follow. A computer is then used to generate random values for each risk factor based on its assumed probability distributions.

Question 12

Simple random sampling

Answer 12

selecting a sample in such a way that each item or person in the population being studied has the same likelihood of being included in the sample

Question 13

systematic sampling

Answer 13

selecting every nth member from a population.

Question 14

Sampling error

Answer 14

difference between a sample statistic (the mean, variance, or standard deviation of the sample) and its corresponding population parameter

Question 15

Stratified random sampling

Answer 15

uses a classification system to separate the population into smaller groups based on one or more distinguishing characteristics. From each subgroup, or stratum, a random sample is taken and the results are pooled. The size of the samples from each stratum is based on the size of the stratum relative to the population.

Question 16

Time-series data

Answer 16

observations taken over a period of time at specific and equally spaced time intervals.

Question 17

Cross-sectional data

Answer 17

sample of observations taken at a single point in time

Question 18

Longitudinal data

Answer 18

observations over time of multiple characteristics of the same entity, such as unemployment, inflation, and GDP growth rates for a country over 10 years.

Question 19

Panel data

Answer 19

observations over time of the same characteristic for multiple entities, such as debt/equity ratios for 20 companies over the most recent 24 quarters