Statistics Exam 2

Question 1

Variables that vary within their domain and depend on the outcome of an experiment.

Answer 1

Random Variables

Question 2

Type of distribution that has only two outcomes.

Answer 2

Bernoulli Distribution

Question 3

Defines the “Shape” of a distribution

Answer 3

Parameter

Question 4

Seeks to determine the probability that something is less than or equal to a number or greater than or equal to a number.

Answer 4

Cumulative Distribution Function (CDF)

Question 5

The value you would most likely expect for an outcome given a pmf.

Answer 5

Expected Value

Question 6

Describes the spread of the values of the sample in the population.

Answer 6

Variance of a Random Variable

Question 7

Applications:
- Tossing of a coin.
- Lights on or off.
- Disease in a person.
- Roulette wheel.

Answer 7

Bernoulli Distribution

Question 8

Applications:
- The first success occurring on the Xth trial.
- The number of failures before the first success.

Answer 8

Geometric Distribution

Question 9

The sum of N Bernoulli trials.

Answer 9

Binomial Distribution

Question 10

Applications:
- Six heads when you toss a coin ten times.
- 12 women in sample size of 20.
- Three defective items in batch of 100.

Answer 10

Binomial Distribution

Question 11

Applications:
- The Powerball lottery.
- Poker hands.
- Chance of picking a defective part from a box.
- Picking R or D voters in a sample of voters in a district.

Answer 11

Hypergeometric Distribution

Question 12

Applications:
- Rolling the 5th 6 on the 20th roll of a die.
- Getting the 10th defective item on the 1000th item inspected.
- Selecting the 10th woman as the 15th participant .

Answer 12

Negative Binomial Distribution

Question 13

Applications:
- Text messages per hour.
- Customers in a restaurant.
- Machine malfunctions.
- Website visitors per month.

Answer 13

Poisson Distribution

Question 14

Variables that vary within their domain (the sample space), can take on any value in a range, and depend on the outcome of an experiment.

Answer 14

Continuous Random Variables

Question 15

Distributions that are countable, have distinct points, the points have probability, and p(x) is a probability mass function.

Answer 15

Discrete Distributions

Question 16

Distributions that are uncountable, are on a continuous interval, the points have no probability, and f(x) is a probability density function.

Answer 16

Continuous Distributions

Question 17

Applications:
- Random number generator.
- Random sampling.
- Radioactive decay over time.

Answer 17

Uniform Distribution

Question 18

Applications:
- Heights of individuals.
- Blood pressure.
- IQ scores.
- Measurement errors.

Answer 18

Normal Distribution

Question 19

Applications:
- Time until a bus arrives.
- Time until the 3rd customer enters.
- Days before travel that a ticket is purchased.

Answer 19

Exponential Distribution

Question 20

Applications:
- Time between independent events.
- Time until death.
- Time until parts wear out.
- Time until the 3rd accident.

Answer 20

Gamma Distribution

Question 21

Specifies the number of events you are modeling.

Answer 21

Shape Parameter (Gamma Distribution)

Question 22

Represents the mean time between events.

Answer 22

Scale Parameter (Gamma Distribution)

Question 23

Applications:
- Widely used in reliability.
- Fits a wide variety of data sets allowing for both left and right skewed data.

Answer 23

Weibull Distribution

Question 24

Applications:
- Milk production by cows.
- Lives of industrial units with failure modes.
- Amount of rainfall.
- Size of raindrops.

Answer 24

Lognormal Distribution

Question 25

Applications:
- Used if there is a finite interval for the RV X.

Answer 25

Beta Distribution

Question 26

Whether a variation in one variable results in a variation of another.

Answer 26

Covariance

Question 27

The direction and the strength of the relationship between two variables.

Answer 27

Correlation

Question 28

Type of probability distribution that is created by drawing many random samples of the given size from the same population.

Answer 28

Sampling

Question 29

The sampling distribution of the mean will always be normally distributed, as long as the sample size is large enough regardless of the distribution of the underlying population.

Answer 29

Central Limit Theorem