Qa-233 The Normal Probability Distribution

Front 1

What determines the location and shape of the normal probability distribution?

Back 1

Two parameters:
1) mean (location)
2) standard deviation (shape)

Front 2

What is the probability of any particular value for a continuous random variable?

Back 2

0

Front 3

What is the highest point on the normal probability distribution curve?

Back 3

The mean, which is also the median and the mode.

Front 4

What random variable can take on any value in a range?

Back 4

Continuous random variable

Front 5

What are the 4 discrete probability distributions most commonly used in business applications?

Back 5

1) Discrete uniform
2) Binomial
3) Poisson
4) Hypergeometric

Front 6

What determines the spread or width of the normal probability distribution?

Back 6

The standard deviation

Front 7

A numerical description of the outcome of an experiment:

Back 7

Random variable

Front 8

What is the area under the normal probability distribution curve?

Back 8

1.0

Front 9

Graphical respresentation of how probabilities are allocated to potential values of a random variable:

Back 9

Probability distribution curve

Front 10

Probability and height are the same for what kind of variables?

Back 10

Discrete random variables

Front 11

A numerical description of the outcome of an experiment that can yield only a finite number of values or an infinite sequence such as 0, 1, 2,...:

Back 11

Discrete random variable

Front 12

Continuous probability distribution in which all potential values of a random variable X are equally likely:

Back 12

Continuous uniform (ex: )

Front 13

Description of how probabilities are allocated to potential values of a random variable:

Back 13

Probability distribution

Front 14

Discrete probability distribution in which all potential values of a random variable X are equally likely:

Back 14

Discrete uniform (ex: lottery)

Front 15

Mathematical expression for assigning probabilities to potential values of a random variable:

Back 15

Probability distribution function

Front 16

A numerical description of the outcome of an experiment whose outcome can assume any numerical value in an interval or collection of intervals:

Back 16

Continuous random variable

Front 17

The probability distribution associated with any normal random variable that has a mean of 0 and a standard deviation of 1:

Back 17

Standard Normal Probability Distribution

Front 18

The means by which any normal random variable with a mean of 0 and a standard deviation of 1 and can be converted into a standard normal random variable:

Back 18

z-Transformation

Front 19

A numerical description of the outcome of an experiment:

Back 19

Random Variable

Front 20

Discrete probability distribution that describes the probability of a given number of occurrences of some relatively rare event over time or space:

Back 20

Poisson

Front 21

List 4 discrete probability distributions:

Back 21

1. Discrete Uniform
2. Binomial
3. Poisson
4. Hypergeometric

Front 22

The product of every value of random variable X and the corresponding value of f(x) over all possible values of X:

Back 22

Expected Value of a Random variable

Front 23

Continuous probability distribution that describes the probability A GIVEN AMOUNT OF TIME will pass b/t consecutive occurrences of some relatively rare (Poisson) event:

Back 23

Exponential

Front 24

Discrete probability distribution in which all potential values of random variable X are equally likely:

Back 24

Discrete Uniform

Front 25

Continuous probability distribution that describes the likelihoods of outcomes for a random variable X with a particular symmetric and unimodal distribution:

Back 25

Normal

Front 26

A numerical description of the outcome of an experiment that can yield only a finite number of values or an infinite sequence such as 0, 1, 2, ....:

Back 26

Discrete Random Variable

Front 27

The description of how probabilities are allocated to potential values of a random variable:

Back 27

Probability Distribution

Front 28

Discrete probability distribution that describes the probability of a given number of successes (the random variable X) over n identical and independent trials where the probability of success p is constant across trials for an infinite population:

Back 28

Binomial

Front 29

Continuous probability distribution in which all potential values of random variable X are equally likely:

Back 29

(Continuous) Uniform

Front 30

Discrete probability distribution that describes the probability of a given number of successes (the random variable X) over n identical and independent trials where the probability of success p is constant across trials for a finite population of N elements with r successes:

Back 30

Hypergeometric

Front 31

A numerical description of the outcome of an experiment whose outcome can assume any numerical value in an interval or collection of intervals:

Back 31

Continuous Random Variable

Front 32

Which discrete probability distribution should you use if all outcomes are equally likely?

Back 32

Discrete Uniform Distribution

Front 33

The product of the squared difference b/t every value of random variable X & the mean of X & the corresponding value of f(x) over all possible values of X:

Back 33

Variance of a Random variable

Front 34

Which continuous probability distribution should you use if you are measuring the time b/t occurrences of a relatively rare (Poisson) event?

Back 34

Exponential Distribution

Front 35

Classify the following random variables as discrete or continuous:

The length of time you stay in a class.

Back 35

Continuous

Front 36

Which probability disttribution would best describe the number of cars passing the freeway in a 5 minute interval?

Back 36

Poisson (discrete)

Front 37

Which continuous probability should you use if all outcomes are equally likely?

Back 37

(Continuous) Uniform Distribution

Front 38

Graphical representation of how probabilities are allocated to potential values of a random variable where f(x) represents the height of the curve (on the y-axis) at the corresponding value of the random variable:

Back 38

Probability distribution curve