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Business Statistics I > Chapter 4 and Chapter 5 > Flashcards

Chapter 4 and Chapter 5 Flashcards

1
Q

A numerical measure of the likelihood that an event will occur.

A

Probability

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2
Q

The set of all experimental outcomes.

A

Sample Space

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3
Q

An element of the sample space that represents an experimental outcome.

A

Sample Point

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4
Q

A collection of sample points.

A

Event

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5
Q

A method of assigning probabilities that is appropriate when all the experimental outcomes are equally likely.

A

Classical Method

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6
Q

A process that generates well-defined outcomes.

A

Probability Experiment

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7
Q

A method of assigning probabilities on the basis of judgment.

A

Subjective Method

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8
Q

A method of assigning probabilities that is appropriate when data are available to estimate the proportion of the time the experimental outcome will occur if the experiment is repeated a large number of times.

A

Relative Frequency Method

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9
Q

Name 3 methods for assigning probabilities.

A

Classical Method
Subjective Method
Relative Frequency Method

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10
Q

A graphical representation that helps in visualizing a multiple-step experiment.

A

Tree Diagram

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11
Q

A graphical representation for showing symbolically the sample space and operations involving events in which the sample space is represented by a rectangle and events are represented as circles within the sample space.

A

Venn Diagram

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12
Q

In an experiment, we may be interested in determining the number of ways n objects may be selected from among N objects without regard to the order in which the n objects are selected.

A

Combination

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13
Q

In an experiment, we may be interested in determining the number of ways n objects may be selected from among N objects when the order in which the n objects are selected is important.

A

Permutation

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14
Q

The event containing all sample points belonging to A or B or both.

A

Union

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15
Q

A probability law used to compute the probability of the union of two events.

A

Addition Law

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16
Q

Events that have no sample points in common.

A

Mutually Exclusive Events

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17
Q

The event consisting of all sample points that are not in A.

A

Complement

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18
Q

The event containing the sample points belonging to both A and B.

A

Intersection

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19
Q

Two events A and B where P(A I B) = P(A) or P(B I A) = P(B); that is, the events have no influence on each other.

A

Independent Events

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20
Q

A probability law used to compute the probability of the intersection of two events. It is P(B)P(A I B) or P(A)P(B I A). For independent events it reduces to P(A)P(B).

A

Multiplication Law

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21
Q

The probability of two events both occurring; that is, the probability of the intersection of two events.

A

Joint Probability

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22
Q

The values in the margins of a joint probability table that provide the probabilities of each event separately.

A

Marginal Probability

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23
Q

The probability of an event given that another event already occurred.

A

Conditional Probability

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24
Q

Initial estimates of the probability of events.

A

Prior Probabilities

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25
Q

Revised probabilities of events based on additional information.

A

Posterior Probabilities

26
Q

A method used to compute posterior probabilities.

A

Bayes’ Theorem

27
Q

A numerical description of the outcome of an experiment.

A

Random Variable

28
Q

A random variable that may assume either a finite number of values or an infinite sequence of values.

A

Discrete Random Variable

29
Q

A random variable that may assume any numerical value in an interval or collection of intervals.

A

Continuous Random Variable

30
Q

A function f(x) that provides the probability that x assumes a particular value for a discrete random variable.

A

Probability Function

31
Q

A discrete probability distribution for which the relative frequency method is used to assign the probabilities.

A

Empirical Discrete Distribution

32
Q

A measure of the central location of a random variable.

A

Expected Value

33
Q

A measure of the variability, or dispersion, or a random variable.

A

Variance

34
Q

The positive square root of variance.

A

Standard Deviation

35
Q

A probability distribution for which each possible value of the random variable has the same probability.

A

Discrete Uniform Probability Distribution

36
Q

Name the 4 properties of a binomial experiment.

A
  1. It consists of identical trials.
  2. There are two outcomes possible on each trial (success or failure).
  3. The probability of success on each trial is the same.
  4. The trials are independent.
37
Q

A probability distribution showing the probability of x successes in n trials of a binomial experiment.

A

Binomial Probability Distribution

38
Q

A probability distribution showing the probability of x occurrences of an event over a specified interval of time of space.

A

Poisson Probability Distribution

39
Q

A probability distribution showing the probability of x successes in n trials from a population with r successes and N-r failures.

A

Hypergeometric Probability Distribution

40
Q

The Excel function for combinations.

A

COMBIN

41
Q

The Excel function for permutations.

A

PERMUT

42
Q

The Excel function for factorial

A

FACT

43
Q

The Excel function to find the sum of the products within multiple arrays.

A

SUMPRODUCT

44
Q

The Excel function for square root.

A

SQRT

45
Q

The Excel function for a binomial probability distribution.

A

BINOM.DIST

46
Q

The Excel function for a binomial probability distribution for a range of values for the random variable… ???

A

BINOM.DIST.RANGE

47
Q

The Excel function for a Poisson probability distribution.

A

POISSON.DIST

48
Q

The Excel function for a hypergeometric probability distribution.

A

HYPGEOM.DIST

49
Q

What is the discrete uniform probability function?

A

f(x) = 1/n

50
Q

f(x) = 1/n

A

Discrete Uniform Probability Function

51
Q

The sum of each random variable value multiplied by its corresponding probability.

A

Expected Value of a Discrete Random Variable

52
Q

Σxf(x)

A

Expected Value of a Discrete Random Variable

53
Q

Σ[(x-μ)^2]f(x)

A

Variance of a Discrete Random Variable

54
Q

The sum of the squared deviations for each random variable value multiplied by its corresponding probability (i.e. the sum of the weighted squared deviations).

A

Variance of a Discrete Random Variable

55
Q

E(x) = μ

A

Expected Value

56
Q

Var(x) = σ^2

A

Variance

57
Q

sqrt(σ^2) = σ

A

Standard Deviation

58
Q

Name the 2 properties of a Poisson experiment.

A
  1. The probability of an occurrence is the same for any two intervals of equal length.
  2. The occurrence or nonoccurrence in any interval is independent of the occurrence or nonoccurrence in any other interval.
59
Q

A property of the _______ distribution is that the mean and variance are equal.

A

Poisson

60
Q

P(A U B) = P(A) + P(B) - P(A Π B)

A

Addition Law

61
Q

Name the 2 ways in which a hypergeometric probability distribution differs from the bionomial distribution.

A
  1. The trials are not independent.

2. The probability of success changes from trial to trial.

Business Statistics I (5 decks)

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