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Module 3: Financial Mathematics > Concepts (CH 3): Statistical Concepts and Market Returns > Flashcards

Concepts (CH 3): Statistical Concepts and Market Returns Flashcards

1
Q

refer to data and to the methods used to analyze data

A

statistics

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2
Q

what are the two statistical methods

A

descriptive & inferential

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3
Q

used to summarize the important characteristics of a large data set

A

descriptive

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4
Q

procedures used to make forecasts, estimates, or judgements about a large set of data on the basis of the statistical characteristics of a smaller set (a sample)

A

inferential

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5
Q

set of all possible members of a stated group

A

population

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6
Q

subset of the population of interest

A

sample

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7
Q

What are the types of measurement scales

A

nominal scales, ordinary scales, interval scales, ratio scales

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8
Q

observations are classified/ counted in no particular order (least information)

A

nominal scales

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9
Q
  • higher level of measurement than nominal scales
  • every observation is assigned to one of several categories
  • categories are ordered with respect to a specified characteristic
A

ordinary scales

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10
Q
  • provides relative ranking, like ordinary scales
  • differences between scale values are equal
A

interval scales

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11
Q

What is the weakness of the interval scale?

A

measurement of zero (0) does not necessarily indicate the total absence of what is being measures

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12
Q
  • most refined level of measurement
  • provides ranking and equal differences
    between scale values
  • have a true zero point as origin
A

ratio scales

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13
Q

measure used to describe a characteristic of a population

A

PARAMETER

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14
Q

measures characteristic of a sample

A

SIMPLE STATISTIC

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15
Q

- tabular presentation of statistical data that aids the analysis of large data sets
- summarizes statistical data by assigning
it to specified groups, or intervals
- data employes with a frequency distribution may be measured using any
type of measuryment scale

A

FREQUENCY DISTRIBUTION

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16
Q

in a frequency distribution, it is the interval with the greatest frequency

A

modal interval

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17
Q
  • useful way to present data
  • percentage of total observations falling within each interval
A

RELATIVE FREQUENCY

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18
Q

sum of the absolute or relative
frequency starting at the lower interval and progressing through the highest

A

CUMULATIVE ABSOLUTE FREQUENCY/CUMULATIVE RELATIVE FREQUENCY

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19
Q
  • graphical presentation of the absolute
    frequency distribution
  • bar chart of continuous data
  • allows us to see where most
    observations are concentrated
A

HISTOGRAM/FREQUENCY POLYGON

20
Q
  • the midpoint of each interval is plotted on the horizontal axis and the absolute frequency for that interval is plotted on the vertical axis
  • points are connected with a straight line
A

frequency polygon

21
Q
  • identify the center, or average of a data set
  • can be used to represent the typical or expected value in the data set
A

measures of central tendency

22
Q

all observed values in the population are summed and divided by the number of observations in the population

A

population mean

23
Q

sumofallthevaluesinasampleofa population divided by the number of observations in the sampler

A

sample mean

24
Q
  • most widely used measure of central tendency
  • only measure of central tendency for which the sum of the deviations from the mean is zero (0)
A

ARITHMETHIC MEAN

25
Q

recognizes that different observations may have a disproportionate influence on the mean

A

WEIGHTED MEAN

26
Q

value that occurs most frequently in a data set

A

MODE

27
Q
  • when a distribution has one value that appears most frequently
A

unimodal

28
Q

when a set of data has two or three values that occur most frequently

A

bimodal or trimodal

29
Q

is often used when calculating investment returns over multiple periods or when measuring compound growth rates

A

GEOMETRIC MEAN

30
Q
  • used for certain computations
  • such as the average cost of shares
    purchased over time
A

HARMONIC MEAN

31
Q

uncertain quantity/number

A

random variable

32
Q

an observed value of a random variable

A

outcome

33
Q

single outcome or a set of outcomes

A

event

34
Q

events that cannot both happen at the same time

A

mutually exclusive events

35
Q

those that include all possible outcomes

A

exhaustive events

36
Q

2 defining properties of probability

A
  1. probability of occurrence of any event
    is between 0 and 1
  2. if a set of events, E1, E2,… En, is
    mutually exclusive and exhaustive, the probability of those events sum to 1
37
Q

established by analyzing past data

A

empirical probability

38
Q

determined using a formal reasoning and inspection process

A

priori probability

39
Q
  • least formal method of developing probabilities
  • involves the use of personal judgment
A

subjective probability

40
Q

stating the odds that an event will or will not occur is an alternative way of expressing probabilities

A

ODDS FOR and AGAINST the
EVENT

41
Q
  • marginal probability
  • probability of an event regardless of the
    past or future occurrence of other events
A

unconditional probability

42
Q
  • occurrence of one event affects the other probability of the occurrence of another event
A

conditional probability

43
Q
  • the probability that they will both occur
  • can be calculated from the conditional
    probability that A will occur given B occurs (conditional) and the probability that B will occur (unconditional)
A

MULTIPLICATION RULE OF PROBABILITY

44
Q
  • used to determine the probability that at least one of the 2 events will occur
  • either A or B will occur
A

addition rule

45
Q
  • refer to events for which the occurrence of one does not influence the occurrence of the others
  • can be expressed in terms of conditional probabilities
A

INDEPENDENT EVENTS

46
Q
  • the occurrence of event A affects the probability of the occurrence of event B
A

DEPENDENT EVENTS

Module 3: Financial Mathematics (3 decks)

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