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2-25. ISB 12C(2022)-A (Leclab) Intro To Business Analytics > Chapter 1-5 > Flashcards

Chapter 1-5 Flashcards

1
Q

The facts and figures collected, analyzed, and summarized for presentation and interpretation.

A

Data

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2
Q

Managers’ responsibility:

A

To make strategic, tactical, or operational decisions.

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3
Q

Involve higher-level issues concerned with the overall direction of the organization; Define the organization’s overall goals and aspirations for the future.

A

Strategic decisions

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4
Q

Concern how the organization should achieve the goals and objectives set by its strategy. ;Are usually the responsibility of midlevel management.

A

Tactical Decisions

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5
Q

Affect how the firm is run from day to day.; Are the domain of operations managers, who are the closest to the customer.

A

Operational Decisions

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6
Q

Decision making can be defined as the following process:

A
  1. Identify and define the problem.
  2. Determine the criteria that will be used to evaluate alternative solutions.
  3. Determine the set of alternative solutions.
  4. Evaluate the alternatives.
  5. Choose an alternative.
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7
Q

Common approaches to making decisions include:

A
  1. Tradition.
  2. Intuition.
  3. Rules of thumb.
  4. Using the relevant data available.
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8
Q

Scientific process of transforming data into insight for making better decisions.

A

Business Analytics

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9
Q

Used for data-driven or fact-based decision making, which is often seen as more objective than other alternatives for decision making.

A

Business Analytics

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10
Q

Encompasses the set of techniques that describes what has happened in the past

A

Descriptive analytics

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11
Q

A request for information with certain characteristics from a database.

A

Data Query

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12
Q

Collections of tables, charts, maps, and summary statistics that are updated as new data become available.

A

Data Dashboards

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13
Q

The use of analytical techniques for better understanding patterns and relationships that exist in large data sets.

A

Data Mining

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14
Q

The use of analytical techniques for better understanding patterns and relationships that exist in large data sets.

A

Data Mining

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15
Q

Consists of techniques that use models constructed from past data to predict the future or ascertain the impact of one variable on another.

A

Predictive Analysis

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16
Q

involves the use of probability and statistics to construct a computer model to study the impact of uncertainty on a decision.

A

Simulation

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17
Q
A
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18
Q

A characteristic or a quantity of interest that can take on different values.

A

Variable

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19
Q

A set of values corresponding to a set of variables.

A

Observation

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20
Q

The difference in a variable measured over observations.

A

Variation

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21
Q

A quantity whose values are not known with certainty.

A

Random variable/uncertain variable

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22
Q

Symbol, Industry, Share Price, and Volume

A

Variable

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23
Q

Time, customers, items, etc.

A

Variation

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24
Q

All elements of interest

A

Population

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25
Q

Subset of the population.

A

Sample

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26
Q

A sampling method to gather a representative sample of the population data.

A

Random sampling

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27
Q

Data on which numeric and arithmetic operations, such as addition, subtraction, multiplication, and division, can be performed.

A

Quantitative data

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28
Q

Data on which arithmetic operations cannot be performed.

A

Categorical Data

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29
Q

Scales of measurement include:

A
  • Nominal
  • Ordinal
  • Interval
  • Ratio
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30
Q

The scale determines the amount of information contained in the data.

A

Scales of measurement

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31
Q

The scale indicates the data summarization and statistical analyses that are most appropriate.

A

Scales of measurement

32
Q

Data are labels or names used to identify an attribute of the element.

A

Nominal

33
Q

A nonnumeric label or numeric code may be used.

A

Nominal

34
Q

1: Farley,
2: Keenan,
3: Zahm, and so on).

A

Numeric Code

35
Q

Farley,
Keenan,
Zahm,
Breen-Phillips,
and so on.

A

Nonnumeric Code

36
Q

The data have the properties of nominal data and the order or rank of the data is meaningful.

A nonnumeric label or numeric code may be used.

A

Ordinal

37
Q

The data have the properties of ordinal data, and the interval between observations is expressed in terms of a fixed unit of measure.

are always numeric.

A

Interval data

38
Q

The data have all the properties of interval data and the ratio of two values is meaningful.

A

Ratio

39
Q

Variables such as distance, height, weight, and time use the ratio scale.

A

Ratio

40
Q

This scale must contain a zero value that indicates that nothing exists for the variable at the zero point.

A

Ratio

41
Q

Data collected from several entities at the same, or approximately the same, point in time.

A

Cross-sectional data

42
Q

Data collected over several time periods.

A

Time series data

43
Q

A variable of interest is first identified.

Then one or more other variables are identified and controlled or manipulated so that data can be obtained about how they influence the variable of interest.

A

Experimental study

44
Q

Makes no attempt to control the variables of interest.

A survey is perhaps the most common type of observational study.

A

Nonexperimental study or observational study

45
Q

Existing Sources

A
  • Within a firm
  • Business database services
  • Government agencies
  • Industry associations
  • Special-interest organizations - - Admission Council
  • Collect your own
46
Q

Average value for a variable.

A

Mean

47
Q

Value in the middle when the data are arranged in ascending order.

A

Median

48
Q

Value that occurs most frequently in a data set.

A

Mode

49
Q

The data are spread fairly evenly

A

Mean

50
Q

The data set has an outlier

A

Median

51
Q

The data involve a subject in which many data points of one value are important, such as election results.

A

Mode

52
Q
  • A measure of location that is calculated by finding the nth root of the product of n values.
  • Used in analyzing growth rates in financial data
A

Geometiric Mean

53
Q

A summary of data that shows the number (frequency) of observations in each of several nonoverlapping classes.

A

Frequency Distribution

54
Q

A tabular summary of data showing the relative frequency for each bin.

A

Relative frequency distribution

55
Q

is used to provide estimates of the relative likelihoods of different values of a random variable.

A

Percent Frequency Distribution

56
Q

are formed by specifying the ranges used to group the data.

A

Bins

57
Q

Three steps necessary to define the classes for a frequency distribution with quantitative data:

A
  1. Determine the number of nonoverlapping bins.
  2. Determine the width of each bin.
  3. Determine the bin limits.
58
Q

Formula of Bin

A

Largest data value - smallest data value / number of bins

59
Q

A common graphical presentation of quantitative data.

A

Histogram

60
Q

Constructed by placing the variable of interest on the horizontal axis and the selected frequency measure (absolute frequency, relative frequency, or percent frequency) on the vertical axis.

A

Histogram

61
Q
  • Lack of symmetry.
  • is an important characteristic of the shape of a distribution.
A

Skewness

62
Q

can be found by subtracting the smallest value from the largest value in a data set.

A

Range

63
Q

is a measure of variability that utilizes all the data.

A

Variance

64
Q

It is based on the deviation about the mean, which is the difference between the value of each observation (xi) and the mean.

A

Variance

65
Q

is the positive square root of the variance.

A

Standard Deviation

66
Q

is a descriptive statistic that indicates how large the standard deviation is relative to the mean.

Expressed as a percentage.

A

coefficient of variation

67
Q

When the data is divided into four equal parts:

  • Each part contains approximately 25% of the observations.
  • Division points are referred to as quartiles.
A

Quartiles

68
Q

The difference between the third and first quartiles is often referred to as the ___________________.

A

interquartile range, or IQR

69
Q

measures the relative location of a value in the data set.

A

Z-score

70
Q

Helps to determine how far a particular value is from the mean relative to the data set’s standard deviation.

A

Z-score

71
Q

Often called the standardized value.

A

Z-score

72
Q

can be used to determine the percentage of data values that are within a specified number of standard deviations of the mean.

A

empirical rule

73
Q

is a graphical summary of the distribution of data.
Developed from the quartiles for a data set.

A

box plot

74
Q

Extreme values in a data set.

A

Outliers

75
Q

is a descriptive measure of the linear association between two variables

A

Covariance

76
Q
A

2-25. ISB 12C(2022)-A (Leclab) Intro To Business Analytics (2 decks)

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