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prelims > BUSAL > Flashcards

BUSAL Flashcards

1
Q

Value states that benefits outweighs the costs (T or F)

A

True

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2
Q

A coordinated, standardized set of activities conducted by both people and equipment to accomplish a specific business task

A

Business Process

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3
Q

A data specialist who curates and uses data to help an organization make effective business decisions

A

Business Analyst

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4
Q

raw facts that have little meaning on their own

A

Data

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5
Q

data organized in a way to be useful to the analyst or user combining data with context

A

Information

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6
Q

setting, event, statement, or situation

A

Context

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7
Q

conclusion reached after consideration of knowledge is considered

A

Decisions

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8
Q

Needs knowledge and information to make decisions

A

Decision Maker

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8
Q

understanding or familiarity with information gained

A

Knowledge

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9
Q

One that knows business, knows what data is needed, and knows how to communicate with both the decision maker and the data scientist

A

Business/Data Analyst

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9
Q

Interpreter or Liaison

A

Business/Data Analyst

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10
Q

A specialist who knows how to work with, manipulate, and statistically test data

A

Data Scientist

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10
Q

O in the SOAR analytics model

A

Obtain the Data

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11
Q

act or business of promoting and selling products or services

A

Marketing

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11
Q

measures and attempts to improve its marketing performance

A

Marketing analytics

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11
Q

R in the SOAR analytics model

A

Report the results

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11
Q

Defined as the use of data to create knowledge, to help draw conclusions, and address business questions

A

Business Analytics

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11
Q

most important component of marketing analytics is providing insights into customer preferences and trends (T or F)

A

True

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12
Q

works to measure, record, and communicate financial performance to decision makers, including shareholders, management, customers, suppliers, and regulators

A

Accounting/ Accounting Analytics

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13
Q

management of money by investing, borrowing, lending, budgeting, saving, and forecasting financial capital (money)

A

Finance/financial analytics

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13
Q

includes an evaluation of a company’s human resource (evaluation of employee efficiency and turnover), IT operations, and supply chain

A

Operations/operations analytics

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13
Q

An analytics mindset is the ability to:

A

Ask the right questions;

Extract, transform, and load relevant data;

Apply appropriate data analytic techniques;

Interpret and share the results with stakeholders

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14
Q

S in the SOAR analytics model

A

Specify the question

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15
Q

A in the SOAR analytics model

A

Analyze the data

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16
Q

“Which data needs to be collected?” SOAR Model

A

Obtain the Data/O

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16
Q

“What is the best way to communicate what we’ve found in our data analysis?” SOAR Model

A

Report the results/R

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16
Q

Questioning the situation SOAR Model

A

Specify the question/S

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17
Q

Defined as graphic representation of data, usually in the form of a graph, chart, or other image

A

Data Visualization

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17
Q

A type of data visualization that is part of the “Analyze the Data” step of the SOAR analytics model

A

Exploratory Data Visualizations

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18
Q

Useful for uncovering patterns and useful insights in the data, generally as part of descriptive or diagnostic analytics

A

Exploratory Data Visualizations

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18
Q

A type of data visualization that is part of the “Report the Results” step of the SOAR analytics model

A

Explanatory Data Visualizations

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19
Q

Important means of reporting the findings of the business analytics to stakeholders

A

Explanatory Data Visualizations

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19
Q

Science that deals with collection, analysis, and interpretation of data

A

Statistics

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20
Q

Totality of objects under investigation

A

Population

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20
Q

Characteristics that is being studied

A

Variable

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21
Q

Subset of a population

A

Sample

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22
Q

Numerical description of sample

A

Parameter

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23
Q

Numerical description of sample

A

Statistic

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24
Q

Ex. A 2016 survey found out that 50% of millennials plan to stay at their current job for more than a year

What is the parameter in the scenario?

A

millenials

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25
Q

Ex. A 2016 survey found out that 50% of millennials plan to stay at their current job for more than a year

What is the statistic in the scenario?

A

50%

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26
Q

A kind of variable that is considered as any controlling data

A

Independent Variable

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27
Q

Any data that is affected by the controlling data

A

Dependent

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28
Q

Affects the relationship between a predictor variable, and an outcome variable

A

Moderating Variable

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29
Q

An intervening variable which explains relationship between a predictor variable and criterion variable

A

Mediating Variable

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30
Q

Ex. To predict the value of sunlight on the growth of a certain plant

What is the dependent variable in the situation?

A

growth of a certain plant

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31
Q

Ex. To predict the value of sunlight on the growth of a certain plant

What is the independent variable in the situation?

A

value of sunlight

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32
Q

Consists of methods for organizing, displaying, and describing data by using tables graph and summary

A

Descriptive Statistics

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33
Q

Consists of methods that use sample results to help make predictions about a population

A

Inferential Statistics

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34
Q

Compilation of facts, and figures, or other contents, both numerical and non-numerical

A

Data

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35
Q

Data that have been organized, analyzed, and processed in a meaningful and purposeful way

A

Information

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36
Q

Derived from a blend of data, contextual information, experience, and intuition

A

Knowledge

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37
Q

Information which is gathered directly from the original source

A

Primary Data

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38
Q

Information which is taken from the secondary source

A

Secondary Data

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39
Q

Types of Data (According to Source)

A

Primary Data and Secondary Data

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40
Q

Types of Data (According to Function)

A

Qualitative Data, Quantitative Data, and Continuous Data

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41
Q

Consist of attributes, labels or non numeric entries; categorical

A

Qualitative Data

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42
Q

Consist of numerical data, measurements, or counts; Numerical

A

Quantitative Data

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43
Q

Data which can be counted using integral values

A

Discrete Data

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44
Q

Data which can assume any numerical value over an interval or intervals

A

Continuous Data

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45
Q

An example of this data is the number of sales

A

Discrete Data

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46
Q

An example of this data are rankings

A

Continuous Data

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47
Q

Types of Data (According to Format)

A

Structured Data, Unstructured Data, Human or Machine-generated, and Big Data

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48
Q

Reside in a pre-defined, row-column format
Spreadsheet or database applications

A

Structured Data

49
Q

Numerical information that is objective and not open to interpretation

A

Structured Data

50
Q

Do not conform to a pre-defined, row-column format

A

Unstructured Data

51
Q

email, text, social media, presentations

A

Unstructured human

52
Q

satellite images, video data, camera images

A

Unstructured machine

53
Q

sensors, speed cameras, web server logs

A

Structured machine

54
Q

price, income, retail sales

A

Structured human

55
Q

A massive volume of structured and unstructured data

A

Big Data

55
Q

immense amount of data compiled for a single or multiple sources

A

Volume

56
Q

all types, forms, granularity, structure, or unstructured

A

Variety

57
Q

generated at a rapid speed, management is a critical issue

A

Velocity

58
Q

credibility and quality of the data, reliability

A

Veracity

59
Q

methodological plan for formulating questions, curating the right data, and unlocking hidden potential

A

Values

60
Q

categorized using names, labels, or qualities and cannot be arranged in any particular order

A

Nominal Scale (Categorical)

61
Q

Can be arranged in order but differences between data entries are not meaningful

A

Ordinal Scale (Categorical)

62
Q

Has a limit of measurement that data permits us to describe how much more or less one object possesses than another; A zero entry simply represents a position on a scale

A

Interval Scale (Numerical)

63
Q

A zero entry is an inherent zero; Modified internal level

A

Ratio Scale (Numerical)

64
Q

data organized into sets of columns (fields) and rows (records)

A

Tables

65
Q

columns that contain descriptive information about the observations in the table (including primary and foreign keys)

A

Fields

66
Q

rows in a table; each row, or record, corresponds to a unique instance of what is being described in the table

A

Records

67
Q

efficient means of storing data in one place, in one table instead of multiple places

A

Relational databases

68
Q

unique identifier in each table

A

Primary Key

69
Q

exist to create relationships or links between two tables

A

Foreign Key

70
Q

Data structured into rows and columns

A

Tabular Data

71
Q

each column starts and ends in the same place in every row

A

Fixed-width Format:

72
Q

a delimiter separates fields, typically comma (CSV file)

A

Delimited Format:

73
Q

structured data, each piece enclosed in a pair of tags, gives information on what the data are

A

Extensible Markup Language (XML)

74
Q

structured data with tags, gives information on how to display the data

A

HyperText Markup Language (HTML)

75
Q

alternative to XML, transmit human-readable data in compact files, not as verbose as XML, supports wide range of data types, parsing is faster

A

JavaScript Object Notation (JSON)

76
Q

Social Media Data, Census Data, Small Business Administration Data, Publicly Available Data, Financial Statements of all publicly traded companies, Stock price data, and Summarized financial data are examples of external data sources (T or F)

A

True

77
Q

Data already processed and transformed

A

Aggregated Data

78
Q

Give the analyst the flexibility to process data as they see fit

A

Raw Data

79
Q

method where there is a person-to-person interaction, an exchange of idea between the one soliciting information and the one that is supplying the information

A

Interview

80
Q

Known as the paper and pencil method, an alternative to interview method.

A

Survey

81
Q

A documentary analysis wherein data are gathered from fact or information on file

A

Registration

82
Q

Applied to gather data if the researcher wants to control the factors affecting the variable being studied

A

Experimentation

82
Q

Utilized to gather data regarding attitudes, behavior, cultural patterns of the samples under investigation

A

Observation

83
Q

Usually done through qualitative or mixed research

A

Experimentation

84
Q

It is being applied once the entire elements of the population are not available or the population is too large

A

Sampling

85
Q

Every member of the population has an equal chance of being selected

A

Simple Random Sampling

85
Q

Involves randomly selecting participants from population to obtain a representative sample

A

Probability Sampling

86
Q

Involves dividing the population into homogeneous subgroup called –

A

strata

86
Q

Involves selecting every nth individual from a population; the first individual is selected randomly, and then the remaining individuals are selected systematically

A

Systematic Sampling

86
Q

Involves dividing the population into homogeneous subgroup called strata, and then selecting random sample from each –

A

Stratum

87
Q

Involves dividing the population into homogeneous subgroup called strata, and then selecting random sample from each stratum

A

Stratified Sampling

87
Q

An example of this sampling technique are graduates or undergraduates

A

Stratified Sampling

87
Q

Involves dividing the population into clusters or groups, and then selecting a random sample of clusters; each selected cluster is then sampled in its entirety

A

Cluster Sampling

87
Q

Participants are selected until the quota is reached, but the selection of individuals within each quota group is non-random

A

Quota Sampling

87
Q

An example of this sampling technique is getting one from a program (1 student from HR)

A

Cluster Sampling

87
Q

Involves selecting participants based on factors other than random selection, such as convenience or willingness to participate

A

Non-Probability Sampling

88
Q

Participants are selected based on their availability or accessibility

A

Convenience Sampling

89
Q

are numerical values that indicate how much or how many

A

Quantitative Data

89
Q

To get the number of classes:

A

Largest Data Value - Lowest Data Value

89
Q

Initial participants are selected through a non-probability method, and they are asked to refer other individuals they know who meet the criteria for participation

A

Snowball or Respondent Driven Sampling

89
Q

use labels or names to identify categories of like items

A

Categorical Data

89
Q

A tabular summary of data showing the number of observations in each of several non-overlapping categories or classes

A

Frequency Distribution

90
Q

Elements of Frequency Distribution

A

Number of Classes
Class Limits
Class Boundaries
Class Size (Class Width)
Class Boundaries
Class Mark (Midpoint)

90
Q

3 ways to calculate sample size:

A

By percentage
By Slovin’s Formula
By Cochran’s Formula

90
Q

Classes are formed by:

A

specifying ranges that will be used to group the data

91
Q

To get class boundaries: (Lower)

A

minus 0.5

91
Q

To get class boundaries: (Upper)

A

Plus 0.5

91
Q

To get class midpoint:

A

finding the average of the lower class limit and the upper class limit

Ex. Class Limit: 12 - 33
Class Mark: (12+33)/2 = 22.5

92
Q

Totality of frequency

A

CUMULATIVE FREQUENCY

92
Q

A graphical presentation of the relationship between two quantitative variables

A

Scatter Diagram

92
Q

shows the frequency distribution or relative frequency distribution categorical data

A

Bar Chart

92
Q

Provides an approximation of the relationship

A

Trendline

92
Q

Refers to the difference between the upper class boundary and the lower class boundary

A

Class Size (Class Width)

Ex. Class Boundaries = 11.5 - 33.5
Class Size = 33.5 - 11.5 = 22.5/ 5 = 4.4

93
Q

Pie Chart

A

show the relative frequency or percent frequency for categorical data

93
Q

Dot Plot

A

show the distribution for quantitative data over the entire range of the data

93
Q

Histogram

A

show the frequency distribution for quantitative data over a set of class intervals

93
Q

Stem-and-Leaf Display

A

show both the rank order and shape of the distribution for quantitative data

93
Q

measures are computed for data from a sample

A

sample statistics

93
Q

measures are computed for data from a population

A

population parameters

93
Q

sample statistic

A

point estimator

93
Q

2 types of descriptive statistics

A

Measures of Location/Central Tendency and Measures of Variability/Dispersion

94
Q

The most important measure of location; Provides a central location

A

Mean

95
Q

The sample mean

A

point estimator

95
Q

Select participants who are knowledgeable about the research topic or have experienced a particular phenomenon of interest

A

Purposive Sampling

95
Q

Data that has two modes

A

bimodal

95
Q

Data that has more than 2 modes

A

multimodal

95
Q

Value that occurs with greatest frequency

A

Mode

96
Q

In some instance, the mean is computed by giving each observation a weight that reflects its relative importance

A

Weighted Mean

96
Q

Calculated by finding the nth root of the product of n values

A

Geometric Mean

97
Q

Should be applied anytime you want to determine the mean rate of change over several successive periods

A

Geometric Mean

97
Q

Often used in analyzing growth rates in financial data

A

Geometric Mean

98
Q

Often desirable to consider measures of variability (dispersion) as well as measures of location

A

Measures of Variability

98
Q

Provides information about how the data are spread over the interval from the smallest value to the largest value

A

Percentiles

98
Q

Overcomes the sensitivity to extreme data values

A

Interquartile Range

99
Q

Simplest measure of variability

A

Range

99
Q

Difference between the largest and smallest data values

A

Range

100
Q

Difference between the third quartile and the first quartile

A

Interquartile Range

100
Q

Based on the difference between the value of each observation (X1) and the mean (for a sample for a population)

A

Variance

100
Q

Average of the squared differences between each data value and the mean

A

Variance

100
Q

Indicates how large the standard deviation is in relation to the mean

A

Coefficient of Variation

101
Q

Positive square root of the variance

A

Standard Deviation

prelims (4 decks)

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