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Quanti > Module 1 Lesson 1 > Flashcards

Module 1 Lesson 1 Flashcards

1
Q

It includes techniques by which data are
collected, organized, presented, analyzed
and interpreted in order to formulate
inferences. The focal point is the process
of decision making.

A

STATISTICS

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2
Q

It is the science of analyzing raw data in
order to make conclusions about that
information. Many of the techniques and
processes of this have been
automated into mechanical processes
and algorithms that work over raw data.

A

ANALYTICS

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3
Q

Summaries of samples from
populations; methods for analyzing
samples

A

Statistics

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4
Q

Provides methods for designing
surveys and experiments, selecting
samples, and collecting data in a
representative manner.

A

Statistics

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5
Q

Summaries of batches of data;
methods for discovering patterns in
data

A

Analytics

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6
Q

Involves the use of tools and
techniques to gather, clean, and
preprocess large datasets efficiently.

A

Analytics

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7
Q

Involves measures of central
tendency and measures of
variability to summarize and
describe the main features of a
dataset.

A

Statistics

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8
Q

Utilizes summary statistics,
visualizations, and dashboards to
provide a comprehensive
understanding of the data.

A

Analytics

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9
Q

Uses statistical inference to make
predictions or inferences about a
population based on a sample. This
includes hypothesis testing and
confidence intervals.

A

Statistics

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10
Q

Utilizes regression models to
analyze relationships between
variables and make predictions.

A

Statistics

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11
Q

Involves the use of tools and
techniques to gather, clean, and
preprocess large datasets efficiently.

A

Analytics

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12
Q

Involves exploring data through
graphical representations
(histograms, scatter plots) and
numerical summaries.

A

Statistics

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13
Q

Leverages exploratory data analysis
techniques to uncover patterns,
trends, and outliers.

A

Analytics

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14
Q

Supply Chain Analytics: Maturity model stages

A

Descriptive
Diagnostic
Predictive
Prescriptive

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15
Q

Based on live data, tells what’s happening in real time

A

Descriptive

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16
Q

Accurate & Handy for operations management

Easy to visualize

A

Descriptive

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17
Q

Automated RCA - root cause analysis

A

Diagnostic

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18
Q

Explains why things are happening

A

Diagnostic

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19
Q

Helps troubleshoot isseus

A

Diagnostic

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20
Q

Tells what’s likely to happen

A

Predictive

21
Q

Based on historical data, and assumes a static business plan/model

A

Predictive

22
Q

Helps business decisions be automated using algorithms

A

Predicitve

23
Q

Defines future actions: what to do next

A

Prescriptive

24
Q

Based on current data analytics, predefined future plans, goals, and objectives

A

Prescriptive

25
Q

Advanced algorithms to test potential outcomes of each decision and recommends the best course of action

A

Prescriptive

26
Q

Modeling Purpose

A

Primarily aims to create a representation
that captures essential aspects of a system to gain
insights, analyze relationships, or facilitate decision-
making.

27
Q

Modeling Output

A

Results in a conceptual or mathematical
representation of a system, such as equations,
diagrams, or graphical models.

28
Q

Simulation Purpose

A

Primarily aims to observe the dynamic
behavior of a system under different conditions or
scenarios to understand its performance, test
hypotheses, or optimize processes.

29
Q

Simulation Output

A

Generates output based on the execution
of a model, providing insights into the system’s
behavior over time. Output may include time-series
data, statistics, or visual representations.

30
Q

Concerns about the
collection, organization and
presentation of
information under studied

A

Descriptive
Statistics

31
Q

Concerns about the analysis
and interpretation of
information under studied

A

Inferential
Statistics

32
Q

The researcher tries to
describe a situation under
study

A

Descriptive
Statistics

33
Q

This implies before carrying
out an inference, appropriate
and correct descriptive
measures are employed to
bring out good results

A

Inferential
Statistics

34
Q

Measures the value or
counts of data
Answers the question,
“how many or how
much?”

A

Quantitative

35
Q

Describes the data as to
categories or groups
Answers the question,
“what type?”

A

Qualitative

36
Q

Characterized by data that consist of
names, labels or categories only.

A

NOMINAL

37
Q

Involves data that may be arranged in some
order, but differences between data values either
cannot be determined or are meaningless.

A

ORDINAL

38
Q

Like the ordinal, with the additional property that
meaningful amounts of differences between data
can be determined. However, no inherent zero
starting point is used.

A

INTERVAL

39
Q

At this level, inherent zero starting point
is important, and differences and ratios
are meaningful.

A

RATIO

40
Q

The totality of all the
objects of a certain class
under consideration

A

Population

41
Q

A finite number of
objects taken from the
population

A

Sample

42
Q

A complete set of
individuals, objects or
measurements having
some common
observable
characteristics.`

A

Population

43
Q

describes the whole
population

A

PARAMETER

44
Q

describes a
sample of a given population

A

STATISTIC

45
Q

is any quantity that make different values

A

Variable

46
Q

A characteristics of data observed or measured that
may vary from person to person

A

Variable

47
Q

Classification of variables acc. to functional relationships

A

independent; dependent

48
Q

Classification of variables acc. to continuity of values

A
  1. Continuous
  2. Discrete

Quanti (2 decks)

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