Module 1 Lesson 1 Flashcards

(48 cards)

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

21
Q

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

22
Q

Helps business decisions be automated using algorithms

23
Q

Defines future actions: what to do next

24
Q

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

25
Advanced algorithms to test potential outcomes of each decision and recommends the best course of action
Prescriptive
26
Modeling Purpose
Primarily aims to create a representation that captures essential aspects of a system to gain insights, analyze relationships, or facilitate decision- making.
27
Modeling Output
Results in a conceptual or mathematical representation of a system, such as equations, diagrams, or graphical models.
28
Simulation Purpose
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
Simulation Output
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
Concerns about the collection, organization and presentation of information under studied
Descriptive Statistics
31
Concerns about the analysis and interpretation of information under studied
Inferential Statistics
32
The researcher tries to describe a situation under study
Descriptive Statistics
33
This implies before carrying out an inference, appropriate and correct descriptive measures are employed to bring out good results
Inferential Statistics
34
Measures the value or counts of data Answers the question, “how many or how much?”
Quantitative
35
Describes the data as to categories or groups Answers the question, “what type?”
Qualitative
36
Characterized by data that consist of names, labels or categories only.
NOMINAL
37
Involves data that may be arranged in some order, but differences between data values either cannot be determined or are meaningless.
ORDINAL
38
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.
INTERVAL
39
At this level, inherent zero starting point is important, and differences and ratios are meaningful.
RATIO
40
The totality of all the objects of a certain class under consideration
Population
41
A finite number of objects taken from the population
Sample
42
A complete set of individuals, objects or measurements having some common observable characteristics.`
Population
43
describes the whole population
PARAMETER
44
describes a sample of a given population
STATISTIC
45
is any quantity that make different values
Variable
46
A characteristics of data observed or measured that may vary from person to person
Variable
47
Classification of variables acc. to functional relationships
independent; dependent
48
Classification of variables acc. to continuity of values
1. Continuous 2. Discrete