Module 1 Lesson 1 Flashcards
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.
STATISTICS
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.
ANALYTICS
Summaries of samples from
populations; methods for analyzing
samples
Statistics
Provides methods for designing
surveys and experiments, selecting
samples, and collecting data in a
representative manner.
Statistics
Summaries of batches of data;
methods for discovering patterns in
data
Analytics
Involves the use of tools and
techniques to gather, clean, and
preprocess large datasets efficiently.
Analytics
Involves measures of central
tendency and measures of
variability to summarize and
describe the main features of a
dataset.
Statistics
Utilizes summary statistics,
visualizations, and dashboards to
provide a comprehensive
understanding of the data.
Analytics
Uses statistical inference to make
predictions or inferences about a
population based on a sample. This
includes hypothesis testing and
confidence intervals.
Statistics
Utilizes regression models to
analyze relationships between
variables and make predictions.
Statistics
Involves the use of tools and
techniques to gather, clean, and
preprocess large datasets efficiently.
Analytics
Involves exploring data through
graphical representations
(histograms, scatter plots) and
numerical summaries.
Statistics
Leverages exploratory data analysis
techniques to uncover patterns,
trends, and outliers.
Analytics
Supply Chain Analytics: Maturity model stages
Descriptive
Diagnostic
Predictive
Prescriptive
Based on live data, tells what’s happening in real time
Descriptive
Accurate & Handy for operations management
Easy to visualize
Descriptive
Automated RCA - root cause analysis
Diagnostic
Explains why things are happening
Diagnostic
Helps troubleshoot isseus
Diagnostic
Tells what’s likely to happen
Predictive
Based on historical data, and assumes a static business plan/model
Predictive
Helps business decisions be automated using algorithms
Predicitve
Defines future actions: what to do next
Prescriptive
Based on current data analytics, predefined future plans, goals, and objectives
Prescriptive
Advanced algorithms to test potential outcomes of each decision and recommends the best course of action
Prescriptive
Modeling Purpose
Primarily aims to create a representation
that captures essential aspects of a system to gain
insights, analyze relationships, or facilitate decision-
making.
Modeling Output
Results in a conceptual or mathematical
representation of a system, such as equations,
diagrams, or graphical models.
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.
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.
Concerns about the
collection, organization and
presentation of
information under studied
Descriptive
Statistics
Concerns about the analysis
and interpretation of
information under studied
Inferential
Statistics
The researcher tries to
describe a situation under
study
Descriptive
Statistics
This implies before carrying
out an inference, appropriate
and correct descriptive
measures are employed to
bring out good results
Inferential
Statistics
Measures the value or
counts of data
Answers the question,
“how many or how
much?”
Quantitative
Describes the data as to
categories or groups
Answers the question,
“what type?”
Qualitative
Characterized by data that consist of
names, labels or categories only.
NOMINAL
Involves data that may be arranged in some
order, but differences between data values either
cannot be determined or are meaningless.
ORDINAL
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
At this level, inherent zero starting point
is important, and differences and ratios
are meaningful.
RATIO
The totality of all the
objects of a certain class
under consideration
Population
A finite number of
objects taken from the
population
Sample
A complete set of
individuals, objects or
measurements having
some common
observable
characteristics.`
Population
describes the whole
population
PARAMETER
describes a
sample of a given population
STATISTIC
is any quantity that make different values
Variable
A characteristics of data observed or measured that
may vary from person to person
Variable
Classification of variables acc. to functional relationships
independent; dependent
Classification of variables acc. to continuity of values
- Continuous
- Discrete
