Glossary Flashcards
5S
workplace organization method promoting efficiency
and effectiveness; five terms based on Japanese
words for: sorting, set in order, systematic cleaning,
standardizing, and sustaining
5 Whys
iterative process of discovery through repetitively asking
‘why’; used to explore cause and effect relationships
underlying and/or leading to problem
80/20 Rule
AKA the Pareto principle: roughly 80% of results come
from 20% of effort
Accuracy
quality or state of being correct or precise, or the degree
to which the result of a measurement, calculation, or
specification conforms to the correct value or standard
Activity-based costing
method of assigning costs to products or services on
the resources that they consume
Agent-based modeling
a class of computation models for simulating actions and interactions of autonomous agents with a view to assessing their effects on the system as a whole
Algorithm
set of specific steps to solve a problem
Amortization
allocation of cost of an item or items over a time period
such that the actual cost is recovered; often used to
account for capital expenditures
Analytics
scientific process of transforming data into insight for
making better decisions
Analytics professional
person capable of making actionable decisions through
the analytic process; also a person holding the Certified
Analytics Professional (CAP®) credential
ANCOVA
Analysis of Covariance
ANOVA
Analysis of Variance
Artificial Intelligence
branch of computer science that studies and develops
intelligent machines and software
Artificial Neural
Networks
computer-based models inspired by animal central
nervous systems
Assemble-to-Order
manufacturing process where products are assembled as
they are ordered; characterized by rapid production and
customization
Assignment problem
one of the fundamental combinatorial optimization problems in the branch of optimization or operations research in mathematics;
Used to understand optimal way to assign n resources to n tasks in the most efficient way possible;
Consists of finding a maximum-
weight matching in a weighted bipartite graph
http://www.math.harvard.edu/archive/20_spring_05/handouts/assignment_overheads.pdf
Automation
use of mechanical means to perform work previously
done by human effort
Average
sum of a range of values divided by the number of
values to arrive at a value characteristic of the midpoint
of the range; see also, Mean
Batch production
method of production where components are produced
in groups rather than a continual stream of production;
see also, Continuous production
Benchmarking
act of comparison against a standard or the behavior of
another in attempt to determine degree of conformity to standard or behaviour
Benchmark problems
comparison of different algorithms using a large test set
Bias
a tendency for or against a thing, person, or group in
a way as to appear unfair; in statistics, data calculated
so that it is systematically different from the population
parameter of interest
Big data
data sets too voluminous or too unstructured to be analyzed by traditional means
Box-and-whisker plot
a simple way of representing statistical data on a plot
in which a rectangle is drawn to represent the second
and third quartiles, usually with a vertical line inside
to indicate the median value. The lower and upper
quartiles are shown as horizontal lines either side of the
rectangle
Branch-and-Bound
a general algorithm for finding optimal solutions of
various optimization problems; consists of a system
enumeration of all candidate solutions where large
subsets of fruitless candidates are discarded en masse
using upper and lower estimated bounds of the quantity
being optimized
Business analytics
refers to the skills, technologies, applications, and
practices for continuous iterative exploration and
investigation of past business performance to gain
insight and drive business planning; can be descriptive,
prescriptive, or predictive; focuses on developing new
insights and understanding of business performance
based on data and statistical methods
Business case
reasoning underlying and supporting the estimates of
business consequences of an action
Business intelligence
a set of methodologies, processes, architectures, and
technologies that transform raw data into meaningful
and useful information
Business Process
Modeling or Mapping
(BPM)
act of representing processes of an enterprise so that the
current process may be analyzed and improved; typically
action performed by business analysis and managers
seeking improved efficiency and quality
Chief Analytics Officer
CAO
possible title of one overseeing analytics for a company;
may include mobilizing data, people, and systems
for successful deployment, working with others to
inject analytics into company strategy and decisions,
supervising activities of analytical people, consulting
with internal business functions and units so they may
take advantage of analytics, contracting with external
providers of analytics
Chi-squared
Automated
Interaction
Detection (CHAID)
a technique for performing decision tree analysis
developed by Gordon V. Kass. CHAID is one of several
commonly used techniques for decision trees and
is based upon hypothesis testing using Bonferroni
correction
Classification
assortment of items or entities into predetermined
categories
Cleansing
AKA cleaning or scrubbing: the process of detecting and
correcting (or removing) corrupt or inaccurate records
from a record set, table, or database; may also involve
harmonization of data, and standardization of data
Clustering
grouping of a set of objects in such a way that objects
in the same group (cluster) are more similar to each
other than to those in other groups or clusters
Combinatorial
optimization
a topic that consists of finding an optimal object from a
finite series of objects; used in applied mathematics and
theoretical computer science
Confidence interval
a type of interval estimate of a population parameter
used to indicate the reliability of an estimate. It is
an observed interval (i.e., it is calculated from the
observations), in principle different from sample
to sample, that frequently includes the parameter
of interest if the experiment is repeated
Confidence level
if confidence intervals are constructed across many
separate data analyses of repeated (and possibly
different) experiments, the proportion of such intervals
that contain the true value of the parameter will match
the confidence level
Conjoint analysis
allows calculation of relative importance of varying
features and attributes to customers
Constraint
a condition that a solution to an optimization problem
is required by the problem itself to satisfy. There
are several types of constraints—primarily equality
constraints, inequality constraints, and integer
constraints
Constraint Programming
a programming paradigm wherein relations between
variables are stated in the form of constraints
Continuous Production
method of production where components are produced
in a continuous stream
Correlation
a broad class of statistical relationships involving dependence
Cost of capital
the cost of funds used for financing a business. Cost
of capital depends on the mode of financing used—it
refers to the cost of equity if the business is financed
solely through equity, or to the cost of debt if it is
financed solely through debt
Cumulative density
function
probability that a real-valued random variable X with a
given probability distribution will be found at a value
less than or equal to x; used to specify the distribution of
multivariate random variables
Cutting stock
problem
optimization or integer linear programming problem
arising from applications in industry where high
production problems exist
Data
(plural form of datum) values of qualitative or
quantitative variables, belonging to a set of items;
represented in a structure, often tabular (represented
by rows and columns), a tree (a set of nodes with
parent-children relationship), or a graph structure (a
set of interconnected nodes); typically the results of
measurements
Data mining
relatively young and interdisciplinary field of computer
science; the process of discovering new patterns from
large data sets involving methods at the intersection of
artificial intelligence, machine learning, statistics, and
database systems; see also, KDD
Data warehouse
a central repository of data that is created by integrating
data from one or more disparate sources; used for
reporting and data analysis
Database
an organized collection of data organized to model
relevant aspects of reality to support processes requiring
this information
Decision tree
graphic illustration of how data leads to decision when
branches of the tree are followed to their conclusion;
different branches may lead to different decisions
Decision variables
a decision variable represents a problem entity for
which a choice must be made. For instance, a decision
variable might represent the position of a queen on a
chessboard, for which there are 100 different possibilities
(choices) on a 10x10 chessboard or the start time of an
activity in a scheduling problem. Each possible choice
is represented by a value, hence the set of possible
choices constitutes the domain that is associated with a
variable
Descriptive analytics
prepares and analyzes historical data to identify
patterns for reporting trends
Design of
experiments
design of any information gathering exercise where
variation is present, whether under the control of the
experimenter or not; see also, Experimental design
Discrete event
simulation
models the operation of a system as a discrete sequence
of events in time; between events, no change in the
system is assumed thus a simulation can move in time
from one event to the next
Dynamic
programming
based on the Principle of Optimality, this was originally
concerned with optimal decisions over time. For
continuous time, it addresses problems in variational
calculus. For discrete time, each period is sometimes
called a stage, and the DP is called a multistage decision
process. Here is the Fundamental Recurrence Equation
for an additive process:
F(t, s) = Opt{r(t, s, x) + aF(t’, s’): x in X(t, s) and s’=T(t,
s, x)},
Effective domain
the domain of a function for which its value is finite
Efficiency
the comparison of what is actually produced or
performed with what can be achieved with the same
consumption of resources (money, time, labor, etc.). It
is an important factor in determination of productivity
Engagement
an estimate of the depth of visitor interaction against a
clearly defined set of goals; may be measured through
analytical models
Enterprise resource
planning (ERP)
a cross-functional enterprise system driven by an
integrated suite of software modules that supports the
basic internal business processes of a company
ETL (extract,
transform, load)
refers to three separate functions combined into a single
programming tool. First, the extract function reads data
from a specified source database and extracts a desired
subset of data. Next, the transform function works with
the acquired data—using rules or lookup tables, or
creating combinations with other data—to convert it to
the desired state. Finally, the load function is used to
write the resulting data (either all of the subset or just
the changes) to a target database, which may or may
not previously exist
Experimental design
in quality management, a written plan that describes the
specifics for conducting an experiment, such as which
conditions, factors, responses, tools, and treatments are
to be included or used; see also, Design of experiments
Expert systems
a computer program that simulates the judgment and
behavior of a human or an organization that has expert
knowledge and experience in a particular field. Typically,
such a system contains a knowledge base containing
accumulated experience and a set of rules for applying
the knowledge base to each particular situation that is
described to the program
Factor analysis
a statistical method used to describe variability among
observed, correlated variables in terms of a potentially
lower number of unobserved variables called factors.
Factor analysis searches for such joint variations in
response to unobserved latent variables
Failure Mode and
Effects Analysis
(FMEA)
a systematic, proactive method for evaluating a
process to identify where and how it might fail, and
to assess the relative impact of different failures to
identify the parts of the process that are most in need
of change
Fixed cost
a cost that is some value, say C, regardless of the level as
long as the level is positive; otherwise the fixed charge
is zero. This is represented by Cv, where v is a binary
variable. When v = 0, the fixed charge is 0; when v = 1,
the fixed charge is C. An example is whether to open
a plant (v = 1) or not (v = 0). To apply this fixed charge
to the non-negative variable x, the constraint x <= Mv
is added to the mathematical program, where M is a
very large value, known to exceed any feasible value
of x. Then, if v = 0 (e.g., not opening the plant that is
needed for x > 0), x = 0 is forced by the upper bound
constraint. If v = 1 (e.g., plant is open), x <= Mv is a
redundant upper bound. Fixed charge problems are
mathematical programs with fixed charges
Forecasting
the use of historic data to determine the direction of
future trends
Fuzzy logic
a form of mathematical logic in which truth can
assume a continuum of values between 0 and 1
Game Theory
in general, a (mathematical) game can be played by
one player, such as a puzzle, but its main connection
with mathematical programming is when there are at
least two players, and they are in conflict. Each player
chooses a strategy that maximizes his payoff. When
there are exactly two players and one player’s loss is the
other’s gain, the game is called zero sum. In this case, a
payoff matrix A is given where Aij is the payoff to player
1, and the loss to player 2, when player 1 uses strategy
i and player 2 uses strategy j. In this representation
each row of A corresponds to a strategy of player 1,
and each column corresponds to a strategy of player 2.
If A is m × n, this means player 1 has m strategies, and
player 2 has n strategies
Genetic algorithms
a class of algorithms inspired by the mechanisms of
genetics, which has been applied to global optimization
(especially for combinatorial programs). It requires
the specification of three operations (each is typically
probabilistic) on objects, called “strings”
Global optimal
refers to mathematical programming without convexity
assumptions, which are NP-hard. In general, there could
be a local optimum that is not a global optimum. Some
authors use this term to imply the stronger condition
there are multiple local optima. Some solution strategies
are given as heuristic search methods (including those
that guarantee global convergence, such as branch
and bound). As a process associated with algorithm
design, some regard this simply as attempts to assure
convergence to a global optimum (unlike a purely
local optimization procedure, like steepest ascent).
Goodness of fit`
degree of assurance or confidence to which the results
of a sample survey or test can be relied upon for making
dependable projections. Described as the degree of
linear correlation of variables, it is computed with the
statistical methods such as chi-square test or coefficient
of determination
Graphical User Interface (GUI)
a human–computer interface (i.e., a way for humans to
interact with computers) that uses windows, icons, and
menus, and that can be manipulated by a mouse (and
often to a limited extent by a keyboard as well)
Greedy heuristics
an algorithm that follows the problem-solving heuristic
of making the locally-optimal choice at each stage
with the hope of finding a global optimum
Heuristic
in mathematical programming, this usually means a
procedure that seeks an optimal solution but does not
guarantee it will find one, even if one exists. It is often
used in contrast to an algorithm, so branch and bound
would not be considered a heuristic in this sense. In
AI, however, a heuristic is an algorithm (with some
guarantees) that uses a heuristic function to estimate
the “cost” of branching from a given node to a leaf
of the search tree (Also, in AI, the usual rules of node
selection in branch and bound can be determined by
the choice of heuristic function: best-first, breadth-first,
or depth-first search)
Histogram
graphic depiction of data using columns to represent
relative size/importance of data grouping
Hypothesis testing
the theory, methods, and practice of testing a hypothesis
by comparing it with the null hypothesis. The null
hypothesis is only rejected if its probability falls below
a predetermined significance level, in which case the
hypothesis being tested is said to have that level of
significance
Influence diagram
depicts structure of decision process and notes the data
needed to make the decision
INFORMS
the largest professional society in the world for
professionals in the field of operations research (OR),
management science, and analytics
Innovative
Applications in
Analytics Award
award administered by the Analytics Section
of INFORMS to recognize creative and unique
developments, applications, or combinations of
analytical techniques. The prize promotes the
awareness of the value of analytics techniques in
unusual applications, or in creative combination to
provide unique insights and/or business value
Integer program
the variables are required to be integer-valued.
Historically, this term implied the mathematical program
was otherwise linear, so one often qualifies a nonlinear
integer program versus a linear IP
Integrity
the measure of the trust that can be placed in the
correctness of the information supplied by a navigation
system
Internal rate of return
IRR
the rate of growth that a project or investment is
expected to create, expressed as a percentage, over a
specified term. IRR is, in essence, the theoretical interest
rate earned by the project
FPA Definition: The discount rate such that the net present value is zero.
KDD
acronym for knowledge discovery in databases process (data mining)
Knapsack problem
The knapsack problem is a problem in combinatorial optimization: Given a set of items, each with a weight and a value, determine the number of each item to include in a collection so that the total weight is less than or equal to a given limit and the total value is as large as possible.
Lead time
time between the initial phase of a process and the
emergence of results, as between the planning and
completed manufacture of a product
Lean production
n a Japanese approach to management that focuses on
cutting out waste while ensuring quality. This approach
can be applied to all aspects of a business – from
design through production to distribution
Lift or lift curve
a measure of the effectiveness of a predictive model
calculated as the ratio between the results obtained with
and without the predictive model; lift charts consisting
of lift curve and a baseline are visuals aids for measuring
model performance
Linear program
opt{cx: Ax = b, x >= 0}. (Other forms of the constraints
are possible, such as Ax <= b.) The standard form
assumes A has full row rank. Computer systems ensure
this by having a logical variable (y) augmented, so the
form appears as Opt{cx: Ax + y = b, L <= (x, y) <=
U} (also allowing general bounds on the variables).
The original variables (x) are called structural. Note
that each logical variable can be a slack, surplus, or
artificial variable, depending on the form of the original
constraint. This computer form also represents a range
constraint with simple bounds on the logical variable.
Some bounds can be infinite (i.e., absent), and a
free variable (logical or structural) is when both of its
bounds are infinite
Little’s law
queuing theory where numerator and denominator
are halved so queues are roughly equivalent no matter
how many are in line; the long-term average number of
customers in a stable system L is equal to the long-term
average effective arrival rate, λ, multiplied by the (Palm)
average time a customer spends in the system, W; or
expressed algebraically: L = λW. The relationship is not
influenced by the arrival process distribution, the service
distribution, the service order, or practically anything
else
Local optimal
a solution that is optimal (either maximal or minimal)
within a neighbouring set of candidate solutions
Logistic regression
a type of probabilistic classification model [1] used for
predicting the outcome of a categorical dependent
variable (i.e., a class label) based on one or more
predictor variables (features). Logistic regression
can be binomial or multinomial. Binomial or binary
logistic regression deals with situations in which the
observed outcome for a dependent variable can have
only two possible types (for example, “dead” versus
“alive”). Multinomial logistic regression deals with
situations where the outcome can have three or more
possible types (e.g., “better” versus “no change”
versus “worse”)
Machine learning
an artificial intelligence (AI) discipline geared toward
the technological development of human knowledge.
Machine learning allows computers to handle new
situations via analysis, self-training, observation, and
experience
MANOVA
multivariate analysis of variance (for use with multiple independent variables)
Mean
the arithmetic average of a set of values or distribution;
however, for skewed distributions, the mean is not
necessarily the same as the middle value (median),
or the most likely (mode); see also, Average
Mean squared error
MSE
the unbiased estimator of population variance. MSE
divides by the error degrees of freedom, e.g., if only
the mean is estimated, MSE divides by N-1, if four
parameters are estimated, MSE divides by N-4, and so
on
Mean time between
failures (MTBF)
a measure of how reliable a hardware product or
component is. For most components, the measure
is typically in thousands or even tens of thousands of
hours between failures
Median
the value such that the number of terms having values
greater than or equal to it is the same as the number
of terms having values less than or equal to it
Metaheuristics
a general framework for heuristics in solving hard
problems. The idea of ``meta’’ is that of level. An analogy
is the use of a metalanguage to explain a language. For
computer languages, we use symbols, like brackets,
in the metalanguage to denote properties of the
language being described, such as parameters that are
optional. Examples of metaheuristics are: Ant Colony
Optimization, Genetic Algorithms, Memetic Algorithms,
Neural networks, etc.
Mode
value of the term that occurs the most often
Monte Carlo
simulation
a computerized mathematical technique that allows
people to account for risk in quantitative analysis and
decision making. The technique is used by professionals
in such widely disparate fields as finance, project
management, energy, manufacturing, engineering,
research and development, insurance, oil and gas,
transportation, and the environment
Net present value
value in today’s currency of an item or service
Network optimization
the process of striking the best possible balance
between network performance and network costs, in
consideration of grade of service requirements
Next best offer (NBO)
a targeted offer or proposed action for customers
based on analyses of past history and behavior, other
customer preferences, purchasing context, attributes of
the produces, or services from which they can choose
Nominal group
technique (NGT)
a structured method for group brainstorming that
encourages contributions from everyone
Normalization
splits up data to avoid redundancy (duplication) by
moving commonly repeating groups of data into new
tables. Normalization therefore tends to increase the
number of tables that need to be joined to perform a
given query, but reduces the space required to hold the
data and the number of places where it needs to be
updated if the data changes
Objective function
the (real-valued) function to be optimized. In a
mathematical program in standard form, this is denoted
f
OLAP
an abbreviation for “Online Analysis and Processing”;
a type of database technology that has long been used
by the business community to analyze and interactively
explore large financial data sets. The basic idea is that
data sets are viewed as cubes with hierarchies along
each axis
OLAP cube
an array of data understood in terms of its zero or more
dimensions; each cell of the cube holds a number that
represents some measure of the business, such as
sales, profits, expenses, budget, and forecast
Operations management
deals with the design and management of products,
processes, services, and supply chains. It considers the
acquisition, development, and utilization of resources
that firms need to deliver the goods and services their
clients want
Operations Research
a discipline that deals with the application of advanced
analytical methods to help make better decisions
Opportunity cost
the cost of an alternative that must be forgone to pursue
a certain action
Optimization
procedure or procedures used to make a system or
design as effective or functional as possible, especially
the mathematical techniques involved
Pareto concept
Pareto principle or the 80/20 rule - roughly 80% of results come
from 20% of effort
Pattern recognition
in machine learning, pattern recognition is the
assignment of a label to a given input value
Payback
the length of time required to recover the cost of an
investment
Pie chart
graphic depiction of data using a pie with different
‘slices’ to represent the relative size of different
groupings of data points to the size of the whole
Precision
the degree to which repeated measurements under
unchanged conditions show the same results
Predictive analytics
any approach to data mining with four attributes:
an emphasis on prediction (rather than description,
classification, or clustering), rapid analysis measured in
hours or days (rather than the stereotypical months of
traditional data mining), an emphasis on the business
relevance of the resulting insights (no ivory tower
analyses), and (increasingly) an emphasis on ease of
use, thus making the tools accessible to business users
Prescriptive analytics
evaluates and determines new ways of operating
targeting business objective and balancing all
constraints
Pricing
a tactic in the simplex method, by which each variable
is evaluated for its potential to improve the value of
the objective function. Let p = c_B[B^-1], where B is a
basis, and c_B is a vector of costs associated with the
basic variables. The vector p is sometimes called a dual
solution, though it is not feasible in the dual before
termination; p is also called a simplex multiplier or
pricing vector. The price of the jth variable is c_j - pA_j.
The first term is its direct cost (c_j) and the second term
is an indirect cost, using the pricing vector to determine
the cost of inputs and outputs in the activity’s column
(A_j). The net result is called the reduced cost, and its
value determines whether this activity could improve
the objective value
Principal Component
Analysis (PCA)
a dimension-reduction tool that can be used to reduce
a large set of variables to a small set that still contains
most of the information in the large set
Probability density
function
the equation used to describe a continuous probability
distribution
Problem assessment/
framing
initial step in the analytics process; involves buy in from
all parties involved on what the problem is before a
solution can be found
Project management
the application of knowledge, skills, and techniques to
execute projects effectively and efficiently. A strategic
competency for organizations, enabling them to tie
project results to business goals
Proprietary data
data that no other organization possesses; produced
by a company to enhance its competitive posture
Queuing theory
mathematical study of waiting in lines; results are used
when making business decisions about the resources
needed to provide service; research begun by A. K.
Erlang
Random or random selection
of or characterizing a process of selection in which each
item of a set has an equal probability of being chosen
Range
the difference between the maximum and minimum
observations providing an estimate of the spread of the
data
Regression
a statistical measure that attempts to determine the
strength of the relationship between one dependent
variable (usually denoted by Y) and a series of other
changing variables (known as independent variables)
Regression analysis
statistical approach to forecasting change in a dependent variable (e.g., sales revenue) on the basis of change in one or more independent variables (e.g., population and income); AKA curve fitting or line fitting
Response surface
methodology (RSM)
a surface in (n+1) dimensions that represents the
variations in the expected value of a response variable
(see, regression) as the values of n explanatory
variables are varied. Usually the interest is in finding the
combination that gives a global maximum (or minimum)
Return on investment
ROI
calculations that provide a basis for comparison with
other investment opportunities; typically calculated
using ROI = ((Total value/benefits) – (total investment
costs))/Total investment costs (
Revenue
management
the science and art of enhancing revenues while selling
essentially the same amount of product
RFM
data related to customer relationship management;
refers to recency, frequency, and monetary value of
purchases
Risk
the potential of loss (an undesirable outcome, however
not necessarily so) resulting from a given action, activity,
and/or inaction
Robust optimization
a term given to an approach to deal with uncertainty,
similar to the recourse model of stochastic
programming, except that feasibility for all possible
realizations (called scenarios) is replaced by a penalty
function in the objective. As such, the approach
integrates goal programming with a scenario-based
description of problem data
Scatter plot
graphic depiction of data, used to show/identify
relationship between independent variables
Scenario analysis
a process of analyzing possible future events by
considering alternative possible outcomes (scenarios).
The analysis is designed to allow improved decision
making by allowing more complete consideration
of outcomes and their implications
Scheduling
a schedule for a sequence of jobs, say j1,…,jn, is a
specification of start times, say t1,…,tn, such that certain
constraints are met. A schedule is sought that minimizes
cost and/or some measure of time, like the overall
project completion time (when the last job is finished)
or the tardy time (amount by which the completion
time exceeds a given deadline). There are precedence
constraints, such as in the construction industry,
where a wall cannot be erected until the foundation
is laid
Sensitivity analysis
the concern with how the solution changes if some
changes are made in either the data or in some of the
solution values (by fixing their value). Marginal analysis
is concerned with the effects of small perturbations,
maybe measurable by derivatives. Parametric analysis
is concerned with larger changes in parameter values
that affect the data in the mathematical program,
such as a cost coefficient or resource limit
Shadow price
an economic term to denote the rate at which the
optimal value changes with respect to a change in some
right-hand side that represents a resource supply or
demand requirement
Simulate annealing
an algorithm for solving hard problems, notably
combinatorial programs, based on the metaphor of how
annealing works: reach a minimum energy state upon
cooling a substance, but not too quickly in order to
avoid reaching an undesirable final state. As a heuristic
search, it allows a nonimproving move to a neighbor
with a probability that decreases over time. The rate of
this decrease is determined by the cooling schedule,
often just a parameter used in an exponential decay (in
keeping with the thermodynamic metaphor). With some
(mild) assumptions about the cooling schedule, this will
converge in probability to a global optimum
Six Sigma
a set of strategies, techniques, and tools for process
improvement. It seeks to improve the quality of
process outputs by identifying and removing the
causes of defects (errors) and minimizing variability
in manufacturing and business processes
Spreadsheet analysis
the analysis of data using special computer software to
anticipate marketing performance under a given set of
circumstances
Standard Deviation
measure of the unpredictability of a random variable,
expressed as the average deviation of a set of data
from its arithmetic mean and computed as the positive
square root of the variance. Customarily represented
by the lower-case Greek letter sigma ( ), it is considered
the most useful and important measure of dispersion
that has all the essential properties of the variance plus
the advantage of being determined in the same units as
those of the original data. Also called root mean square
(RMS) deviation
Statistical significance
probability of obtaining a test result that occurs by
chance and not by systematic manipulation of data
Statistics
branch of mathematics concerned with collection,
classification, analysis, and interpretation of numerical
facts, for drawing inferences on the basis of their
quantifiable likelihood (probability). Statistics can
interpret aggregates of data too large to be intelligible
by ordinary observation because such data (unlike
individual quantities) tend to behave in regular,
predictable manner. It is subdivided into descriptive and
inferential statistics
Stepwise regression
a semi-automated process of building a model by
successively adding or removing variables based solely
on the t-statistics of their estimated coefficients
Supply-chain management
the active management of supply chain activities to
maximize customer value and achieve a sustainable
competitive advantage
System dynamics
a computer-aided approach to policy analysis and
design. It applies to dynamic problems arising in
complex social, managerial, economic, or ecological
systems
Tolerance
an approach to sensitivity analysis in linear programming
that expresses the common range that parameters
can change while preserving the character of the
solution
Traveling salesman
problem (TSP)
given n points and a cost matrix [cij], a tour is a
permutation of the n points. The points can be cities,
and the permutation the visitation of each city exactly
once, then returning to the first city (called home).
Uncertainty
the estimated amount or percentage by which an
observed or calculated value may differ from the true
value
Validation (of a model)
determining how well the model depicts the real-world
situation it is describing
Variability
describes how spread out or closely clustered a set of
data is
Variable cost
a periodic cost that varies in step with the output or the
sales revenue of a company. Variable costs include raw
material, energy usage, labor, distribution costs, etc.
Variance
a parameter in a distribution that describes how far the
values are spread apart. Variance is a characteristic of
some probability distribution, which distinguishes the
concept of variance from the ways to estimate it from
sample data
Variation reduction
reference to process variation where reduction leads
to stable and predication process results
Vehicle routing
problem (VRP)
finding optimal delivery routes from one or more
depots to a set of geographically scattered points (e.g.,
population centers). A simple case is finding a route for
snow removal, garbage collection, or street sweeping
(without complications, this is akin to a shortest path
problem). In its most complex form, the VRP is a
generalization of the TSP, as it can include additional
time and capacity constraints, precedence constraints,
and more
Verification (of a model)
includes all the activities associated with the producing
high quality software: testing, inspection, design
analysis, specification analysis
Web analytics
ability to use data generated through Internet-based
activities; typically used to assess customer behaviors;
see also, RFM
Yield
percentage of ‘good’ product in a batch; has three
main components: functional (defect driven), parametric
(performance driven), and production efficiency/
equipment utilization
Traceability
Knowing the data source for each data element and understanding the validity of the data element.
Traceability
Knowing the data source for data elements included in the analysis and understanding the validity of the data element (critical if the data element is a critical part of the conclusion)
