Index

A

a-priori power
abseentism
accuracy
about
assessing
of regression slopes
of statistics
statistical power
statistics size and
Acemoglu, D.
advanced statistical analysis packages
agreement tests
airline industry, big data in
analysis of level
analytical skills
analytics and reporting stages, tasks in
AngloGold Ashanti Look Ahead
See descriptive statistics
ANN (artificial neural networks)
annotations, placing in graphs
ANOVA
ANOVA F-Statistic
answer formats
Question & Answer Format Issues
Formats
Apache Hadoop®
artificial neural networks (ANN)
associating variables
about
causation and
continuous data
correlation
correlation coefficients
covariance
ordinal data
relating categorical variables
statistical association
variable categories
AstraZeneca case
autocorrelation
average
Brief Introduction to Statistics
Means/Averages
Always Use Both Medians & Averages for Continuous Variables

B

background analyses, versus displayed reports
bar graphs, in SGPLOT procedure
Barr, J.
Bauer, H.H.
Bayesian statistics
about
Introduction
Introduction [2]
classical statistics
final answer (posterior)
pre-existing guesses (proprs)
sample data
BCA (Bias Corrected and Accelerated)
Becker, G.
Berengueres, J.
Bias Corrected and Accelerated (BCA)
big data
about
Introduction to Big Data
Conclusion on Big Data
characteristics of
in airline industry
solutions for
bimodal distribution
binomial data
binomial proportions, assessing categories through
black-and-white graphs, versus color graphs
Blattberg, R.C.
Boom, A.
bootstrapped confidence intervals
bootstrapping
Bootstrapping as a Better Way of Assessing Significance
Introduction to Bootstrapping
More on BCA Bootstrapping
Diagnostic Issue #4: Influential Outliers
Bootstrapping
Step 3: Compare Versions of the Traditional Parametric T-Test
Boudreau, J.W.
box-and-whisper plots, in SGPLOT procedure
breakeven
Burmeister, S.
business analysis
about
combining statistics with per-unit financial values
examples of business extrapolation
financial estimates of revenue or cost of one unit
financial extrapolation process
focal variables
net profitability
scope
business statistics
interpretation of
reporting
tasks in

C

CALIS procedure
Diagnostic Issue #1: Model Structure
Diagnostic Issue #7: Missing Data
capitalization, in SAS code
Cascio, W.F.
categorical data
about
Categorical Data
Introduction
Conclusion on Categorical Data Analysis
linear regression and
linking categorical variables
one-way categorical distributions
statistical questions about
categorical predictors
categorical variables
about
Overview of Associations for Different Variable Types
Introduction to Categorical Variable Links
associating
centrality for
crosstabs
FREQ procedure for associating
linking
Linking Categorical Variables Together: Crosstabs
Linking a Categorical Variable to Continuous Variables
Repeat: Linking Categorical Variables Together
relating
spread for
testing general association between
testing possibilities in association
categories
assessing through binomial proportions
comparing
Introduction to Comparison of Categories
Comparing Related Categories
comparing continuous variables across
comparing means for more than two
comparing means with related categories
CATMOD procedure
Testing Trend in Ordinal Variables
Further Testing Possibilities in Categorical Variable Association
causation
associating variables and
between independent variables
central tendency
centrality
about
as a variable characteristic
checking
for categorical variables
for continuous variables
for ordinal variables
change analysis
change situations, static situations and
character (text) data, versus numerical data
chart modules
Cherrier, J.
Chi-Square
classical statistics
CLV (customer lifetime value)
Cochran-Mantel-Haenszel Statistics
code files
capitalization in
opening existing
Code window (SAS Studio)
coefficients, implications of
color graphs, versus black-and-white graphs
comparison
of categories
of dependent variables
of independent variables
of means
Comparison of Means
Introduction to the Relation Cases
Comparing Means for More than Two Categories: ANOVA
of means for more than two categories
of means with related samples or categories
of more than two categories
of related categories
of two categories
of two means
computers, versus math
computing power and speed, growth in
concepts, measuring relationships between
condition indices
conditional variables
confidence intervals
Significance Method # 1: Confidence Intervals
Introduction to Regression Slopes
confirmatory factor analysis
constellations
constructs
about
choosing
control
defined
focal
importance of
predictor
context
What Do the Statistics Mean?
Examining Fit in More Detail: Error & Residuals
Contingency Coefficient
contingency tables
continuous (ratio or interval) data
Ratio Data
Interval Data
Question Formats & Data Sources
Overview of Associations for Different Variable Types
Introduction
continuous variable spread
continuous variables
centrality for
comparing aross categories
interquartile range for
linking to categorical variables
control constructs, data and
convergent validity
Cook's D
CORR procedure
Calculating Correlations in SAS
Internal Reliability: Measurement through the Cronbach Alpha
correlation analysis
correlation coefficients
correlation tables
correlations
as back-up diagnostics
between independent variables
calculating
compared with covariance
sizes of
types of
cost of one unit, financial estimates of
covariance
about
compared with correlation
Cramer's V statistic
Crestor case
Cronbach alpha
Internal Reliability: Measurement through the Cronbach Alpha
Assessing the Reliability Output
crosstabs
customer lifetime value (CLV)
customer satisfaction, as a variable

D

data
about
assumptions about
Introduction to Data Assumptions
Step 2: Assess Data Assumptions
binomial
capturing
Introduction
Correct Sampling
Initial Data Capture: Which Package?
Reminder of Major Initial Data Issues
charactertistics of variables
Introduction to Variable Characteristics
Introduction to Variable Characteristics [2]
checking for mistakes in
cleaning
The Pre-Analysis Data Cleaning & Preparation Steps
Reminder of Major Initial Data Issues
collecting
continuous (ratio or interval)
Ratio Data
Interval Data
Question Formats & Data Sources
Overview of Associations for Different Variable Types
Introduction
control constructs and
defined
dichotomous
entering
existing
extracting statistics from
fitting
fitting complex mathematical equations to
focal constructs and
forming data tables
gathering
The Importance of Data in Statistics
Introduction
Correct Sampling
Question & Answer Format Issues
importance of in statistics
importing
initial assumptions about
interval
Ratio Data
Interval Data
Question Formats & Data Sources
Overview of Associations for Different Variable Types
Introduction
issues with
Initial Concepts
Reminder of Major Initial Data Issues
manipulating
Overview of the Three Big Tasks in Business Statistics
Introduction to Data Manipulation
modeling preconceived ideas about
multi-row
objects
observations
ordinal
Ordinal Data
Overview of Associations for Different Variable Types
Introduction
populations
post-capturing issues of
ratio
Ratio Data
Interval Data
Question Formats & Data Sources
Overview of Associations for Different Variable Types
Introduction
real-time
samples
See also big data
See also categorical data
See also data patterns
See also errors
See also missing data
See descriptive statistics
shape issues with
testing for normal distributions
testing for straight line shapes
data analysis
about
Overview of the Three Big Tasks in Business Statistics
Major Task #2: Data Analysis
in data warehousing
software for
data architecture skills
DATA keyword
data management
combining datasets
creating datasets
creating temporary datasets in Work library
creating variables
manipulating current variables
data mining
about
compared with theory-based analysis
patterns and
theory versus
data patterns
about
comparing theory-based analysis and data mining
defined
fitting mathematical models
forcing
multivariate patterns
plots versus statistical fit measures of
See also interpreting patterns
single variable patterns
theory versus data mining
troubleshooting
data points, inappropriate
data tables, forming
data warehousing
about
Introduction
Conclusion on Data Warehousing
issues and alternatives in
steps in traditional
database software
dataset analysis
datasets
combining
complex types of
complications in
creating
Creating New Datasets in SAS
Creating Temporary Datasets in the Work Library
creating in Work library
dispersed
incongruent
integrating
longitudinal
multi-level
primary
secondary
vulnerable
dates, capturing
Davenport, T.H.
decision-making, in statistics process
deep learning
Delwiche, L.D.
dependent variables
characteristics of
comparison of
missing
transforming
Transforming the Dependent Variable
Step 3: Compare Versions of the Traditional Parametric T-Test
descriptive statistics
about
Introduction
Conclusion on Descriptive Statistics
assessing distribution
centrality
end outcome of analysis of
getting in SAS
shape
spread
dichotomous data
discriminant validity
dispersed datasets
displayed reports, versus background analyses
distributed computing, improved storage and processing through
distribution, assessing
Dull, T.
dummy variables
Introducing Dummy Variables
Slope Assessment # 2: Size of Significant and Accurate slopes
Durbin-Watson statistic
Dyché, J.

E

Editor window (SAS 9)
Efimov, D.
Ellison, L.
employee stocks
employee-related variables, value of
employees
movement of
performance of
reductions in expensive behaviors
turnover of
endogeneity
enquiries
as a variable
of customers
Enterprise Resource Programs (ERPs)
equivalence, testing for
ERPs (Enterprise Resource Programs)
errors
about
checking centrality and spread
inappropriate data points
missing data
multi-item scales
residuals and
strange variable distributions
ETL (Extract-Transform-Load)
examples
about
brief
Your Brief for the Case Example
Reminder - Your Brief for the Textbook Case Study
company
correlation analysis
current research needs
of business extrapolation
of interpreting when patterns are not found
of SGPLOT procedure graphs
of simulation
existing data
exploratory factor analysis
Explorer window (SAS 9)
Extract-Transform-Load (ETL)
extracting
statistics from data
to data marts

F

face-to-face interviews
Facebook
feedback loops
FIML (full information maximum likelihood)
Assessing Missing Data in Variables
Diagnostic Issue #7: Missing Data
final statistic parameters and coefficients, intermediate fit statistics versus
financial extrapolation process
financial profitability
financial variables, values of
fit
about
Introduction
Plots Versus Statistical “Fit” Measures of Patterns
Examining Fit in More Detail: Error & Residuals
assessing
steps in
troubleshooting
What Happens If Fit Cannot be Achieved?
What to Do If Global Fit is Poor
fitting models
See statistics process
focal constructs, data and
focal variables
folders and files, linking with
Linking SAS Studio with Folders & Files on your Computer
Creating a SAS Studio Library & Linking It with a Folder
follow-up recommendations
formats
answer
Question & Answer Format Issues
Formats
question
Question & Answer Format Issues
Question Formats & Data Sources
formatting, in SGPLOT procedure
FREQ procedure
Getting Descriptive Statistics in SAS
Centrality for Categorical Variables
Linking Categorical Variables Together: Crosstabs
Repeat: Linking Categorical Variables Together
General SAS PROC FREQ Code for Categorical Variable Association
Testing Trend in Ordinal Variables
Further Testing Possibilities in Categorical Variable Association
full information maximum likelihood (FIML)
Assessing Missing Data in Variables
Diagnostic Issue #7: Missing Data

G

Garbage in, Garbage out (GIGO)
geographical mapping, using GMAP procedure
GIGO (Garbage in, Garbage out)
GKPI procedure
Summary of SAS Graphing Modules
Business Dashboards through PROC GKPI
global fit, troubleshooting
GMAP procedure
Summary of SAS Graphing Modules
Geographical Mapping Using PROC GMAP
good fit
Goodnight, Jim
GPLOT procedure
graphing
about
Introduction
Conclusion on SAS Graphing
black-and-white versus color
flexibility in
GKPI procedure
Summary of SAS Graphing Modules
Business Dashboards through PROC GKPI
GMAP procedure
Summary of SAS Graphing Modules
Geographical Mapping Using PROC GMAP
modules for
placing annotations in graphs
procedures for
SGPANEL procedure
SGPLOT procedure
SGSCATTER procedure
groups, comparing

H

Hammerschmidt, M.
Hats (leverage scores)
Heath, D.
Helwig, J.
heteroscedasticity
about
effects of
in residual plots
remedies for
Hoeffding Dependence Cpefficient
Hong, S.J.
HTML files
hypothesis testing

I

IF-THEN concept
in-memory processing, improved processing through
inaccuracy, faces of
inappropriate data points
incongruent datasets
independent variable slopes
independent variables
causal relationships between
comparison of
correlations between
influence, defined
influential outliers
initial phase, in data warehousing
inputs, costs of
integration phase, in data warehousing
intercept
intermediate fit statistics, versus final statistical parameters and coefficients
interpretations
interpreting patterns
about
implications of model and coefficients
steps in
interquartile range, for continuous and ordinal variables
interval data
Ratio Data
Interval Data
Question Formats & Data Sources
Overview of Associations for Different Variable Types
Introduction
issues

J

Jackofsky, E.F.
Janmaat, E.
JIPSA (Joint Initiative for Priority Skills Acquisition)
JMP®
Brief Introduction to SAS
Running SAS Tasks through Point-and-Click Windows
Joint Initiative for Priority Skills Acquisition (JIPSA)

K

Kendall's Tau
knowledge
Kuhfeld, W.
kurtosis
Appendix A to Chapter 7: Basic Normality Statistics
Appendix A to Chapter 7: Basic Normality Statistics [2]
Example 2: Testing Data for a Normal Distribution

L

Lawrence, R.D.
Lehrer, J.
leverage scores (Hats)
libraries
creating in SAS 9
creating in SAS Studio
licenses
as variable
distribution of
variables analyzed by
Likelihood Ratio Chi-Square
Likert-type scale
Formats
Single Likert-Type Scale Items as Ordinal Predictors
line plots, in SGPLOT procedure
linear regression
about
Introduction
Getting Started
aim of
applying remedies
assessing fit
categorical predictors
core textbook example
defined
implementing multiple regression
initial data issues
interpreting regression slopes
ordinal predictors
reporting multiple regression results
running regresson analysis
simplest case of
single Likert-type scale items
variables in
variables in multiple regression
linearity
lines, in SAS code
loading, in data warehousing
Log window
SAS 9
SAS Studio
logic, importance of
lognormal distribution
longitudinal datasets

M

machine learning
Introduction to Machine Learning
Some Types of Machine Learning
magnitude
See size, of statistics
Malthouse, E.C.
Mantel-Haenszel Chi-Square test
Mardia score
marketing outcomes
Matange, S.
math, versus computers
mathematical models, fitting
mathematical simulations
means
about
comparing
comparing for more than two categories
comparing to population benchmarks
comparing two
comparing with related samples or categories
MEANS procedure
Getting Descriptive Statistics in SAS
Getting Descriptive Statistics in SAS [2]
Centrality for Categorical Variables
Linking a Categorical Variable to Continuous Variables
measurement error
measurement, growth in
medians
Medians
Always Use Both Medians & Averages for Continuous Variables
MI procedure
MIANALYZE procedure
Miner, Bob
Mining Qualifications Authority (MQA)
missing data
as a diagnostic issue
assessing in observations
assessing in variables
dealing with
diagnosis of in regression
in observations
in variables
linear regression and
remedies for
steps
mode
model fitting
See statistics process
MODEL statement
models
structures of
theoretical and practical implications of
modules, for graphing
MQA (Mining Qualifications Authority)
multi-item assessment
Designing Multi-Item Measures
Designing Multi-Item Measures [2]
multi-item scales
about
Dealing with Missing Data
Multi-Item Scales: A Brief Reminder
aggregating multiple items into summary variables
assessing internal reliability of each
dealing with
linear regression and
reversed items
tasks in preparing
multi-item variables
multi-level datasets
multi-row datasets
multicollinearity
Diagnostic Issue #2: Multicollinearity
Diagnostic Issue #2: Multicollinearity [2]
multiple imputations
Assessing Missing Data in Variables
Diagnostic Issue #7: Missing Data
multiple regression
implementing
reporting results of
multivariate patterns

N

needs, for statistics process
negative linearity
net profitability
about
basic profit
breakeven
return on investment (ROI)
New Import Data wizard
Nirmalanof, G.
non-linearity
about
effects of
in residual plots
remedies for
noninferiority tests
nonparametric statistics
nonparametric T-test
normal distribution
The Normal Distribution
Example 2: Testing Data for a Normal Distribution
normality
normality statistics
Normalized Multivariate Kurtosis score
numerical data, versus text (character) data

O

Oates, E.
objects
observations
about
loss of
missing data in
Assessing Missing Data in Observations
Diagnostic Issue #7: Missing Data
odds ratio test, homogeneity of
ODS Graphics engine
ODS outputs
in SAS 9
in SAS Studio
one-way categorical distributions
one-way frequencies
about
assessing categories through binomial proportions
assessing distribution of
online slider scale
operational time, value of
operational variables, costs and revenues of
Oracle South Africa case study
Oracle VirtualBox
ordinal data
Ordinal Data
Overview of Associations for Different Variable Types
Introduction
ordinal predictors
single Likert-type scale items as
special treatment of
ordinal variables
about
centrality for
interquartile range for
testing trend in
outlier weighting
outlier, defined
output formats, reporting through
overtime

P

p-value
Significance Method # 2: Single Inaccuracy Estimates & P-Values
The Parametric Standard Error, Test Statistic and P-Value
Fit Part 3: The ANOVA F-Statistic
Introduction to Regression Slopes
paired samples
parabola
paradigms, patterns and
parametric
Introduction to Data Assumptions
Alternative Parametric Statistics
parametric approach
about
p-value
standard error
test statistic
patterns
implications of
over time
reasons for
See also data patterns
PDF files
Introduction
Black-and-White Versus Color Graphs
Pearson correlations
people variables
per-unit financial values, combining statistics with
Phi coefficient
physical simulations
Pischke, J-S.
plots, versus statistical fit measures of patterns
point-and-click
populations
The Observations We Study: Samples & Populations
Comparing a Mean to a Population Benchmark
positive linearity
post-capturing
POWER procedure
pre-analysis data cleaning and preparation
pre-existing guesses (proprs)
predictor constructs
primary datasets
process simulations
products, value of
programming code
about
advantages of
doing tasks through
lessons on
running
protocols, in data analysis software
psychometric measures

Q

question formats
Question & Answer Format Issues
Question Formats & Data Sources

R

R-Sq statistics
about
interpreting size of
random patterms
ratio data
Ratio Data
Interval Data
Question Formats & Data Sources
Overview of Associations for Different Variable Types
Introduction
raw data records
raw datasets
real-time data
REG procedure
Run the Regression in SAS
Diagnostic Issue #7: Missing Data
regression
See also linear regression
regression analysis, running
regression parameters
about
independent variable slopes
intercept
regression slopes
about
Relational Statistic 3: Regression and the Regression Slope
Introduction to Regression Slopes
Conclusion on Analyzing Slopes
interpreting
process for interpreting
significance and accuracy of
size of significance and accuracy of
reliability output, assessing
remedies, applying
REPORT procedure
reporting
about
skills for
through output formats
representativity
requirements, for statistics process
residual plots
about
diagnosing data shape issues with
heteroscedasticity in
non-linearity in
residuals
about
error and
normality of
Results window
SAS 9
SAS Studio
return on investment (ROI)
returns
revenue, financial estimates of
reverse-worded items
reversed items, dealing with
Rich Text Files
Introduction
Black-and-White Versus Color Graphs
robust regression
ROBUSTREG procedure
ROI (return on investment)
Royal FrieslandCampina example
See data
Run command

S

sales
Are Sales Associated with Other Variables?
Sales
Sall, J.
sample size
samples and sampling
The Observations We Study: Samples & Populations
Correct Sampling
SAS
about
Introductory Vignette: SAS On Top of the Analytics World
Brief Introduction to SAS
website
SAS® 9
about
creating libraries in
importing data into
installing
ODS outputs in
opening
opening code files in
setting options
setting up
SAS® Enterprise Guide®
Brief Introduction to SAS
Running SAS Tasks through Point-and-Click Windows
SAS® Enterprise Miner
SAS® LASR
Improved Processing through In-Memory Processing
Issues & Alternatives in Data Warehousing
SAS® Studio
about
Brief Introduction to SAS
(Optional): Setting SAS 9 Options
Getting Acquainted with SAS Studio
Running SAS Tasks through Point-and-Click Windows
creating libraries
importing data
installing
linking libraries with folders
linking with folders and files on computers
ODS outputs in
opening
opening code files in
setting options
setting up
Visual Programmer mode
SAS® Text Miner
SAS® University Edition
Brief Introduction to SAS
SAS Studio Installation
SAS® Visual Analytics
satisfaction, of customers
scalability
scatter graphs, in SGPLOT procedure
scatterplots, SGSCATTER procedure for multiple
scope
SD (standard deviation)
secondary datasets
semantic differential
semicolons
Server Files and Folders (SAS Studio)
services, value of
SGPANEL procedure
Summary of SAS Graphing Modules
Multiple Plots Simultaneously through PROC SGPANEL
SGPLOT procedure
about
Summary of SAS Graphing Modules
Introduction to PROC SGPLOT
examples of graphs
graphing options and formatting in
SGSCATTER procedure
Summary of SAS Graphing Modules
PROC SGSCATTER for Multiple Scatterplots
shapes
about
bimodal distribution
fitting data to exact mathematical
lognormal distribution
normal distribution
testing data for straight line
uniform distribution
significance, of regression slopes
simple imputations
Assessing Missing Data in Variables
Diagnostic Issue #7: Missing Data
simulation
about
Introduction to Simulation
Conclusion on Simulation Modeling
example of
types of
single accuracy estimates
single data points
single variable patterns
size
as variable
levels of
of correlations
of R-Sq statistics
of significance and accuracy of regression slopes
of statistics
Introduction
Size Matters!
Interpreting Raw Size
Putting Statistical Size and Accuracy Together
variables analyzed by
skewness
Appendix A to Chapter 7: Basic Normality Statistics
Appendix A to Chapter 7: Basic Normality Statistics [2]
Example 2: Testing Data for a Normal Distribution
skills
data architecture
extending your
reporting
Slaughter, S.J.
slow to access data
Snippets
social media
software
data analysis
spacing, in SAS code
Spearman correlations
specification error
spread
about
as a variable characteristic
calculating variables spread
checking
continuous variable
for categorical variables
interquartile range for continuous and ordinal variables
Sreekumar, K.P.
staging phase, in data warehousing
standard deviation (SD)
standard error, parametric approach and
standardized slopes
Introduction to Regression Slopes
Slope Assessment # 2: Size of Significant and Accurate slopes
standardized statistics
static situations, change situations and
statistical association
statistical effect
statistical extrapolation
about
examples of
means-based example of
regression-based example of
statistical power
about
before and after testing
elements of
measurement of
problems with
understanding
statistical significance
about
bootstrapping
confidence intervals
single inaccuracy estimates and p-values
statistical tests of distribution
statistics
about
accuracy of
Do We Trust the Statistics? Are They Accurate?
Issue # 2: Accuracy of Statistics
advice on
classical
combining with per-unit financial values
extracting from data
generating
importance of data in
meaning of
nonparametric
normality
See also descriptive statistics
standardized
statistics process
about
Introductory Case: Big Data in the Airline Industry
Introduction
Summary of the Statistics Process
challenges in
decision-making
extracting statistics from data
getting data
needs and requirements for
patterns in data
understanding
storage, growth in
strikes
structural equation modeling
Studentized Residual
subgroups, comparing
summary variables
superiority tests
supervised learning algorithms
surveys
SYSLIN procedure

T

T-tests
about
assessing data assumptios
end-point of
implementing nonparametric
related data
running initial
versions of traditional parametric
tabs, in SAS code
TABULATE procedure
tasks
doing through programming code (syntax)
in analytics and reporting stages
running through point-and-click
test statistic, parametric approach and
testing
assessing power before and after
for statistical significance
text (character) data, versus numerical data
textbook materials
textual analysis
theory
defined
importance of
versus data mining
theory-based analysis, compared with data mining
times
capturing
changes in
traditional parametric t-test, versions of
transformations
trust
as variable
of customers
TTEST procedure
Twitter
two-stage least squares regression
type, as a variable characteristic

U

understanding, in statistics process
unequal variances t-test
uniform distribution
UNIVARIATE procedure
Getting Descriptive Statistics in SAS
Getting Descriptive Statistics in SAS [2]
Centrality for Categorical Variables
Summary of SAS Graphing Modules
unstandardized slopes
Introduction to Regression Slopes
Slope Assessment # 2: Size of Significant and Accurate slopes
unstructured data, growth in
unsupervised learning

V

value, of big data
variable distribution
variables
about
analyzed by license and size
assessing missing data in
calculating spread
categories of
characteristics of
Introduction to Variable Characteristics
Introduction to Variable Characteristics [2]
choosing
choosing the right
conditional
continuous
Means/Averages
The Interquartile Range for Continuous & Ordinal Variables
Linking a Categorical Variable to Continuous Variables
What Are We Comparing?
creating
dependent
Dealing with Missing Data
Interpreting the Size of the R2
Comparison of Other Features of the Dependent Variable
Transforming the Dependent Variable
Step 3: Compare Versions of the Traditional Parametric T-Test
dummy
Introducing Dummy Variables
Slope Assessment # 2: Size of Significant and Accurate slopes
focal
importance of types
in linear regression
independent
Diagnostic Issue #1: Model Structure
Diagnostic Issue #2: Multicollinearity
Comparisons of Independent Categories
manipulating
missing data in
ordinal
Centrality for Ordinal Variables
The Interquartile Range for Continuous & Ordinal Variables
Introducing Dummy Variables
Testing Trend in Ordinal Variables
sales and
See also associating variables
See also categorical variables
specifying
strange distributions of
summary
variance inflation factors (VIFs)
variances
Continuous Variable Spread: Standard Deviations & Variances
Explaining Variance: The Aim of Linear Regression
Introduction to Data Assumptions
variety, of big data
velocity, of big data
veracity, of big data
Viewers (SAS 9)
VIFs (variance inflation factors)
virtualization program
Visa
Visual Programmer mode
Running SAS Tasks through Point-and-Click Windows
The Visual Programmer Mode in SAS Studio
VMWare Player
volume, of big data
vulnerable datasets

W

wage bill, changes in
Walmart
weak relationship
weighted regression
West Point
See linear regression
Windows folders, linking SAS library to
Work library, creating datasets in
workforce numbers, changes in

Z

zero relationship
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