Index
Note: Page numbers followed by f indicate figures, t indicate tables, and b indicate boxes.
A
Absolute nesting data structure
990
Adaptive quadrature process
1005
Advanced Integrated Multidimensional Modeling Software (AIMMS)
774
binary programming (BP)
733
integer programming (IP) model
734–736
mixed-integer programming (MIP) problem
734–736
nonlinear programming (NLP)
733
Akaike information criterion (AIC)
695
A Mathematical Programming Language (AMPL)
774
Analysis of variance (ANOVA)
525,
527f
multiple interactions
246
residual sum of squares
233
residual sum of squares (RSS)
240
sum of squares of factor
240
Anti-Dice similarity coefficient
323
Arbitrary weighting procedure
314
ungrouped discrete and continuous data
first-order autocorrelation
492
generalized least squares method
494
modulus/absolute deviation
54
ungrouped discrete and continuous data
54,
54t,
54b
B
Balanced nested data structure
988
Bayesian (Schwarz) information criterion (BIC)
695
Best linear unbiased predictions (BLUPS)
1005
Binary logistic regression model
539
explanatory variables
540
overall model efficiency (OME)
560
logistic probability adjustment
555,
555f
Binary programming (BP)
733
linear programming model
894
Net Present Value (NPV)
894
Solver Parameters dialog box
895,
896f
Bivariate descriptive statistics
93,
94f
qualitative variables, measures of association
quantitative variable, measures of correlation
Blocked Adaptive Computationally Efficient Outlier Nominators (BACON) algorithm
52
Bowley’s coefficient of skewness
63–64,
64b
IBM SPSS Statistics Software
527
ordinary least squares (OLS) method
480–481
Stata, regression models estimation
511,
512f
C
linear programming model
894
Net Present Value (NPV)
894
Solver Parameters dialog box
895,
896f
Chi-square statistic measures
arbitrary weighting procedure
314
discriminant analysis
311
data standardization procedure
318
Euclidean squared distance
318,
320t
Pearson’s correlation coefficient
315
Pythagorean distance formula
316,
317f
multinomial logistic regression
311
anti-Dice similarity coefficient
323
arbitrary weighting problems
321
Dice similarity coefficient (DSC)
323
Hamann similarity coefficient
324
Ochiai similarity coefficient
323
Rogers and Tanimoto similarity coefficient
323
Russel and Rao similarity coefficient
323
simple matching coefficient (SMC)
322
Sneath and Sokal similarity coefficient
324
Yule similarity coefficient
323
Coefficient of kurtosis
65,
66f
Coefficient of skewness on Stata
64–65
Coefficient of variation
61,
61b
Conditional probability
131
negative binomial regression model
644,
644t
Confirmatory factor analysis
383
Confirmatory techniques
405
Contingency tables creation
IBM SPSS Statistics Software
Cell Display dialog box
97,
100f
variables selection
96,
97f
Continuous random variable
139f
expected/average value
140
probability density function
139
probability distributions
Continuous variables ,
16
Convex and nonconvex sets
748,
748f
Correlation coefficients
383
Crossindustry standard process of data mining (CRISP-DM)
984–985,
986f
Cumulative distribution function (CDF)
D
Data, information, and knowledge logic ,
4f
crossindustry standard process of data mining (CRISP-DM)
984–985,
986f
knowledge discovery in databases (KDD)
984,
985f
tools and software packages
986
variety and variability
983
grouped discrete data
50,
50b
Decision-making process ,
Design of experiments (DOE)
935
factorial design (FD)
937
one-way analysis of variance (one-way ANOVA)
938
randomized block design (RBD)
937,
937f
results and conclusions
936
Dice similarity coefficient (DSC)
323
Dichotomous/binary variable (dummy)
16,
17t
Direct Oblimin methods
398
probability distributions
Dispersion/variability measures
modulus/absolute deviation
54
ungrouped discrete and continuous data
54,
54t,
54b
coefficient of variation
61,
61b
ungrouped discrete and continuous data
57,
57b
IBM SPSS Statistics Software
528,
528f
Stata, regression models estimation
515,
516f
E
parameter, definition
189
maximum likelihood estimation
192
ordinary least squares (OLS)
191–192
population parameters
189
Euclidean squared distance
318,
320t
Explanatory variables
935
Exploratory factor analysis
383
Exploratory multivariate technique
383
F
network programming problem
902
Factorial analysis of variance
239–246
Factorial design (FD)
937
Feasible basic solution (FBS)
755
Fixed effects parameters
988
G
binomial approximation
155
cumulative distribution function
154,
154f
probability density function
152,
153f
General Algebraic Modeling System (GAMS)
774
Generalized linear latent and mixed model (GLLAMM)
1005
Geometric propagation/snowball sampling
177,
177b
H
Hamann similarity coefficient
324
Hierarchical agglomeration schedules
Hierarchical crossclassified models (HCM)
990
Hierarchical linear models (HLM)
988
generalized linear latent and mixed models (GLLAMM)
1052,
1052f
hierarchical logistic models
mixed effects logistic regression models
1052–1053
three-level hierarchical linear models, repeated measures (HLM3)
998
intercepts and slopes randomness
996–997
intraclass correlation
997
variance-covariance matrix
995
two-level hierarchical linear models, clustered data (HLM2)
intraclass correlation
993
likelihood-ratio tests
993,
995
logarithmic likelihood function
994–995
maximum likelihood estimation (MLE)
994
multiple linear regression model
991
multivariate normal distribution
993
random intercepts model
993
reduced maximum likelihood
994
restricted maximum likelihood (REML)
994
statistical significance
993
Higher-order correlation coefficients
387–389
Horizontal bar charts
26,
27f
binary logistic regression model
554
Huber-White robust standard error estimation
491
Stata, regression models estimation
507
statistical hypothesis
199
I
IBM SPSS Statistics Software
Box-Cox transformations
527
confidence levels and intercept exclusion
519,
519f
Descriptives dialog box
76,
77f
Options dialog box
76,
78f
Options, summary measures
76,
78f
Descriptives Option, results
78,
81f
Explore dialog box
78,
79f
Outliers option results
79,
81f
Statistics dialog box
78,
80f
Charts dialog box
74,
75f
Frequencies dialog box
73,
73f
frequency distribution table
74,
76f
qualitative and quantitative variables
72
Statistics dialog box
73,
74f
Statistics, summary measures
74,
75f
heteroskedasticity problem
524
hierarchical agglomeration schedules
350,
351f
nominal (qualitative) classification
356,
358f
multicollinearity diagnostic
523,
524f
normality plots with tests
523,
523f
parameter and confidence intervals selection
518–519,
518f
principal components factor analysis
Display factor score coefficient matrix
412,
412f
KMO statistic and Bartlett’s test of sphericity
413,
413f
Varimax orthogonal rotation method
416,
416f
Shapiro-Wilk normality test result
523,
523f
stepwise procedure selection
519,
521f
univariate tests for normality
VIF and Tolerance statistics
523,
524f
network programming problem
902
mathematical formulation
890
metaheuristic procedure
887
mixed binary programming (MBP)
887
mixed integer programming (MIP)
887
Interquartile range/interquartile interval (IQR/IQI)
37,
52
Intraclass correlation
993,
997
J
Joint frequency distribution tables
contingency/crossed classification/correspondence table
93
quantitative variables
114
Judgmental/purposive sampling
175,
175b
K
Karhunen-Loève transformation
384
mathematical formulation
890
Knowledge discovery in databases (KDD)
984,
985f
Kolmogorov-Smirnov (K-S) test
1177t
Kruskal-Wallis test
1190t
L
Latent root criterion
393
Likelihood-ratio tests
993,
995
Linear Interactive and Discrete Optimizer (LINDO) Systems
773–774
Linear mixed models (LMM)
988
Linear programming (LP) problems
additivity assumption
713
Advanced Integrated Multidimensional Modeling Software (AIMMS)
774
binary programming (BP)
733
integer programming (IP) model
734–736
mixed-integer programming (MIP) problem
734–736
nonlinear programming (NLP)
733
convex and nonconvex sets
748,
748f
divisibility and non-negativity
713
feasible basic solution (FBS)
755
General Algebraic Modeling System (GAMS)
774
inequality constraint
711
linear function and constraints
709
Linear Interactive and Discrete Optimizer (LINDO) Systems
773–774
A Mathematical Programming Language (AMPL)
774
Optimization Subroutine Library (OSL)
774
financial investments
724
investment portfolio risk minimization
725–728
investment portfolio’s expected return
724–725
production and inventory problem
demand per product and period
730t
integer programming (IP) problem
731
inventory balance equations
731
maximum inventory capacity
732
maximum production capacity
731
resource optimization problems
713
independent constraints terms
807
Solver error messages, unlimited and infeasible solutions
Solver Results dialog box
798
standard maximization problem
711
viable/feasible solution
747
analysis of variance (ANOVA)
457,
458f
ceteris paribus condition
474
confidence interval amplitudes
479
residual sum of squares (RSS)
451
sum of squares due to regression (SSR)
451
total sum of squares (TSS)
451
explanatory variables
443
metric/quantitative variable
443
predicted value and parameters
444
quantitative dependent variable
443
coefficients and significance
459,
461f
Logarithmic likelihood function
994–995
M
Maximum likelihood estimation (MLE)
192,
994,
1005
negative binomial regression model
Poisson regression model
621t
non-negative and discrete values
620
Stata, regression models estimation
500
Mean absolute deviation (MAD)
725
Mixed binary programming (MBP)
887
Mixed effects logistic regression models
1052–1053
Mixed-integer programming (MIP) problem
734–736,
887
frequency distribution
920
probability density functions (PDF)
920
risks and uncertainties
920
Multilevel negative binomial regression model
1059
Multilevel Poisson regression model
1059
Multinomial logistic regression model
311,
539
occurrence probabilities
563
ceteris paribus concept
467
null hypothesis, nonrejection
472
residual sum of squares
468
Multivariate normal distribution
394,
993
Mutually excluding/exclusive events
128,
128f,
130
N
Negative binomial regression model
negative binomial type 1 (NB1) regression model
636
negative binomial type 2 (NB2) regression model
636
occurrence probability
634
probability distribution function
634
quantitative variable
633
balanced transportation problem
839
directed and undirected arc
836,
836f
directed and undirected cycle
837
directed and undirected path
837
transhipment problem (TSP)
intermediate transhipment points
860–862
transportation unit cost
862
Nonhierarchical
k-means agglomeration schedule
338–339
one-way analysis of variance (ANOVA)
348,
349t
variation and
F statistic
348t
Nonlinear programming (NLP)
733
Nonlinear regression models
443
binary and multinomial logistic models
497
Poisson and negative binomial regression models
497
Nonmetric/qualitative variables
dichotomous/binary variable (dummy)
16,
17t
descriptive statistics
10,
12
variable selection
11,
13f
scales of accuracy
16,
16f
advantages and disadvantages
169–170
geometric propagation/snowball sampling
177,
177b
judgmental/purposive sampling
175,
175b
O
Oblique rotation methods
398
Ochiai similarity coefficient
323
finite population, sample size
proportion estimation
185
infinite population, sample size
One-way analysis of variance (one-way ANOVA)
937–938
decision and parameter variables
707
Optimization Subroutine Library (OSL)
774
Ordinary Gauss-Hermite quadrature
1005
Ordinary least squares (OLS) method
191–192
first-order autocorrelation
492
generalized least squares method
494
Excel Regression tool
450
Breusch-Pagan/Cook-Weisberg test
489–490
chi-square distribution
490
probability distribution
488
weighted least squares method
490
linear regression estimation
450,
451f
auxiliary regressions
487
normal distribution of residuals
480,
480f
regression equation coefficients
450,
453f
residual sum of squares minimization
449,
450f
Shapiro-Wilk test/Shapiro-Francia test
480
travel time
vs. distance traveled
445,
446f
Orthogonal rotation method
397
Overall model efficiency (OME)
560
negative binomial regression model
634
P
univariate tests for normality
variance homogeneity tests
Partial correlation coefficients
387–388
Pearson’s contingency coefficient
107
Pearson’s linear correlation
384
three-dimensional scatter plot
385,
386f
Pearson’s second coefficient of skewness
63,
63b
grouped discrete data
50,
50b
maximum likelihood estimation
192
ordinary least squares (OLS)
191–192
non-negative and discrete values
620
probability of occurrence
619,
619t
Polychotomous variable
16
moment of distribution
190
financial investments
724
investment portfolio risk minimization
725–728
investment portfolio’s expected return
724–725
Position/location measures
interquartile range (IQR)
52
outlier identification methods
52,
53b
Principal components factor analysis
coefficient of determination
395
confirmatory factor analysis
383
confirmatory techniques
405
correlation coefficients
383
Cronbach’s alpha’s magnitude
390
exploratory factor analysis
383
exploratory multivariate technique
383
Direct Oblimin methods
398
oblique rotation methods
398
orthogonal rotation method
397
higher-order correlation coefficients
387–389
Karhunen-Loève transformation
384
latent root criterion
393
multiple linear regression model
393
multivariate normal distribution
394
partial correlation coefficients
387–388
Pearson’s correlation coefficients
398,
399t
Pearson’s linear correlation
384
three-dimensional scatter plot
385,
386f
structural equation modeling
383
weighted rank-sum criterion
408,
409t
zero-order correlation coefficients
387–388
Probability density functions (PDF)
920
negative binomial regression model
634
conditional probability
131
Probability variation field
129
Production and inventory problem
demand per product and period
730t
integer programming (IP) problem
731
inventory balance equations
731
maximum inventory capacity
732
maximum production capacity
731
non-negativity constraints
732
Proportional stratified sampling
173
Pythagorean distance formula
316,
317f
Q
bivariate descriptive statistics
univariate descriptive statistics
Quantile regression models
48–52
median regression models
532
normality of residuals
533
conditional distribution
537
median regression model outputs
535,
535f
nonconditional median
535
bivariate descriptive statistics
scales of accuracy
16,
16f
univariate descriptive statistics
grouped discrete data
50,
50b
R
Random coefficients models
988
Random effects parameters
988
Randomized block design (RBD)
937,
937f
advantages and disadvantages
169
two-stage cluster sampling
174,
175b
Reduced maximum likelihood
994
Reduced normal distribution
153
negative binomial regression model
617,
618f
Regression specification error (
RESET) test
495
Relative cumulative frequency
22
Residual sum of squares (RSS)
451,
468
Robit regression models
610f
Bernoulli distribution
609
logistic distribution
609
Rogers and Tanimoto similarity coefficient
323
Russel and Rao similarity coefficient
323
S
Sample moment of distribution
190
negative linear relationship
114,
115f
positive linear relationship
114,
114f
Simple Scatterplot dialog box
115,
117f