Index

Alternative hypothesis, 44

Analysis of variance (ANOVA)

definition of, 53

description of, 53–61

homogeneity of variance, 60–61

in R, 61–62

Assignment models, 150–155

Average chart, 31

Bar graph, 18–19

Big data, 157–159

Binding constraint, 139

Boundedness, 135

Business analytic modeling tools

big data, 157–159

data mining, 159–160

Business analytics

definition of, 1

modeling, 160–162

Business decision making

description of, 3–4

management decision process, 5–6

management science, 7–9

scientific method, 4–5

C-Charts. See Control charts

CDF. See Cumulative distribution function

Certainty, 133

Chart tools, 19–20

Column graph, 18–19

Confidence level, 29–30

Continuity, 133–134

Continuous numbers, 30

Control charts (C-Charts), 38–40

Correlation, 96–97

Critical path models, 9

Cumulative distribution function (CDF), 57

Data analysis Toolpak add-In, 85–90

Data mining, 159–160

Data series, 91–95

Decision theory, 9

Degeneracy, 135

Dichotomous events, 103–105

Dual prices, 139–141

Dynamic programming, 9

Feasibility, 134

Finite population, 27, 28

Full model vs. actual HangSeng, 92

Game theory, 9

HangSeng monthly time series regression, 93

HangSeng regression vs. time, 95

HangSeng time series, 95

Heat map, 16

Histograms, 23–25

Homogeneity of variance, 60–61

Hypothesis, 43

Hypothesis statement, 44

Hypothesis testing

criteria of decision, 44–46

definition of, 43

hypothesis statement, 44

making decision, 46–47

observations and sample statistics, 46

“In control” process, 32

Infinite population, 28

Integer models, 142

Inventory modeling, 8

KM. See Knowledge management

Knowledge acquisition, 10

Knowledge management (KM)

analytics perspective, 12

big data, 10–11

computer system perspective, 11–12

definition of, 9–10

Kruskal-Wallis test

decription of, 67–69

R solutions, 69

Lagged models, 100–101

Linearity, 132

Linear programming, 8–9

complications, 134–135

definition of, 119

integer models, 142

model assumptions, 132–134

optimization, 119–127

sensitivity analysis, 135–141

solver, 127–132

transportation models, 149–155

zero-one programming, 143–149

Linear regression

software demonstrations, 81–82

time series forecasting, 71–81

Line charts, 16–17, 20–21

Logistic regression

definition of, 103

log transformation of odds, 105–107

multiple, 112–114

odds of dichotomous events, 103–105

simple, 107–112

software solution for, 114–115

Log likelihood function, 106–107

Log transformation of odds, 105–107

Lower control limit, 35

Management decision process, 5–6

Management science, 7–9

Mann-Whitney test, 63–65

Mean line, 35

Modeling decision environments, 13–14

Moving average models, 71–72

Multiple logistic regression, 112–114

Multiple optimal solution, 135

Multiple regression

correlation, 96–97

data series, 91–95

lagged models, 100–101

models, 97–100

Network models, 9

Nonbinding constraint, 139–140

Nonlinear models, 9

Nonlinear programming, 9

Nonparametric tests, 47

Nonparametric tests of means

Mann-Whitney and Wilcoxon test, 63–65

Wilcoxon signed rank test, 65–66

Null hypothesis, 44

Optimization, 8–9

components, 122–125

demonstration model, 120–121

description of, 119–120

solution, 125–127

“Out of control” process, 32

Parametric tests, 47

P-Charts. See Probability charts

Pie charts, 22–23

Probability charts (P-Charts), 40–42

Quality control charts, 30

Quality control sampling, 30–42

Random sampling, 29

Range chart, 31, 37–38

Reduced costs, 136–139, 141

Regression models, 72–76

Representative samples, 28

Sampled population, 27

Sample size, 30

Sample variance, 49

Sampling, 27

Sampling frame, 28

Scatter plots, 17–18

Scientific method, 4–5

Seasonality, 78–81

Sensitivity analysis

description of, 135–136

dual prices, 139–141

reduced costs, 136–139, 141

Significant variable model, 94

Simple logistic regression, 107–112

Simple random sample, 28

Simulation, 8

Solver, 127–132

Statistics, 8

Stratified random sampling, 28–29

Subgroup markers, 35

Systematic sampling, 29

Time series error metrics, 76–78

Time series forecasting

moving average models, 71–72

regression models, 72–76

seasonality, 78–81

time series error metrics, 76–78

Transportation models, 9, 149–155

T test

description of, 49–50

using R for, 51–53

Two-sample Z test, 48–49

Unique optimal solution, 135

Upper control limit, 35

Variety, 11

Velocity, 11

Visualization

bar and column graphs, 18–19

chart tools, 19–20

heat map, 16

histograms, 23–25

line charts, 16–17, 20–21

pie charts, 22–23

scatter plots, 17–18

using secondary axis, 21–22

Volume, 10–11

Wilcoxon signed rank test, 63–66

Zero-one programming

constraints, 144–149

objective, 143–144

variables, 144

Z test

for independent samples, 47–48

two-sample, 48–49

using R for, 51–53