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828 Index DMAIC (Define, Measure, Analyze, Improve, and Control) process, 726 Dot plots defined, 25 Minitab, 25, 43 Dummy variables. See Indicator variables Durbin-Watson test critical values for, 793–794 defined, 617 formula, 617 Elementary events, 96–97 Empirical rule. See also Standard deviation analysis, 226 defined, 60, 83 illustrated, 61 Errors of estimation, 275 forecast, 591 input processing, 226 nonsampling, 226–227 recording, 226 response, 227 sampling, 226 Type I, 298 Type II, 298–299 Ethical considerations ANOVA, 453 categorical data analysis, 665 charts and graphs, 35 chi-square goodness-of-fit test, 665 continuous distributions, 207 descriptive statistics, 83 discrete distributions, 170 forecasting, 632 hypothesis testing, 334 introduction to, 12 multiple regression analysis, 539 nonparametric statistics, 710 probability, 128 quality control, 758 regression analysis, 507–508 regression models, 580 sampling, 241 statistical inference (estimation), 280 statistical inference estimation, 280 statistical inferences (two populations), 390 Events collectively exhaustive, 99 complementary, 99 defined, 96 elementary, 96–97 independent, 98–99, 119–121 mutually exclusive, 98 rare, 154 Excel. See also Minitab ANOVA, 460 ANOVA tables, 430–431 bar charts, 28, 43 binomial distributions, 148–149 charts, 42 chi-square goodness-of-fit test, 669 column charts, 28, 43 confidence intervals, 264 continuous distributions, 213 correlation, 468 descriptive statistics, 81, 91 difference in two means (z statistic), 352 difference of means (t test), 357–358 discrete distributions, 176 E exponential function, 205 forecasting, 640–641 frequency distributions, 41–42 frequency polygons, 24, 42 hypergeometric distribution probabilities, 166 hypothesis testing, 340 multiple regression analysis, 544 normal distribution, 194 ogives, 42–43 one-way ANOVA, 411–412 Pareto charts, 43 pie charts, 42 Poisson distribution, 159–160 population mean hypothesis testing (t test), 312–313 population mean hypothesis testing (z statistic), 306–307 power curve, 330–331, 334–335 p-values, 303 quadratic model, 551 randomized block designs, 430–431 regression analysis, 505–506, 515 regression line graph, 473 regression model building, 586–587 related populations, 367–368 sampling, 246 scatter plots, 43, 470 statistical analysis using, 11 statistical inference estimation, 285 two population variances, 386 two populations, 398–399 two-way ANOVA, 443, 444–445 z confidence interval construction, 258 Expected values binomial distribution, 149 discrete distributions, 140 Poisson distribution, 158 Experimental designs completely randomized design, 406–417 defined, 404 factorial, 436–451 introduction to, 404–406 randomized block, 426–436 repeated measures, 426 Experiments binomial, 144 defined, 96 probability, 169 random, 169 Exponential distribution. See also Continuous distributions characteristics, 202 computer use in determining, 205 defined, 202 demonstration problem, 204–205 determining, 202–203 graphs, 203 interarrival times, 203 parameter, 203 Poisson distribution versus, 202 probabilities, 203–204 probability density function, 203, 208 summary, 208 Exponential regression model, 556 Exponential smoothing. See also Smoothing algebraic rearrangement, 600 defined, 599 demonstration problem, 600–602 formula, 599, 600 Holt’s two-parameter, 609–610 process, 599–600 summary, 632 Winters’ three-parameter, 614–615 F distribution defined, 382 degrees of freedom, 382 illustrated, 383 percentage points, 782–791 table, one-way ANOVA, 411 table values, 383, 384 Factorial designs. See also Experimental designs advantages, 436 applications, 437 defined, 436 demonstration problem, 445–447 illustrated, 437 interaction, 439–444 statistically testing, 438–439 summary, 453–454 table, 441 treatment levels, 441 with two treatments, 437 Factors, 404 Failure Mode and Effects Analysis (FMEA), 730 Finite correction factor, 235 confidence interval estimation with, 256 demonstration problem, 256–257 Finite populations mean, testing, 301–302 sampling from, 235 First-differences approach, 620 First-order models, 548 Fishbone diagram. See Cause-and-effect diagrams Flowcharts. See also Process analysis defined, 733 illustrated example, 735 in manufacturing settings, 734 symbols, 734 Forecasting computer use, 640–642 database analysis, 638 defined, 589 error, 591 error measurement, 591 ethical considerations, 632 formulas, 633 index numbers, 623–629 introduction to, 590–593 mean absolute deviation (MAD), 591–592 mean square error (MSE), 592 problems, 593 seasonal effects, 611–616 smoothing techniques, 594–604 summary, 632–633 time-series, 588–642 time-series components, 590–591 trend analysis, 604–610 with trend lines, 500–501 Forward selection, 572 Frames defined, 219 overregistration, 219 in simple random sampling, 220 underregistration, 219 Frequency distributions. See also Grouped data class midpoints, 18 construction of, 18, 36 cumulative frequencies, 19–20 demonstration problem, 19–20 Excel, 41–42 F
Index 829 illustrated, 17 range, 18 relative frequencies, 18–19 Frequency polygons cumulative, 24 defined, 23 Excel, 24, 42 illustrated, 24 Friedman test. See also Nonparametric statistics assumptions, 699 computing, 699–700 defined, 699 demonstration problem, 702–703 example, 700–701 formula, 700 hypotheses, 699 Minitab, 701 summary, 710 Full regression coefficient, 518 F-value for blocks, 428 defined, 409 determination, 409 simple regression, 527 Gaussian distribution, 185 General law of addition, 103 General law of multiplication, 111–113 Good estimators, 63 Graduate Management Aptitude Test (GMAT), 187–189 Graphical depictions. See also Data graphs binomial distributions, 150–152 Poisson distributions, 159 qualitative data graphs, 27–33 quantitative data graphs, 21–27 types of, 36 Grouped data defined, 17, 36 demonstration problem, 74–75 mean, 70–71 measures of central tendency, 70–72 measures of variability, 72–75 median, 71–72 mode, 72 ungrouped data versus, 17–18 G H Healthy residual graphs, 479–480 Heteroscedasticity, 479 Histograms. See also Data graphs axes, 21, 22 construction, 21 defined, 21 examples, 22, 23 Minitab, 22, 43, 230 in quality control, 738 sample means, 229, 230 using, 23 Holt’s two-parameter exponential smoothing method, 609–610 Homoscedasticity, 479 Honestly significantly difference (HSD) test. See also Multiple comparisons defined, 418 formula, 419 Minitab, 420 treatment levels, 418 HTAB system. See also Hypothesis testing illustrated, 295 paradigm, 296 process, 295 tasks, 295–297 Hypergeometric distributions. See also Discrete distributions characteristics, 164–165 computer use for, 166 defined, 164, 169 demonstration problems, 166 formula, 165 outcomes, 164 parameters, 165 probabilities, 165 uses, 165 Hypotheses alternative, 292 defined, 290 Friedman test, 699 Kruskal-Wallis test, 694 null, 292 one-sample runs test, 673 research, 291–292 substantive, 294–295 types of, 291, 334 Wilcoxon matched-pairs signed rank test, 686 Hypothesis testing, 247–248, 288–341 computer use, 340 database analysis, 338 defined, 290 difference of two means (independent samples), 355–359 difference of two means (z statistic), 347–350 ethical considerations, 334 formulas, 335 with HTAB system, 295–297 introduction to, 290–299 mean with finite population, 301–302 nonrejection region, 297–298 one-tailed, 293–294, 309, 334 one-way ANOVA, 407 population mean (t statistic), 308–315 population mean (z statistic), 299–308 population proportion, 315–321 population variance, 321–324 p-value, 302 rejection region, 297–298 related populations, 365–367 slope, 489–494 steps, 295 summary, 334–335 tree diagram taxonomy, 345 two population proportions, 375–379 two population variances, 382–389 two-tailed, 293, 329, 334 Type I errors, 298 Type II errors, 298–299, 324–333 Independent events, 98–99 demonstration problems, 119–121 formula, 119 Independent samples defined, 344 difference of two means, 355–364 Independent variables addition of, 619–620 defined, 404 levels, 405 nonlinear, 580 in regression analysis, 469, 518 I Index numbers. See also Forecasting defined, 613 demonstration problem, 627–628 Laspeyres price index, 626–627 motivation for, 624 Paasche price index, 627 simple, 624 summary, 632–633 unweighted aggregate price, 624–625 uses, 623 weighted aggregate price, 625–626 Indicator variables. See also Variables in business research, 560 defined, 560 as dichotomous, 561 qualitative, 560–561 summary, 580 t ratios, 562 Inferential statistics applications, 6 defined,6,12 process illustration, 7 types of,9,12 Inferential techniques branch of tree diagram, 252, 291 confidence intervals, 344 In-process quality control, 723 Input processing errors, 226 Interaction. See also Factorial designs defined, 439 effects, 440, 442 examples, 439–440 graphical depiction, 440 regression analysis, 352 regression models with, 352–354 Interquartile range, 56 Intersections, 98 Interval estimates, 253 Interval-level data measurement, 8 Irregular fluctuations, 590 Ishikawa diagram. See Cause-and-effect diagrams Joint probability defined, 101 table, 111 Judgment sampling, 225 “Juran Trilogy,” 725 Just-in-time (JIT) inventory systems, 728–729 J K Kruskal-Wallis test. See also Nonparametric statistics analysis example, 695 computation process, 695 defined, 694 demonstration problem, 696–697 formula, 694 hypotheses, 694 Minitab, 695 summary, 710 Kurtosis, 78, 84 L Lambda () defined, 155 value, 157 Large-sample runs test, 675–676 Laspeyres price index, 626–627
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With WileyPLUS: This online teachin
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6 TH EDITION Business Statistics Fo
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Vice President & Publisher George H
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BRIEF CONTENTS UNIT I UNIT II UNIT
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x Contents 3.5 Descriptive Statisti
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xii Contents Statistical Hypotheses
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xiv Contents Forward Selection 572
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PREFACE The sixth edition of Busine
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Preface xix all of the inferential
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Preface xxi very limited access to
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Preface xxiii Interpreting the Outp
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Preface xxv ACKNOWLEDGMENTS John Wi
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UNIT I INTRODUCTION The study of bu
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Statistics Describe the State of Bu
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1.2 Basic Statistical Concepts 5 In
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1.3 Data Measurement 7 FIGURE 1.1 P
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1.3 Data Measurement 9 In addition,
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1.3 Data Measurement 11 Statistical
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Analyzing the Databases 13 1.3 Give
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Case 15 ASSIGNMENT Use the database
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Energy Consumption Around the World
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2.1 Frequency Distributions 19 TABL
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2.2 Quantitative Data Graphs 21 cla
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2.2 Quantitative Data Graphs 23 FIG
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2.2 Quantitative Data Graphs 25 FIG
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2.3 Qualitative Data Graphs 27 2.9
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2.3 Qualitative Data Graphs 29 FIGU
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2.3 Qualitative Data Graphs 31 STAT
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2.4 Graphical Depiction of Two-Vari
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Problems 35 Pie Charts for World Oi
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Supplementary Problems 37 2.19 Cons
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Supplementary Problems 39 include t
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Using the Computer 41 In 1983, the
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Using the Computer 43 ■ ■ ■ t
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CHAPTER 3 Descriptive Statistics LE
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48 Chapter 3 Descriptive Statistics
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ƒ ƒ 58 Chapter 3 Descriptive Stat
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CHAPTER 4 Probability LEARNING OBJE
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94 Chapter 4 Probability FIGURE 4.1
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96 Chapter 4 Probability Subjective
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98 Chapter 4 Probability FIGURE 4.3
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100 Chapter 4 Probability THE mn CO
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102 Chapter 4 Probability FIGURE 4.
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104 Chapter 4 Probability TABLE 4.2
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106 Chapter 4 Probability By the ge
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108 Chapter 4 Probability P (neithe
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110 Chapter 4 Probability 4.10 Use
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ƒ ƒ 112 Chapter 4 Probability ran
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114 Chapter 4 Probability DEMONSTRA
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116 Chapter 4 Probability 4.21 A st
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ƒ 118 Chapter 4 Probability The se
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ƒ 120 Chapter 4 Probability STATIS
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122 Chapter 4 Probability the South
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124 Chapter 4 Probability TABLE 4.8
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126 Chapter 4 Probability Event Pri
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128 Chapter 4 Probability ETHICAL C
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130 Chapter 4 Probability TESTING Y
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132 Chapter 4 Probability 4.49 In a
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UNIT II DISTRIBUTIONS AND SAMPLING
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Life with a Cell Phone As early as
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5.2 Describing a Discrete Distribut
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5.2 Describing a Discrete Distribut
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5.3 Binomial Distribution 143 5.3 T
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5.3 Binomial Distribution 145 selec
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5.3 Binomial Distribution 147 DEMON
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ƒ 5.3 Binomial Distribution 149 TA
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5.3 Binomial Distribution 151 FIGUR
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Problems 153 5.8 Use the probabilit
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5.4 Poisson Distribution 155 time i
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5.4 Poisson Distribution 157 Soluti
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5.4 Poisson Distribution 159 FIGURE
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5.4 Poisson Distribution 161 The Po
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Problems 163 data set as a basis fo
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5.5 Hypergeometric Distribution 165
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Problems 167 5.5 PROBLEMS 5.27 Comp
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Key Terms 169 problem, but it can b
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Supplementary Problems 171 5.36 Use
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Supplementary Problems 173 Suppose
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Case 175 ANALYZING THE DATABASES se
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Using the Computer 177 ■ in a col
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The Cost of Human Resources What is
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6.1 The Uniform Distribution 181 FI
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6.1 The Uniform Distribution 183 DE
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6.2 Normal Distribution 185 FIGURE
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6.2 Normal Distribution 187 TABLE 6
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6.2 Normal Distribution 189 This pr
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6.2 Normal Distribution 191 The pro
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6.2 Normal Distribution 193 DEMONST
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Problems 195 6.9 According to the I
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6.3 Using the Normal Curve to Appro
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6.3 Using the Normal Curve to Appro
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Problems 201 The answer obtained by
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6.4 Exponential Distribution 203 FI
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Problems 205 TABLE 6.6 Excel and Mi
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Summary 207 The area associated wit
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Supplementary Problems 209 6.36 Wor
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Supplementary Problems 211 availabi
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Using the Computer 213 that sales a
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CHAPTER 7 Sampling and Sampling Dis
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218 Chapter 7 Sampling and Sampling
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248 Making Inferences About Populat
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CHAPTER 8 Statistical Inference: Es
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252 Chapter 8 Statistical Inference
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Word-of-Mouth Business Referrals an
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9.1 Introduction to Hypothesis Test
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9.2 Testing Hypotheses About a Popu
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Problems 307 one-tailed hypothesis,
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Problems 313 Excel does not have a
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9.4 Testing Hypotheses About a Prop
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9.5 Testing Hypotheses About a Vari
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Problems 323 DEMONSTRATION PROBLEM
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9.6 Solving for Type II Errors 325
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Problems 333 9.40 The New York Stoc
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Supplementary Problems 335 with an
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Supplementary Problems 337 and she
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Case 339 CASE FRITO-LAY TARGETS THE
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Using the Computer 341 ■ To begin
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Online Shopping The use of online s
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Proportions 2-Samples Chapter 10 St
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10.1 Hypothesis Testing and Confide
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Problems 353 10.2 Use the following
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Problems 363 Test your conjecture,
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10.3 Statistical Inferences for Two
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Problems 373 Client Before After 1
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10.4 Statistical Inferences about T
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Problems 381 b. H 0 : p 1 - p 2 = 0
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10.5 Testing Hypotheses about Two P
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Problems 389 City 1 City 2 3.43 3.3
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Summary 391 SUMMARY Business resear
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Supplementary Problems 393 TESTING
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Supplementary Problems 395 machine
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Case 397 ANALYZING THE DATABASES 1.
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Using the Computer 399 ■ ■ add-
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CHAPTER 11 Analysis of Variance and
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404 Chapter 11 Analysis of Variance
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UNIT IV REGRESSION ANALYSIS AND FOR
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Predicting International Hourly Wag
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12.1 Correlation 467 FIGURE 12.1 Fi
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12.2 Introduction to Simple Regress
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12.3 Determining the Equation of th
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12.3 Determining the Equation of th
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Problems 475 Using these values, th
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12.4 Residual Analysis 477 Raw Stee
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12.4 Residual Analysis 479 FIGURE 1
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12.4 Residual Analysis 481 DEMONSTR
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Problems 483 Cost of Milk (per gall
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12.5 Standard Error of the Estimate
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12.6 Coefficient of Determination 4
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12.7 Hypothesis Tests for the Slope
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12.8 Estimation 495 result, yieldin
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12.8 Estimation 497 FIGURE 12.16 Mi
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12.9 Using Regression to Develop a
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12.9 Using Regression to Develop a
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Problems 503 Solution Shown here is
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12.10 Interpreting the Output 505 F
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12.10 Interpreting the Output 507 S
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Supplementary Problems 509 KEY TERM
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Supplementary Problems 511 publishe
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Case 513 Frequency 7 6 5 4 3 2 1 0
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Using the Computer 515 Cumulative H
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Are You Going to Hate Your New Job?
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13.1 The Multiple Regression Model
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13.1 The Multiple Regression Model
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Problems 523 Solution The following
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13.2 Significance Tests of the Regr
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13.2 Significance Tests of the Regr
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Problems 529 13.9 Using the data in
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13.3 Residuals, Standard Error of t
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13.3 Residuals, Standard Error of t
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13.4 Interpreting Multiple Regressi
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13.4 Interpreting Multiple Regressi
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Summary 539 SUMMARY OUTPUT Regressi
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Supplementary Problems 541 TESTING
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Case 543 ANALYZING THE DATABASES 1.
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CHAPTER 14 Building Multiple Regres
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548 Chapter 14 Building Multiple Re
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CHAPTER 15 Time-Series Forecasting
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590 Chapter 15 Time-Series Forecast
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CHAPTER 16 Analysis of Categorical
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CHAPTER 17 Nonparametric Statistics
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672 Chapter 17 Nonparametric Statis
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18.2 Process Analysis 733 18.2 PROC
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18.2 Process Analysis 735 FIGURE 18
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18.2 Process Analysis 737 FIGURE 18
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18.3 Control Charts 739 charts are
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18.3 Control Charts 741 UCL and LCL
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18.3 Control Charts 743 Using the s
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18.3 Control Charts 745 Compute R R
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18.3 Control Charts 751 below the c
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Problems 753 18.6 A machine operato
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Problems 755 a. 125 X bar Chart 3.0
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Formulas 757 identifying the effect
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Supplementary Problems 759 five con
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Analyzing the Databases 761 12.5 X
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Using the Computer 763 Sample Mean
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APPENDIX A Tables Table A.1: Random
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Appendix A Tables 767 TABLE A.2 Bin
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Appendix A Tables 775 TABLE A.3 Poi
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Page 835 and 836: APPENDIX B Answers to Selected Odd-
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Page 845 and 846: GLOSSARY A a posteriori After the e
Page 847 and 848: Glossary 817 decision table A matri
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Page 856 and 857: 826 Index Centerline. See also Cont
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Page 862 and 863: 832 Index Populations accessing for
Page 864 and 865: 834 Index Samples defined, 5 depend
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Page 869 and 870: Decision Making at the CEO Level CE
Page 871 and 872: 19.1 The Decision Table and Decisio
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Page 879 and 880: Problems C19-13 d. Compute an oppor
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Page 887 and 888: Problems C19-21 Much information ha
Page 889 and 890: 19.4 Revising Probabilities in Ligh
Page 895 and 896: Problems C19-29 The worth of the sa
Page 897 and 898: Problems C19-31 Decision Making at
Page 899 and 900: Supplementary Problems C19-33 for e
Page 901 and 902: Supplementary Problems C19-35 Decis
Page 903 and 904: Case C19-37 Following these initiat
Page 905 and 906: Critical Values from the t Distribu
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