statistics for business and...

,

STATISTICS "FOR

BUSINESS AND

ECONOMICS ~ SECOND EDITION

ANDERSON SWEENEY WILLIAMS

FREEMAN SHOESMITH

~... SOUTH-WESTERN t (ENGAGE Learning'

Australia· Brazil· Japan· Korea· Mexico· Singapore. Spain. United Kingdom. United States

Brief contents

Preface and Acknowledgements xvii

About the Authors xx

Walk-through Tour xxii

Accompanying Website xxiv

Supplements xxv

Data and Statistics I

2 Descriptive Statistics: Tabular and Graphical Presentations 21

3 Descriptive Statistics: Numerical Measures 67

4 Introduction to Probability 117

5 Discrete Probability Distributions 153

6 Continuous Probability Distributions 187

7 Sampling and Sampling Distributions 219

8 Interval Estimation 251

9 Hypothesis Tests 283

10 Statistical Inference about Means and Proportions with Two Populations 335

I I Inferences about Population Variances 373

12 Tests of Goodness of Fit and Independence 399

13 Analysis of Variance and Experimental Design 429

14 Simple linear Regression 489

15 Multiple Regression 555

16 Regression Analysis: Model Building 613

17 Index Numbers 659

18 Forecasting 683

ix

, BRIEF CONTENTS

19 Non-parametric Methods 727

20 Statistical Methods for Quality Control 767

21 Decision Analysis 799

22 Sample Surveys (on CD)

Appendix A References and Bibliography 835

Appendix B Tables 837

Appendix C Summation Notation 867

Appendix D Answers to Even-numbered Exercises 870

Glossary 9 I I

Index 920

Contents

Preface and Acknowledgments xvii

About the Authors xx

Walk-through Tour xxii

Accompanying Website xxiv

Supplements xxv

Data and Statistics

Learning objectives 2

Statistics in practice: The Economist 3

1.1 Applications in business and economics 3

1.2 Data 5

1.3 Data sources 8

1.4 Descriptive statistics I 2

1.5 Statistical inference 14

1.6 Computers and statistical analysis 15

Exercises 1-13 I 6

Summary 20

Key terms 20

2 Descriptive Statistics: Tabular and Graphical Presentations 21


Statistics in practice: YouGov and Brandlndex 23'

2.1 Summarizing qualitative data 22

Exercises 1-7 26

2.2 Summarizing quantitative data 28

Exercises 8-19 38

2.3 Cross-tabulations and scatter diagrams 40

Exercises 20-25 46

Summary 49

Keytenms 50

Key formulae 50

Case problem: In The Mode Fashion Stores 50

Software Section for Chapter 2 52

Tabular and graphical presentations using MINITAB 52

Tabular and graphical presentations using EXCEL 54

Tabular and graphical presentations using PASW 64

J Descriptive Statistics: Num.l~rical Measures 67 ({7J"


Statistics in practice: TV audience measureme~1 69 ~~

3.1

3.2

3.3

Measures of location 68

Exercises 1-8 75

Measures of variability 76

Exercises 9-1 6 8 I

Measures of distributional shape, relative location,

and detecting outliers 82

Exercises 17-24 86

3.4 Exploratory data analysis 88

Exercises 25-31 89

3.5 Measures of association between two variables 91

Exercises 32-35 97

3.6 The weighted mean and working with grouped

data 99

Exercises 36-39 102

Summary 104

Key tenms I 04

Key formulae 105

Case problem: Company. profiles 106

Software Section for Chapter 3 I 08

Descriptive statistics using MINITAB 108

Descriptive statistics using EXCEL I I I

Descriptive statistics using PASW I 14

4 Introduction to Probability 117

Learning objectives I I 8

Statistics in practice: Combating junk e-mail I 19

4.1 Experiments, counting rules and assigning

probabilities I 19

Exercises I-I I 127

4.2 Events and their probabilities 129

Exercises I 2-17 I 30

4.3 Some basic relationships of probability 132

Exercises 18-20 136

xi

CONTENTS

4.4 Conditional probability

Exercises 21-26

4.5 Bayes' theorem

Exercises 27-34

Summary 150 Key terms 150 Key formulae 150

142

143

148

137

Case problem: BAC and the Alcohol Test 15 I

5 Discrete Probability Distributions 153


Statistics in practice: Improving the performance reliability

of combat aircraft 155

5.1 Random variables 154

Exercises 1-6 157

5.2 Discrete probability distributions 158

Exercises 7-13 160

5.3 Expected value and variance 162

Exercises 14-20 164

5.4 Binomial probability distribution 166

Exercises 21-27 174

5.5 Poisson probability distribution 175

Exercises 28-32 177

5.6 Hypergeometric probability distribution

Exercises 33-37

Summary 181 Key terms I 81 Key formulae 182

180

Case problem: Adapting a Bingo Game 183

Software Section for Chapter 5 I 84

178

Discrete probability distributions with MINITAB 184 Discrete probability distributions with EXCEL 184 Discrete probability distributions with PASW 186

6 Continuous Probability Distributions 187

Learning objectives I 88

Statistics in practice: Assessing the effectiveness of new

medical procedures 189

6.1 Uniform probability distribution 188

Exercises 1-7 192

6.2 Normal probability distribution 193

Exercises 8-19 202

6.3 Normal approximation of binomial

probabilities 204

Exercises 20-22 206

6.4 Exponential probability distribution 207

Exercises 23-27 209

Summary 211 Key terms 21 I

Key formulae 21 I

Case problem I: Prix-Fischer Toys 21 2

Case problem 2: Queu'lng pattems in a retail fumiture

store 2 [3

Software Section for Chapter 6 2 [ 5

Continuous probability distributions with M[N[T AB 2 [4

Continuous probability distributions with

EXCEL 2[6 Continuous probability distributions with

PASW 217

1 Sampling and Sampling Distributions 2[9


Statistics in practice: Copyright and Public Lending

Right 22[

7.1 The EAI sampling prbblem 222

7.2 Simple random sampling 222

Exercises [-6 225

7.3 Point estimation 226

Exercises 7-[ 2 228

7.4 Introduction to sampling distributions

7.5 Sampling distribution of X 23 [ .

Exercises 13-22 239

7.6 Sampling distribution of P 240

Exercises 23-3 [ 244



Random sampling using M[N[TAB 247 Random sampling using EXCEL 248 Random sampling using PASW 248

229

8 Interval Estimation 251


Statistics in practice: How accurate are opinion polls and

market research surveys? 253

8.1 Population mean: (J known 252

Exercises 1-7 257

8.2 Population mean: (J unknown 258

Exercises 8-17 263

8.3 Determining the sample size 265

Exercises 18-24 267

8.4 Population proportion

Exercises 25-34 271


268

Case problem I: International bank 274

Case problem 2: Young Professional Magazine 275


Interval estimation using MINITAB 277

Interval estimation using EXCEL 279 Interval estimation using PASW 281

9 Hypothesis Tests 283


Statistics in practice: Monitoring the quality of latex

condoms 285

9.1 Developing null and alternative hypotheses 284

Exercises 1-4 287

9.2 Type I and Type II errors 288

Exercises 5-7 290

9.3 Population mean:(J known 290

Exercises 8-1 6 303

9.4 Population mean: (J unknown 305

Exercises 17-24 309

9.5 Population proportion 31 I

Exercises 25-32 314

9.6 Hypothesis testing and decision-making 315

9.7 Calculating the probability of Type 11 errors 3 I 6

Exercises 33-38 319

9.8 Determining the sample size for hypothesis tests

about a population m~an 321

Exercises 39-43 323


Case problem: Quality Associates 326


Hypothesis testing using MINITAB 328 Hypothesis testing using EXCEL 331 Hypothesis testing using PASW 333

CONTENTS

1'1 10 Statistical Inference Abcfut Means and Proportions with Two Populations 335


Statistics in practice: Fisons Corporation 337

10.1 Inferences about the difference between two

population means: (JI and (J2 known 336

Exercises 1-6 342


population means: (JI and (J2 unknown 344

Exercises 7-16 348


population means: matched samples 352

Exercises 17-22 354


population proportions 357

Exercises 23-29 361


Case problem: Par Products 365


Inferences about two populations using MINITAB 366 Inferences about two populatiof}s using EXCEL 368 Inferences about two populations using PASW 370

I I Inferences about Population Variances 373


Statistics in practice: Takeovers and mergers in the UK

brewing industry 375

11.1 Inferences about a population variance 374

Exercises 1-12 381

CONTENTS

1 1.2 Inferences about two population variances 384

Exercises 13-22 388

Summary 391

Key formulae 391

Case problem: Global economic problems in 2008 391

Software Section for Chapter I I 393

Population variances using MINITAB 393

Population variances using EXCEL 395

Population variances using PASW 396

12 Tests of Goodness of Fit and Independence 399


Statistics in practice: National lotteries 40 I

12.1 Goodness of fit test: a multinomial

population 400

Exercises 1-7 404

12.2 Test of independence 405

Exercises 8-1 6 409

12.3 Goodness of fit test: Poisson and normal

distributions 412

Exercises 17-22 419

Summary 421

Key terms 421

Key formulae 421

Case problem I: Evaluation of Management School website

pages 421

Case problem 2: Checking for randomness in Lotto

draws 423


Tests of goodness of fit and independence using

MINITAB 424


EXCEL 425


PASW 427

I J Analysis of Variance and Experimental Design 429


Statistics in practice: Product customization and

manufacturing trade-offs 431

13.1 An introduction to analysis of variance 430

13.2 Analysis of variance: testing for the equality of

k population means 434

Exercises 1-10 441

13.3 Multiple comparison procedures 445

Exercises I I-I 8 449

13.4 An introduction to experimental design 450" j;,-:;"

13.5 Completely randomized designs 453

Exercises 19-33 456

13.6 Randomized block design 459

Exercises 34-39 464

13.7 Factorial experiments 466

Exercises 40-44 471

Summary 474

Key tenms 474

Key formulae 474

Case problem I: Wentworth Medical Centre 477

Case problem 2: Product Design Testing 478

Software Section for Chapter I 3 480

Analysis of variance and experimental design using

MINITAB 480


EXCEL 481


PASW 485

14 Simple linear Regression 489


Statistics in practice: Foreign direct investment (FDI) in

China 491

14.1 Simple linear regression model 49 I

14.2 Least squares method 494

Exercises 1-6 498

14.3 Coefficient of determination 500

Exercises 7-12 505

14.4 Model assumptions 506

14.5 Testing for significance 508

Exercises I 3-17 5 14

14.6 Using the estimated regression equation for

estimation and prediction 515

Exercises 18-21 520

14.7 Computer solution 520

Exercises 22-24 522

14.8 Residual analysis: validating model

assumptions 523

14.9 Residual analysis: autocorrelation 531

Exercises 25-28 534

14.10 Residual analysis: outliers and influential

observations 536

Exercises 29-30 541

Summary 543

Key terms 543

Key formulae 543

Case problem I: Investigating the relationship between

weight and triglyceride level reduction 546

Case problem 2: US Department of T ransportatioll) 547

Case problem 3: Can we detect dyslexia? 548


Regression analysis using MINITAB 550

Regression analysis using EXCEL 550

Regression analysis using PASW 553

15 Multiple Regression 555


Statistics in practice: Jura 557

15.1 Multiple regression model 556

15.2 Least squares method 558

Exercises 1-6 563

15.3 Multiple coefficient of determination 565

Exercises 7-1 I 567

15.4 Model assumptions 568

15.5 Testing for significance 569


15.6 Using the estimated regression equation for

estimation and prediction 575

Exercises 17-19 576

15.7 Qualitative independent variables 577

Exercises 20-25 583

15.8 Residual analysis 586

Exercises 26-29 591

15.9 Logistic regression 593

Exercises 30-32 602

Summary 605

Key terms 605

Key formulae 606

Case problem: Consumer Research 608


Multiple regression using MINITAB 609

Logistic regression using MINITAB 609

Multiple regression using EXCEL 610

Multiple regression using PASW 61 I

Logistic regression using PASW 612

CONTENTS

16 Regression Analysis: Mod§1' -f>',

Building 613' ':


Statistics in practice: Selecting a university 615; 16.1 General linear model 614

Exercises 1-8 627

16.2 Determining when to add or delete

variables 63 I

Exercises 9-13 633

16.3 Analysis of a larger problem 637

16.4 Variable selection procedures 642

Exercises 14--.1 8 645

Summary 654

Key terms 654

Key formulae 654

Case problem I: Unemployment study 655

Case problem 2: Treating obesity 656

17 Index Numbers 659


Statistics in practice: Index numbers in the headlines 661

17.1 Price relatives 660

17.2 Aggregate price index numbers 662

Exercises 1-8 665

17.3 Computing an aggregate price index from price

relatives 667

Exercises 9-1 3 669

17.4 Some important price index numbers 670

17.5 Deflating a series using a price index number 671

Exercises 14-17 673

17.6 Price index numbers: other considerations 675

17.7 Quantity index numbers 676

Exercises 18-21 677

Summary 679

Key terms 679

Key formulae 679

Case problem: Indices 680

CONTENTS

18 Forecasting 683


Statistics in practice: Asylum applications 685

IS.I Components of a time series 686

IS.2 Smoothing methods 689

Exercises 1-9 696

IS.3 Trend projection 698

Exercises I 0-15 702

IS.4 Trend and seasonal components 703


IS.5 Regression analysis 713

IS.6 Qualitative approaches 715


Case problem I: Forecasting food and beverage sales 718

Case problem 2: Allocating patrols to meet future demand

for vehicle rescue 719

Software Section for Chapter 18 72 I

Forecasting using MINITAB 721 Forecasting using EXCEL 723 Forecasting using PASW 724

I 9 Non-parametric Methods 727


Statistics in practice: Coffee lovers' preference: Costa,

Starbucks and Caffe Nero 729

19.1 Sign Test 730

Exercises 1-8 734

19.2 Wilcoxon signed-rank test 736

Exerc'lses 9-12 738

19.3 Mann-Whitney-Wilcoxon test 740

Exercises 13-17 745

19.4 Kruskal-Wallis test 747

Exercises 18-21 749

19.5 Rank correlation 750

Exercises 22-26 752


Case problem: Company Profiles 11 756


Non-parametric methods using MINITAB 758 Non-parametric methods using PASW 761

20 Statistical Methods for Quality Control 767


Statistics in practice: ABC Aerospace 769

20.1 Statistical process control 769

Exercises 1-9 78 I

20.2 Acceptance sampling 784

Exercises 10-15 79 I


Case problem: ISN Company 794


Control charts using MINITAB 796 Control charts using PASW 796

21 Decision Analysis 799


Statistics in practice: Military hardware procurement in

Greece 80 I

21 .1 Problem formulation 800

21.2 Decision-making with probabilities 803

Exercises 1-7 807

21.3 Decision analysis with sample information 8 I I

Exercises 8-1 3 8 17

21.4 Computing branch probabilities using Bayes'

theorem 821

Exercises 14-17 824

Summary 826 Key tenms 826 Key formulae 826

Case problem I: Stock-ordering at Mintzas 827 Case problem 2: Production strategies 828

21.5 Solving the PDC problem using TreePlan 829

22 Sample Surveys (on CD)

Complete chapter found online and within the book's

accompanying CD-ROM

Appendix A References and Bibliography 835 Appendix B Tables 837 Appendix C Summation Notation 867 Appendix D Answers to Even-numbered Exercises 870 Glossary 9 I I

Index 920

920

Index

ABC Aerospace 769 absolute values 736 acceptance criterion 785-6,912 acceptance sampling 769, 784-91, 912

computing the probability 786-8 selecting a plan 789-91

accounting 3-4 addition law 133-5, 134, 136, 151, 912 adjusted multiple coefficient of determination

566,606,912 aggregate price index 662, 912

computing from price relatives 667-9 air traffic controller stress test 460-1 allocating patrols for vehicle rescue 719-20 alternative hypothesis 284, 287, 912 analysis of variance 430-4

testing for equality 434-41 ANOVA procedure 461-2,467-8,552 ANOVA table 439,454-5,513,912 area as measure of probability 190-1 assignable causes 770, 912 assigning probabilities 124-6 association, measures of 91-7 asylum applications 685 autocorrelation 531,912 autoregressive model 715,912 average range 775, 794

BAC and alcohol test 151-2 backward elimination 643-4 banking 274-5 bar chart 25,55-7,912 bar graph 25, 912 basic requirements for assigning probabilities

124,912 Bayes' theorem 144-7, 151,821,912 best-subsets regression 644-5 bimodal72 bingo game 183 binomial experiment 167, 912

binomial probability distribution 167-8, 912 binomial probability function 168, 171-2,.182,

786,794,912 binomial probability, normal approximation

204-6 binomial probability tables 172-3 blocking 463, 912 bound on the sampling error 912, ch22 p8 box plot 88-9, 912 branch 803, 912 Brandindex 23 brewing industry 375 British Crime Survey Home Office ch22 3

causal forecasting 685 causal forecasting methods 715, 912 census 14,912 central limit theorem 234-6,235,912 chance event 800, 912 chance nodes 803, 912 Chebyshev's theorem 84-5, 912 class

limits 30-1 number 29 open-ended 31 width 29

class midpoint 31,912 classical method 125, 912 cluster sampling 912, ch22 p22-8 coefficient of determination 500,503,544,912 coefficient interpretation 561-3 -coefficient of variation 80-1, 164, 913 coffee preferences 729 combat aircraft 155 combinations 122-3 common causes 770, 913 comparison Type I error rate 448,913 complement of A 132,913 completely randomized design 451,453-5,476,

481-2,485,913

computers 15-16 computing branch probabilities 821-3 computing probabilities

accepting a lot 786-8 exponential distribution 207-9 normal distribution 200 using the complement 150

conditional probability 137, 139, 151,821,913 confidence coefficient 256,913 confidence interval 256, 302, 516, 545, 913 confidence interval for (beta) 511 confidence interval for mean value of Y

516-17 confidence level 256, 913 consequence 800, 913 Consumer Price Index (CPI) 670, 913 consumer research 608 consumer's risk 785,913 contingency table 406,913 continuity correction factor 205, 913 continuous normal distribution 205 continuous random variable 156, 913 control chart 769, 770-1, 781,913 control limits for an np chart 780 control limits for a p chart 779, 794 control limits for an R chart 777, 794 control limits for an x chart 775, 793, 794 convenience sampling 913, ch22 p4 converting to standard normal distribution 211 Cook's distance measure 590-1,607,913 copyright 221 correlation coefficient 93, 94-8, 504, 913

interpretation 95-7 counting rules 120-4

combinations 122-3, 150 multiple-step experiments 121 permutations 124, 150

covariance 91-3, 913 interpretation 92-3

critical value 293,295-7,299-300,913 cross-sectional data 7, 913 cross-tabulation 40-3,53-4, 65, 913 cumulative frequency distribution 34,913 cumulative percentage frequency distribution

34,913 cumulative relative frequency distribution 34

c'

curvilinear modelling 616-19 cyclical component 688, 712, 913

data 5,49,913 acquisition errors 11-12 sources 8-12 types 5-,-8

data set 5,913 decision analysis with sample information

811-17 decision nodes 803, 913 decision strategy 813-17, 913 decision tree 803,811-13,829-33,913 decision-making 315-16

with probabilities 803-7 deflating a series 671-3 degree of belief 125 degrees of freedom 258,345,364,913 Delphi method 716,913 dependent variable 490,621-6,913 descriptive statistics 12-13,913 deseasonalized time series 708-9, 913-14 deviation about the mean 78 discrete binomial distribution 205 discrete random variable 154-6, 914 discrete uniform probability distribution

158-60,914

INDEX

discrete uniform probability function 159, 182 distance interval 177 dot plot 31-2,52,914, dummy variable 579, 914 Durbin-Watson test 531,533,914 dyslexia 548-9

EAI sampling problem 222 economics 4-5 element 5,914, ch22 p2 empirical rule 85, 914 equation for linear trend 700, 718 estimated logistic regression equation 596,

607,914 estimated logit 602,607,692,914 estimated multiple regression equation 558,

606,914 estimated regression equation 493-4,

515-19,914

, INDEX

estimated regression line 493 estimated simple linear regression equation 544 estimated standard deviation of b 510 Eurodistributor Company 559-61 event 129-30, 914 EXCEL

analysis of variance and experimental design 481-5

continuous probability distribution 216 descriptive statistics 111-13 discrete probability distributions 184-6 forecasting 723-==4 hypothesis testing 331-3 inferences about two populations 368-70 C>

interval estimation 279-81 multiple regression 610-11 PivotTable Report 60-4 population variances 395-6 random sampling 248 regression analysis 550-2 tabular/graphical presentations 54-64 tests of goodness of fit and independence

425-6 expected frequencies for contingency tables

under assumption of independence 421 expected value (and variance) for binomial

distribution 173-4, 182 expected value approach 804-5, 914 expected value of discrete random variable

163, 182 expected value (BY) 162-3, 826,914 expected value for hypergeometric distribution

179, 182 expected value of P 241,246 expected value of perfect information (EYPI)

806-7,826,914 expected value of sample information (EYSI)

817,826,914 expected value of X 232, 246 experiment 119-20,914 experimental design 450-2 experimental units 451,914 experimentwise Type I error rate 448,914 expert judgment 716 . exploratory data analysis 35, 914 exponential probability density function 211 exponential probability distribution 207-9, 914

exponential smoothing 693-4, 718, 721-2, 723-4,725,914

F-test 511-13,569-72,606 F-test for adding or deleting p-q variables

631,654 F-test statistic 459, 476 F-test for two populations 394, 395-6 factor 450,914 factorial experiments 466, 476, 481, 484-5,.

486-7, 914 ~;~ finance 4 finite population correction factor 233-4,914 finite population sampling 222-4 first order autocorrelation 532, 545 Fisher's LSD 445-7, 475, 476 Fisons Corporation 337 five-number summary 88, 914 forecast 684,685-6,914

accuracy 692-3,695-6 economic 4-5

forecasting food and beverage sales 718-19 forward selection 642-3 frame 914, ch22 p3 frequency distribution 914

qualitative data 22-3,54-5 quantitative data 28-31,57-8

general linear model 614, 654, 914 global economic problems, 391-2 goodness of fittest 401-4,914 Greer Tyre Company problem 200-2 grouped data 100-3, 106,914

high leverage points 539, 914 histogram 32-3,52-3,58-9,64,914-15 hypergeometric probability distFibuti9n 178-9,

182,915 hypergeometric probability fun~tion 178-9,

182,915 hypothesis tests 300-2, 315-16, 321-3, 340-1,

345-8,378-81,733-4

independent events 140,915 independent samples 352, 915 independent variables 490,915 inferences about population variance 374-81

inferences about two population variances 384-8

infinite population sampling 224-5 influential observation 537,589,915 interaction 467,619,915 interquartile range (IQR) 77-8, 105,915 intersection of A and B 134,915 interval estimate 252,254-6,259-61,338-40,

344,915 interval estimate of difference between two

population means 363, 364 interval estimate of population mean: (J(sigma)

known 255, 273 interval estimate of population mean: (J (sigma)

unknown 273 interval estimate of population proportion

274,358 interval estimate of population variance 391 interval estimation 301-2,344-5,376-8,516 interval scale 6-7, 728,915 intuitive approaches 716 irregular component 688-9,915 ISN Company 794-5 ith residual 500, 915

J ohansson Filtration 577-9 joint probability 138, 823,915 judgment sampling 915, ch22 pp4-5 junk e-mail 119 Jura 557

KALI785-6 Kruskal-Wallis test 747-9,756,760-1,

764-6,915

large-sample case 733, 743-5 Laspeyres price index 665, 915 latex condoms 285 least squares criterion 496, 544, 606 least squares method 494,558-9,915 length interval 177 level of significance 289, 915 leverage 586 limits 88 logistic regression 593-602 logistic regression equation 595,607,915 logit 601-2,607,915

logit transformation 601-2 lot 784,915

INDEX

management school website 421-2 Mann-Whitney-Wi1coxon (MWW) test 740-5,

755,759-60,762-4,915 margin of error 252,254-6,259-61, 9111:? marginal probability 138,915 (:"?t

marine clothing store problem 168-74 market research surveys 253 marketing 4 matched samples 352, 915 mean 68-71, 915 mean square due to error 475, 476 mean square due to treatments 475 mean square error (MS E) 508, 544, 570, 606,

692,915 mean square regression 512,570,606 measurement, scales of 5-7 measures of association between two variables

91-7 measures of variability 76-81 median 71-2,915 medical procedures 189 military hardware procurement 801 MINITAB


continuous probability distribution 215-16 control charts 796 descriptive statistics 108-10 discrete probability distributions 184 forecasting 721-3 hypothesis testing 328-31 inferences about two populations 366-8 interval estimation 277-9 logistic regression 609-10 multiple regression 609 non-parametric methods 758-61 population variances 393-5 random sampling 247 regression analysis 550 tabular/graphical presentations 52-4 tests for goodness of fit and independence

424-5 Mintzas stock-ordering 827 mode 72,915

, INDEX

monthly data 712 moving averages 689-91, 717, 723,915 multicollinearity 573-4,915 multimodal 72 multinomial distribution goodness of fit test

403-4 multinomial popUlation 400, 915-16 multiple coefficient of determination 566,

606,916 multiple comparison procedures 445-9, 916 multiple regression 491 multiple regression analysis 556, 916 multiple regression equation 558,606,916 multiple regression model 556-8, 568, 606, 916' multiple sampling plan 790-1,916 multiple-step experiments 120-2 multiplication law 141-2, 151,916

independent events 141, 151 multiplicative time series model 704, 718,

722-3,726,916 mutually exclusive events 135,916

national lotteries 401, 423 node 803, 916 nominal scale 5, 728, 916 non-parametric methods 728,916 non-probabilistic sampling 916, ch22 p4 non-sampling error 916, ch22 pp5-6 nonlinear models 626.0...7 normal approximation of binomial probabilities

204-6 normal curve 193-'-5 normal distribution 415-18 normal probability density function 211 normal probability distribution 193-200,916 normal probability plot 528,916 np chart 771,780-1,916 null hypothesis 284, 287, 916 number of experimental outcomes providing

exactly x successes in n trials 169, 182

obesity treatment 656-7 observation 5,916 odds in favour of an event occurring 599,916 odds ratio 599-600, 607, 916 ogive 34-5, 916 one-tailed test 291-2,305-6,916

operating characteristic 319 operating characteristic curve 787, 916 opinion polls 253 ordinal scale 6, 728, 916 outlier 86, 88,526,588,916 overall sample mean 475, 774, 793

p chart 771, 778-80, 916 p-value 293-5,298-9,393,395,397,

632,916 Paasche price index 665, 917 pairwise comparisons 455 Par Products 365 parameter 220,917 Pareto diagram 26 partitioning 440,917 partitioning of sum of squares 439, 475 PASW


continuous probability distribution 217-18 control charts 796-7 descriptive statistics 114-16 discrete probability distributions 186 forecasting 724-6 hypothesis testing 333-4 inferences about two populations 370-2 interval estimation 281-2 logistic regression 612 multiple regression 61 ~ -12 non-parametric methods 761-6 population variances 396-8 random sampling 248-9 regression analysis 553 tabular/graphical presentations 64-5 tests of goodness of fit and independence 427

payoff 802, 917 payoff table 802, 917 Pears on product moment correlation

coefficient population data 94, 106 sample data 94, 106

percentage frequency distribution 25, 31,917

percentile 73-4, 917 permutations 124 pie chart 269,917

PivotTable Report field list 62-3 finalizing 63-4 initial worksheet 60-2

point estimate 227,917 point estimation 227,516 point estimator 68,227,338,917

of difference between two population means 363

of difference between two population proportions 357, 364

Poisson distribution 412-15 Poisson and exponential distribution

relationship 209 Poisson probability distribution 175, 917 Poisson probability function 175-6, 182,917 pooled estimate of n359, 364, 917 population 14, 917, ch22 p2 population covariance 92, 106 population mean: (}known 252-7,277,279-80,

290-303,301-2,328 population mean: (}unknown 258-63,278,

280-2,302-3,305-8,328-9,333-4 population mean 105, 336, ch22 pp6-8,

14-15,24-5 population mean for grouped data 102, 106 population parameter 68, 917 population proportion 268-71,278-9,

311-13,330-1,333, ch22 pp9-10, 16-18, 26-7

population total ch22 pp8-9, 15-16,25-6 population variance 78, 105

between-treatments estimate 436, 453 comparing estimates (Ftest) 437-9, 454 grouped data 102, 106 inferences 374-81, 384-8 within-treatments estimate 436-7, 453-4

positioning 440 posterior probabilities 917 posterior (revised) probabilities 144, 811, 917 power 319,917 power curve 319, 917 prediction 515-19,575-6 prediction interval 516, 917 prediction interval for Y 517-19,545 price index numbers 670-1

deflating a series 671-3

quality changes 675-6 selection of a base period 675 selection of items 675

price relative 660, 679, 917 prior probabilities 143-4, 811, 917 Prix-Fischer Toys 212 probabilistic sampling ch22 4,917 probability 118,917

of an event 129-30 basic relationships 132-6

probability density function 188, 917 probability distribution 158,917 probability function 158, 917 problem formulation 800-3 Producer Price Index (PPI) 670-1, 917 producer's risk 785,917 product custornizationlmanufacturing

trade-offs 431 product design testing 478-9 production 4 production strategies 828 public lending right 221

qualitative data 7, 22-6, 917 qualitative independent variable 577, 917 qualitative variable 7,917 Quality Associates 326-7 quality control 768,917 quantitative data 7,22,28-37,917 quantitative methods 1.15-16 quantitative variable 7,917 quantity index 676-7, 917 quartiles 74-6, 917 questionnaires 11 queuing patterns 213-14

R chart 771,776-8,917 random variable 154,917

INDEX

randornized block design 459-60,476,480-1, 482-3,486,917

range 77,917 rank correlation 750-2 ratio scale 7, 728, 917-18 regression analysis 490, 713-15 regression equation 492, 918 regression model 492, 918 rejection rule for lower tail test 296

rejection rule using p-value 295 relationship among SST, SSR, SSE 503, 544,

565,606 relative frequency 24, 31 relative frequency distribution 25,918 relative frequency method 125, 918 relative location 83-4 replications 467, 918 residual analysis 524, 531-4, 536-41,

586-91,918 residual for observation i 523, 545 residual plot 525, 918 residual plot against y 527

a (sigma) known 253,918 a (sigma) unknown 258,918 sample 14,918, ch22 p2 sample correlation coefficient 504, 544 sample covariance 105, 164 sample information 811, 918 sample mean 69, 105

grouped data 100, 106 sample point 120,918 sample size

determination 265-7,269-71,321-3, ch22 ppl0-12, 18-21,28

for interval estimate of population mean 266,274

for interval estimate of population proportion 274

for one-tailed hypothesis test about population mean 326

sample space 120, 918 sample statistic 68, 226, 918 sample survey 14,918 sample variance 78-9, 105

grouped data 101, 106 for treatment j 475

sampled population 918, ch22 p2 sampling distribution 229,229-30,375, 384,

756,918 of b 509 of P 240-4 of T for identical popu1ations 737, 744 of X 231-9

sampling error 918, ch22, p5, 6 sampling from finite population 222-4

sampling from infinite population 224-5 sampling with replacement 224,918 sampling unit, 918, ch22 p2-3 sampling without replacement 224,918 scales of measurement 5-7 scatter diagram 44-5,53,59-60,64-5,

494,918 scenario writing 716, 918 seasonal component 688,703-12,918 serial correlation 531,918 share price index numbers 671 sign test 730-4, 755, 758, 761-2, 918 significance tests 513-14 significant rank correlation 751-2 simple linear regression 491,918 simple linear regression equation 543 simple linear regression model 543 simple random sampling 222-5, 918, ch22

pp6-12 Simpson's paradox 43-4,918 single factor observation studies 480,

481-2,485 single-factor experiment 451,918 skewness 82, 918 slope and y-intercept for estimated regression

equation 496, 544 small-sample case 730-2, 740-2 smoothing constant 694,918 smoothing methods 689-96 Spearman rank corre1ati(;m coefficient 750-1,

756,768,918 squared units 79 standard deviation 80, 105, 164,918

of the ith residua1528, 545 of P 241-2,246 of residua1586, 607 of X 233-4, 246

standard error 234, 338, 358, 359, 363, 364,918

of the estimate 509, 544 of the mean 793 of the proportion 778, 794

standard normal density function 196 standard normal probability distribution

195-200,918 standard residual for observation 586 standardized residual 527...,-8, 919

standardized residual for observation i 528, 545,607

standardized value (or score) 84 states of nature 802, 919 statistical analysis 15-16 statistical inference 14-15,919 statistical process control 769-81 statistical studies

experimental 9 observationallnon-experimentaI9, 11

statistics 2, 3-5, 919 stem-and-leaf display 35-7,919 stepwise regression 642 stratified random sampling 919, ch22

pp13-21 studentized deleted residuals 588, 919 subjective method 125-6, 919 'sum of squares

due to error 475,476,477,500,544 due to regression 502, 544 due to treatments 475 for factor A 476 for factor B 477 for interaction 477

systematic sampling 919, ch22 p30

t distribution 258,919 t-test 509,510, 545, 572-3, 606 target population ch22 2, 919 test of independence 405-9 test statistic 293, 340, 344, 360, 364, 919

for equality of k population means 475 for goodness of fit 421 for hypothesis test involving matched

samples 353 for hypothesis tests about population mean:

crknown 325 for hypothesis tests about population mean:

crunknown 305, 326 for hypothesis tests about population

proportion 312,313,326 for hypothesis tests about population

variances 386, 391 for independence 421

testing decision-making situations 286-7 research hypotheses 285-6

for significance 508-14,569-75 validity of a claim 286

time intervals 176-7 time series 684, 919

components 686-9 data 7 method 685

time series data 919 total sum of squares 439, 475, 501, 544 . treatments 451, 919 tree diagram 121, 919 TreePlan 829-33 trend 686, 919 trend line 44-5, 919 trend projection 698-701, 722, 724, 725 trimmed mean 72 TV audience measurement 69 two-tailed test 297-8,306-8,919 Type I error 288-90,447-9,919 Type II error 288-90,919

calculating probability 316-19

unbiased 232,919 unbiasedness 232 unemployment study 655

INDEX

uniform probability density function 189, 211 uniform probability distribution

188-90,919 union of A and B 133,919 university selection 615 unweighted aggregate 'price index in period t

663,679 US Department of Transportation 547-8

variability, measures of 76-81 variable 5, 919

adding or deleting 631-2_ measures of association 91-7 naming 69-70

variable selection procedures 642, 919 variance 78-80, 163-4,919

for binomial distribution 182 of discrete random variable 163, 182 for hypergeometric distribution

179, 182 variance inflation factor 574,607,919 Venn diagram 132, 919

INDEX

weight loss and triglyceride level reduction 546-7

weighted aggregate price index 663-4,680,919 weighted aggregate quantity index 676,680 weighted average price relatives 668, 680 weighted mean 99-100, 106,919 weighted moving averages 692, 919 weighting factor for equation (17.6) 668, 680 Wentworth Medical Centre 477-8 whiskers 88

Wilcoxon signed-rank test 736-8, 755, 758-9,762,919

x chart 770-6,771-6,919

YouGov23 Young Professional magazine 275-6

z-score 83-4, 105, 164, 919 zero 80

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