James T. McClave, P. George Benson, Terry L. Sincich,-First Course in Business Statistics-Prentice-Hall (2000)

James T. McClave, P. George Benson, Terry L. Sincich,-First Course in Business Statistics-Prentice-Hall (2000) Thống kê và phân tích dữ liệu trong kinh tế

Normal Curve Areas IL Source: Abridged from Table I of A Hald, Statistrcal Tables and Formulas (New York: Wiley), 1952 Reproduced by permission of A Hald Critical Values of t 6.314 2.920 2.353 2.132 2.015 1.943 1.895 1.860 1.833 1.812 1.796 1.782 1.771 1.761 1.753 1.746 1.740 1.734 1.729 1.725 1.721 1.717 1.714 1.711 1.708 1.706 1.703 1.701 1.699 1.697 1.684 1.671 1.658 1.645 IUC,ed with the ~dpermission of the 'Itustees of Biometril from E S Pearson and H 0.Hartley (eds), The B~ometnkaTablesfor Stat~st~crans, Vol 1,3d ed ,Biometrika, 1966 Source: A FIRST C O U R S E IN BUSINESS ., , .- Eighth Edition J A M E S T M c C L A V E Info Tech, Inc University of Florida i P GEORGE B E N S O N I Terry College of Business University of Georgia I TERRY S l N C l C H I I University of South Florida I I PRENTICE HALL Upper Saddle River, NJ 07458 "W PROBABILITY Statistics in Action: 11 3.1 Events, Sample Spaces, and Probability 118 3.2 Unions and Intersections 130 3.3 Complementary Events 134 3.4 The Additive Rule and Mutually Exclusive Events 135 3.5 Conditional Probability 140 3.6 The Multiplicative Rule and Independent Events 144 3.7 Random Sampling 154 Lottery Buster 158 Quick Review 158 *"ss""ms,"ms%"W".""""" * " bb " "* "" " m * P RIABLES AND PROBABILITY DISTRIBUTIONS 167 I 4.1 Two Types of Random Variables 168 4.2 Probability Distributions for Discrete Random Variables 171 4.3 The Binomial Distribution 181 4.4 The Poisson Distribution (Optional) 194 4.5 Probability Distributions for Continuous Random Variables 201 4.6 The Uniform Distribution (Optional) 202 4.7 The Normal Distribution 206 4.8 Descriptive Methods for Assessing Normality 219 4.9 Approximating a Binomial Distribution with a Normal Distribution (Optional) 225 4.10 The Exponential Distribution (Optional) 231 4.11 Sampling Distributions 236 4.12 The Central Limit Theorem 242 I Statistics in Action: IQ, Economic Mobility, and the Bell Curve 251 Quick Review 252 Real-World Case: The Furniture Fire Case (A Case Covering Chapters 3-4) 257 CONTENTS sms-*w " "bums INFERENCES BASED ON A SINGLE SAMPLE: ESTIMATION WITH CONFIDENCE INTERVALS I I I I Statistics in Action: Large-Sample Confidence Interval for a Population Mean 260 5.2 Small-Sample Confidence Interval for a Population Mean 268 5.3 Large-Sample Confidence Interval for a Population Proportion 279 Determining the Sample Size 286 5.4 Scallops, Sampling, and the Law 292 Quick Review 293 ma"-mum""-"% I " m-"us m"-w t-""m m ""a","- ""rn *""m%*"-" ASED ON A SINGLE TESTS OF HYPOTHESIS 299 I+ I i a 6.1 The Elements of a Test of Hypothesis 300 6.2 Large-Sample Test of Hypothesis About a Population Mean 306 6.3 Observed Significance Levels:p-Values 313 6.4 Small-Sample Test of Hypothesis About a Population Mean 319 6.5 Large-Sample Test of Hypothesis About a Population Proportion 326 6.6 A Nonparametric Test About a Population Median (Optional) 332 I I i Statistics in Action: March Madness-Handicapping the NCAA Basketball Tourney I 338 Quick Review 338 "s,,,m*x- I 259 5.1 I f vii COMPARING POPULATION MEANS -"a" 345 7.1 Comparing Two Population Means: Independent Sampling 346 7.2 Comparing Two Population Means: Paired Difference Experiments 362 7.3 Determining the Sample Size 374 viii CONTENTS Statistics in Action: 7.4 Testing the Assumption of Equal Population Variances (Optional) 377 7.5 A Nonparametric Test for Comparing Two Populations: Independent Sampling (Optional) 384 7.6 A Nonparametric Test for Comparing Two Populations: Paired Difference Experiment (Optional) 393 7.7 Comparing Three or More Population Means: Analysis of Variance (Optional) 400 On the Trail of the Cockroach 41 Quick Review 418 Real-World Case: The Kentucky Milk Case-Part Statistics in Action: I I (A Case Covering Chapters 5-7) 8.1 Comparing Two Population Proportions: Independent Sampling 428 8.2 Determining the Sample Size 435 8.3 Comparing Population Proportions: Multinomial Experiment 437 8.4 Contingency Table Analysis 445 Ethics in Computer Technology and Use 426 458 Quick Review 461 Real-World Case: - - - Discrimination in the Workplace (A Case Covering Chapter 8) 468 9.1 Probabilistic Models 472 9.2 Fitting the Model: The Least Squares Approach 476 9.3 Model Assumptions 489 9.4 An Estimator of a2 490 9.5 Assessing the Utility of the Model: Making Inferences About the Slope PI 494 9.6 The Coefficient of Correlation 505 9.7 The Coefficient of Determination 509 Statistics in Action: 9.8 Using the Model for Estimation and Prediction 516 9.9 Simple Linear Regression: A Complete Example 529 9.10 A Nonparametric Test for Correlation (Optional) 532 Can "Dowsers" Really Detect Water? 540 Quick Review 544 557 INTRODUCTION TO MULTIPLE REGRESSION 10.1 Multiple Regression Models 558 10.2 The First-Order Model: Estimating and Interpreting the p Parameters 559 10.3 Model Assumptions 565 10.4 Inferences About the P Parameters 568 10.5 Checking the Overall Utility of a Model 580 10.6 Using the Model for Estimation and Prediction 10.7 Residual Analysis: Checking the Regression Assumptions 598 10.8 Some Pitfalls: Estimability, Multicollinearity, and Extrapolation 614 i Statistics in Action: "Wringing" The Bell Curve / 593 624 Quick Review 626 Real-World Case: The Condo Sales Case (A Case Covering Chapters 9-1 0) 634 11.1 Quality, Processes, and Systems 638 11.2 Statistical Control 642 11.3 The Logic of Control Charts 651 11.4 A Control Chart for Monitoring the Mean of a Process: The T-Chart 655 11.5 A Control Chart for Monitoring the Variation of a Process: The R-Chart 672 11.6 A Control Chart for Monitoring the Proportion of Defectives Generated by a Process: The p-Chart 683 Statistics in Action: Deming's 14 Points 692 Quick Review 694 Real-World Case: The Casket Manufacturing Case (A Case Covering Chapter 11) APPENDIXB Tables 707 AP PEN D l X C Calculation Formulas for Analysis of Variance: Independent Sampling 739 ANSWERS TO SELECTED EXERCISES References 747 Index 753 741 699 ",: r This eighth edition of A First Course in Business Statistics is an introductory business text emphasizing inference, with extensive coverage of data collection and analysis as needed to evaluate the reported results of statistical studies and to make good decisions As in earlier editions, the text stresses the development of statistical thinking, the assessment of credibility and value of the inferences made from data, both by those who consume and those who produce them It assumes a mathematical background of basic algebra A more comprehensive version of the book, Statistics for Business and Economics (8/e), is available for two-term courses or those that include more extensive coverage of special topics NEW IN THE EIGHTH EDITION Major Content Changes Chapter includes two new optional sections: methods for detecting outliers (Section 2.8) and graphing bivariate relationships (Section 2.9) Chapter now covers descriptive methods for assessing whether a data set is approximately normally distributed (Section 4.8) and normal approximation to the binomial distribution (Section 4.9) Exploring Data with Statistical Computer Software and the Graphing CalculatorThroughout the text, computer printouts from five popular Windows-based statistical software packages (SAS, SPSS, MINITAB, STATISTIX and EXCEL) are displayed and used to make decisions about the data New to this edition, we have included instruction boxes and output for the TI-83 graphing calculator Statistics in Action-One feature per chapter examines current real-life, highprofile issues Data from the study is presented for analysis Questions prompt the students to form their own conclusions and to think through the statistical issues involved Real-World Business Cases-Six extensive business problem-solving cases, with real data and assignments Each case serves as a good capstone and review of the material that has preceded it Real-Data Exercises-Almost all the exercises in the text employ the use of current real data taken from a wide variety of publications (e.g., newspapers, magazines, and journals) Quick Review-Each chapter ends with a list of key terms and formulas, with reference to the page number where they first appear Language Lab-Following the Quick Review is a pronunciation guide for Greek letters and other special terms Usage notes are also provided 744 Answers t o Selected Exercises 7.109 a H,: p~ = 0, Ha: pc~,> b paired difference tention: not reject Ho for a = 05 c Aad: not reject H, for a = 05; Ab: reject H, for a = 05: In- Chapter 8.3 a z < -2.33 b z < -1.96 c z < -1.645 d z < -1.28 8.5 a .07 f 067 b .06 f 086 c -.I5 f ,131 8.7 z - 1.14, not reject H, 8.9 a p, - p2 8.11 b yes c .19 f 02 d normal approximation not adequate 8.13 a yes b -.0568 f 0270 8.15 yes,z = -2.25 8.17 a n, = n2 = 29,954 b n, = n2 = 2,165 c n , = n2 = 1,113 8.19 a n, = n2 = 911 b no 8.21 n, = 520, n, = 260 8.23 a 18.3070 b 29.7067 c 23.5418 d 79.4900 8.25 b E ( n , ) 8.27 a X2 = 3.293 8.29 a 111,74,74,37,37,37 b 13.541 c reject H, 8.31 a yes, X2 = 87.74, p-value = b .539 f 047 8.33 a H": p, = p2 = p3 = p, = 25 b X2 = 14.805, reject H, c Type I error: conclude opinions of Internet users are not evenly divided among four categories when they are;Type I1 error: conclude opinions of Internet users are evenly divided among four categories when they are not; 8.35 X2 = 16, p-value = ,003, reject H, 8.37 a X2 = 12.734, not reject H, b .05 < p-value < 10 8.39 a H,: row and column classifications are independent b X2 > 9.21034 c 14.37,36.79,44.84,10.63,26.21,33.16 d X2 = 8.71, not reject H, 8.41 yes, X2 = 256.336 8.43 a ,901 b ,690 d X2 = 48.191, reject H, e .211 f 070 8.45 b X2 = 45.357, p-value = 0, reject Ho 8.47 a X2 = 39.22, reject H, b X2 = 2.84, not reject HI, 8.49 yes,x2 = 24.524 8.51 a no,X2 = 2.133 b ,233 f 057 8.53 542 8.55 X2 = 19.10,reject Ho 8.57 union: X2 = 13.37,p-value = 038, reject H,; nonunion: X2 = 9.64, p-value = ,141, not reject H, 8.59 a no c HI,:Jan change and next 11-month change are independent d X2 = 2.373, not reject H, e yes Inspector Accept Committee Accept Committee Reject Totals 101 23 124 Inspector Reject 10 19 29 Totals b yes c X2 = 111 42 26.034, reject HI, 8.63 a yes, X2 153 = 47.98 b .I25 f 046 Chapter 9.3 P, = 113, P, = 1413, y = 1413 + 113x 9.9 no 9.11 a ( y - 3) = 0.02, SSE = 1.2204 c SSE = 108.00 9.13 b negative linear relationship c -.9939,8.543 e range of x: to 9.15 a positive c -205.777,1057.367 9.17 b = 16.593 + 9 ~ d 45.828 9.19 b jj = 569.5801 - ,00192~ e range of x: $16,900 to $70,000 9.21 b -51.362,17.754 9.23 a .3475 b 1.179 9.25 10.10: SSE = 1.22, s2 = 2441, s = 4960; 10.13: SSE = 5.713, s2 = 1.143, s = 1.069 9.27 a jj = 7.381 + 373x b $604.181 billion c SSE = 2,225.63, s2 = 27.24 9.29 a SSE = 20,554.415, s2 = 2,055.442, s = 45.337 9.31 a 95% CI: 31 f 1.13;90% CI: 31 f 92 b 95% CI: 64 f 4.28; 90% C1: 64 f 3.53 c 95% CI: -8.4 f 67;90% CI: -8.4 f 55 9.33 82 f 76 9.35 a yes b jj = -3,284.5 + 451.4~ c yes,t = 7.57 e 451.4 f 104.5 9.37 a support b jj = 15.878 + 927x c yes, t = 2.45 d and l 9.39 a yes, t = 4.98,~-value= 001 b .607 9.41 a t = 3.384,~-value= 0035, reject H(, b 17.75 16.63 9.43 yes, t = -.96 9.45 a positive b negative c slope d positive or negative 9.47 a ,9438 b 2020 9.49 a very weak positwe linear relationship b weak positive linear relationship 9.51 b -.420 d ,1764 9.53 r2 = 0.2935, r = -.5418 9.55 a yes b -.423 9.57 b jj = 1.5 + 946x c 2.221 d 4.338 f 1.170 e 4.338 f 3.223 f prediction interval for y 9.59 a 2.2 f 1.382 d t = 6.10, reject H,, 9.61 b jj = 5,566.13 - 210.346~ c t = -8.69, p = 0, reject H, d 3437 e (3,714.7,4,052.0) f (3,364.1,4,402.7) 9.63 a (12.6384,239.7) b narrower c no 9.65 a yes,t = -5.91; negative b (.656,2.829) c (1.467,2.018) 9.67 a .O1 b .O1 c .975 d .05 9.69 a .4 b -.9 c -.2 d .2 9.71 a -.485 b reject H, 9.73 r, = -.607, not reject H,, 9.75 b r, = 972, reject H, 9.77 a = 37.08 - ~ c 57.2 d 4.4 e -1.6 =k f 13.08 f 6.03 g 13.08 f 7.86 9.79 b r = -.1245, r2 = 0155 c no,t = -.35 d -.091 9.81 b jj = 12.594 + 10936x c yes,t = 3.50 d 28.99 f 12.50 9.83 j j = -92.46 + 8.35x, reject H,,p-value = 0021 9.85 a jj = 3.068 - 015x c t = -3.32, reject H,, d 2.913 f 207 9.87 a 57.14 f 34.82 b 110 is outside range of x c i = 44 9.89 ,8574 9.91 machine hours: t = 3.30, p = ,008, reject Ho,r2 = S21; labor hours: t = 1.43, p = ,183, not reject H,, r2 = ,170 + I I 745 Answers t o Selected Exercises Chapter 10 10.1 a E ( y ) = PO+ PIXI+ P ~ b E(Y) = PO + PIXI+ P2x2 + P3x3 + P ~ c E(Y) = DO + PIXI+ P2x2 + P ~ 3+ P ~ + pjx, 10.3 a t = 1.45, not reject HI) b t = 3.21, rejcct HIl 10.5 n - (k + 1) 10.7 a E(y) = PO + P,x, + P2x2 b ji = -20.352 + 13.3504~~ ~ ~d no,t = 1.74 e (49.69,437.74) 10.9 a = 20.9 + ~-~7 ~ ~.0042x3 b 14.01 c no,t = 1.09 10.11 a = 12.2 - 0265x, - 458x, c t = -.50,p = ,623, donot reject H, d -.458 f ,347 ~ 33,225.9 c yes, e x, = if plants, if not 10.15 a = 93,074 + , ~-~855x2 + 924x3 + , ~+~1 ~ b t = 2.78,~-value= 0059 f t = -2.86, reject H, g .00495 10.17 a .8911 b .8775 c F = 65.462, reject HI, d ,0001 10.19 b F = 8.321, reject H, 10.19 a 10.21 a ,8168 b HI):0, = P2 = Source df SS MS F c F = 31.22, p = 0000069 d reject ; R2 = 4947; Ri = 4352 H,, 10.23 a = -4.30 - 002x1 + Model 12.09 6.045 8.321 + ~, -~ ~, ~ ~ 336x2 + , ~ Error 17 12.35 72647 081x6 + 134x7 b F = 111.1, reject H,) d t = 1.76,~-value= ,079, 19 24.44 Total not reject H, e no, t = -.049, p-value = ,961 10.25 a 51% of the 10.27 F = 1.06 not reject HO t variability in operating margins can be explained by the model b F = 13.53, r e j e ~HO 10.29 a R~ = ,529; R: = S05; Ri b F = 21.88,~-value= 0, reject H, 10.31 a (1,759.7,4,275.4) b (2,620.3,3,414.9) c yes 10.33 (-1.233,1.038) 10.35 a F = 72.11, reject HtI 10.37 yes 10.41 a yes 10.43 a no b yes c no d yes; 26th household e no 10.45 confidence interval 10.47 a = 90.1 - 1.836x, + 285x, b .916 c yes, F = 64.91 d t = -5.01, reject H, e 10.677 10.49 no degrees of freedom for error 10.51 a E(y) = PI, + P,x, + P2x2+ P3x3 + p,x, + P5xs b reject HI,:PI = P2 = P3 = P4 = PS = c E(Y) = PO+ P+I + P Z ~+ZP ~ 3+ P ~ + PSXS + P& + P7x7 d 60.3% of the variability in GSI scores is explained by the model e both variables contribute to the prediction of GSI 10.53 Importance and Support are correlated at ,6991;no 10.55 a E(y) = PI,+ P,x, where x = (1 if H, if L} = 10.575 + 917x c yes,t = 3.38 10.57 c yes,F = 39.505,~-value= ,000 d x, = 60: = 47 + 026x,; b = 1.49 + 026x, e add x: X, = 75: = 98 026x,; x, = 90: + + + + Chapter 11 11.7 out of control 11.9 a 1.023 b 0.308 c 0.167 11.11 b ? = 20.11625, R = 3.31 c UCL = 22.529, LCL = 17.703 d Upper A-B: 21.725, Lower A-B: 18.507, Upper B-C: 20.920, Lower B-C: 19.312 e yes 11.13 a = 23.9971, R = 1815, UCL = 24.102, LCL = 23.892, Upper A-B: 24.067, Lower A-B: 23.927, Upper B-C: 24.032, Lower B-C: 23.962 b in control c yes 11.15 a = 49.129, R = 3.733, UCL = 50.932, LCL = 47.326, Upper A-B: 50.331, Lower A-B: 47.927, Upper B-C: 49.730, Lower B-C: 48.528 b no c no 11.17 a = 52.6467, R = 755, UCL = 53.419, LCL = 51.874, Upper A-B: 53.162, Lower A-B: 52.132, Upper B-C: 52.904, Lower B-C: 52.389 b out of control d no 11.21 a UCL = 16.802 b Upper A-B: 13.853,Lower A-B: 2.043, Upper B-C: 10.900, Lower B-C: 4.996 c in control 11.23 R-chart:R = 4.03, UCL = 7.754, LCL = 0.306, Upper A-B: 6.513, Lower A-B: 1.547, Upper B-C: 5.271, Lower B-C: 2.789.in control; T-chart: = 21.728, UCL = 23.417, LCL = 20.039, Upper A-B: 22.854, Lower A-B: 20.602, Upper B-C: 22.291,Lower B-C: 21.165, out of control 11.25 a yes b R = ,0796, UCL = 168, Upper A-B: l39, Lower A-B: 020, Upper B-C: 109, Lower B-C: ,050 c in control d yes e yes 11.27 a R-= 2.08, UCL = 4.397, Upper A-B: 3.625, Lower A-B: ,535 Upper B-C: 2.853, Lower B-C: 1.307;in control b yes c R = 1.7, UCL = 3.594, Upper A-B: 2.963, Lower A-B: 437, Upper B-C: 2.331, Lower B-C: 1.069;out of control 11.29 a R = 2.756, UCL = 5.826, Upper A-B: 4.803,LowerA-B: ,709,Upper B-C: 3.780, Lower B-C: 1.732 b variation c in control 11.31 104 11.33 a p = 0575, UCL = ,1145, LCL = ,0005, Upper A-B: 0955, Lower A-B: 0195, Upper B-C: 0765, Lower B-C: 0385 d no e no 11.35 a yes b UCL = 02013, LCL = ,00081 c Upper A-B: 01691, Lower A-B: 00403, Upper B-C: 01369, Lower B-C: ,00725;in control 11.37 a p = 04, UCL = ,099, LCL = -.019, Upper A-B: 079, Lower A-B: 001, Upper B-C: 060, Lower B-C: 020 b no c no 11.49 a = 6.4 b increasing variance 11.51 out of control 11.53 a R = 7.4, UCL = 24.1758, Upper A-B: 18.5918, Lower A-B: -3.791 Upper B-C: 12.9959, Lower B-C: 1.8041; out of control b = 344.15, UCL = 358.062, LCL = 330.238, Upper A-B: 353.425, Lower A-B: 334.875, Upper B-C: 348.787, Lower B-C: 339.513;out of control c no d .25 11.55 a R = 5.455, UCL = 11.532, Upper A-B: 9.508, Lower A-B: 1.402, Upper B-C: 7.481,Lower B-C: 3.429 b in control d x = 3.867, UCL = 7.015, LCL = 719, Upper A-B: 5.965, Lower A-B: 1.769, Upper B-C: 4.916, Lower B-C: 2.818 e in control f yes 11.57 a n > 141 b p = ,063, UCL = ,123, LCL = ,003, Upper A-B: ,103,Lower A-B: 023, Upper B-C: 083, Lower B-C: 043 c out of control e no x HL Ish Chapter Careers In Statistics American Statistical Association, Bio- I metric Society, Institute of Mathematical Statistics and Statistical Society of Canada, 1995 Chervany,N.L., Benson, P.G., and Iyer, R.K "The planning stage in statistical reasoning." 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Annals of Mathematical Statistics, 1952,23 Conover, W.J Practical Nonparametric Statistics, 2nd ed New York: Wiley, 1980 Hollander,M., and Wolfe, D A Nonparametric Statistical Methods New York: Wiley, 1973 Savage,I.R "Bibliography of nonparametric statistics and related topics." Journal of the American Statistical Association, 1953,48 Schroeder,R G Operations Management, 4th ed New York: McGraw-Hill, 1993 Siegel,S Nonparametric Statistics for the Behavioral Sciences New York: McGraw-Hill, 1956 Chapter Chatterjee, S., and Price, B Regression Analysis by Example, 2nd ed New York: Wiley, 1991 Draper, N., and Smith, H Applied Regression Analysis, 2nd ed New York: Wiley, 1981 Gitlow, H., Oppenheim, A., and Oppenheim, R Quality Management: Tools and Methods for Improvement, 2nd ed Burr Ridge, Ill.: Irwin, 1995 Graybill, F Theory and Application o f the Linear Model North Scituate, Mass.: Duxbury, 1976 Horngren, C.T., Foster, G., and Datar, S.M Cost Accounting, 8th ed Englewood Cliffs, N.J.: Prentice Hall, 1994 Kleinbaum, D., and Kupper, L Applied Regression Analysis and Other Mutivariable Methods, 2nd ed North Scituate, Mass.: Duxbury, 1997 Lee, C., Finnerty, J and Norton, E Foundations of Financial Management Minneapolis, Minn.: West Publishing Co., 1997 Mendenhall,W Introduction to Linear Models and the Design and Analysis of Experiments Belmont, Ca.: Wadsworth, 1968 Mendenhall, W and Sincich,T A Second Course in Statistics: Regression Analysis, 5th ed Upper Saddle River, N.J.: Prentice Hall, 1996 Mintzberg,H The Nature of Managerial Work New York: Harper and Row, 1973 Neter, J., Wasserman,W., and Kutner, M Applied Linear Statistical Models, 3rd ed Homewood, Ill.: Richard Irwin, 1992 Weisburg, S Applied Linear Regression, 2nd ed New York: Wiley, 1985 Chapter 10 Bamett,V.,and Lewis,T Outliers in Statistical Data New York: Wiley, 1978 Belsley, D.A., Kuh, E., and Welsch, R E Regression Diagnostics: Identiiving Influential Data and Sources of Collinearity New York: Wiley, 1980 Chase, R B., and Aquilano, N J Production and Operations Management, 6th ed Homewood, Ill.: Richard D Irwin, 1992 Chatterjee, S., and Price, B Regression Analysis by Example, 2d ed New York: Wiley, 1991 Draper, N., and Smith, H Applied Regression Analysis, 2nd ed New York: Wiley, 1981 Graybill, F Theory and Application of the Linear Model North Scituate, Mass.: Duxbury, 1976 Horngren, C T., Foster, G., and Datar, S M CostAccounting, 8th ed Englewood Cliffs, N J.: Prentice Hall, 1994 Lee, C., Finnerty, J., and Norton, E Foundations of Financial Management Minneapolis, Minn.: West Publishing Co., 1997 Mendenhall, W Introduction to Linear Models and the Design and Analysis of Experiments Belmont, Calif.: Wadsworth, 1968 Mendenhall, W., and Sincich,T A Second Course in Statistics: Regression Analysis, 5th ed Upper Saddle River, N.J.: Prentice-Hall, 1996 Neter, J., Kutner, M., Nachtsheim, C., and Wasserman, W Applied Linear Statistical Models, 4th ed Homewood, Ill.: Richard D Irwin, 1996 Rousseeuw, P J., and Leroy, A M Robust Regression and Outlier Detection New York: Wiley, 1987 Weisberg, S Applied Linear Regression, 2nd ed New York: Wiley, 1985 Wonnacott, R J., and Wonnacott,T H Econometrics, 2nd ed New York: Wiley, 1979 Chapter 11 Alwan, L.C., and Roberts H.V "Time-series modeling for statistical process control." Journal of Business and Economic Statistics, 1988,Vol , pp 87-95 Banks, J Principles of Quality Control New York:Wiley, 1989 Checkland, F! Systems Thinking, Systems Practice New York: Wiley, 1981 Deming, W.E O u t o f the Crisis Cambridge, Mass.: MIT Center for Advanced Engineering Study, 1986 DeVor, R.E., Chang,T., and Southerland, J.W Statistical Quality Design and Control New York: Macmillan, 1992 Duncan, A.J Quality Control and Industrial Statistics Homewood, Ill.: Irwin, 1986 The Ernst and Young Quality Improvement Consulting Group Total Qua1ity:An Executive's Guide for the 1990s Homewood, Ill.: Dow-Jones Irwin, 1990 Feigenbaum, A.B Total Quality Control, 3rd ed New York: McGraw-Hill, 1983 Garvin, D.A Managing Quality New York: Free Press1 Macmillan, 1988 Gitlow, H., Gitlow, S., Oppenheim,A., and Oppenheim, R Tools and Methods for the Improvement of Quality Homewood, Ill.: Irwin, 1995 d Grant, E.L., and Leavenworth, R.S Statistical Quality Control, 6th ed New York: McGraw-Hill, 1988 Hart, Marilyn K "Quality tools for improvement." Production and Inventory Management Journal, First Quarter 1992,Vol 33, No 1, p 59 Ishikawa, K Guide to Quality Control 2nd ed White Plains, N.Y.: Kraus International Publications, 1986 Joiner, B.L., and Goudard, M.A "Variation, management, and W Edwards Deming." Quality Process, Dec 1990, pp 29-37 Juran, J M Juran on Planning for Quality New York: Free Press/Macmillan, 1988 Juran, J M., and Gryna, F M., Jr Quality Planning Analysis, 2nd ed New York: McGraw-Hill, 1980 Kane,V E Defect Prevention New York: Marcel Dekker, 1989 Latzko, W J Quality and Productivity for Bankers and Financial Managers New York: Marcel Dekker, 1986 Moen, R.D., Nolan,T W., and Provost, L P Improving Quality Through Planned Experimentation New York: McGraw-Hill, 1991 Montgomery, D C Introduction to Statistical Quality Control, 2nd ed New York: Wiley, 1991 Nelson, L L "The Shewhart control chart -Tests for special causes." Journal of Quality Technology, Oct 1984, Vol 16, No 4, pp 237-239 Roberts, H V Data Analysis for Managers, 2nd ed Redwood City, Calif.: Scientific Press, 1991 Rosander, A C Applications of Quality Control in the Service Industries New York: Marcel Dekker, 1985 Rummler, G A,, and Brache,A 'F Improving Performance: How to Manage the White Space on the Organization Chart San Francisco: Jossey-Bass, 1991 Ryan, T.P Statistical Methods for Quality Improvement, New York: Wiley, 1989 Statistical Quality Control Handbook Indianapolis, Ind.: AT&T Technologies, Select Code 700-444 (inquiries: 800-432-6600);originally published by Western Electric Company, 1956 Wadsworth,H.M., Stephens, K S., and Godfrey,A B Modem Methods for Quality Control and Improvement New York: Wiley, 1986 Walton, M The Deming Management Method New York: Dodd, Mead, & Company, 1986 Wheeler, D J., and Chambers, D S Understanding Statistical Process Control Knoxville, Tenn.: Statistical Process Controls, Inc., 1986 Additive rule of probability, 135 Adjusted multiple coefficient of determination, 582 Alternative hypothesis, 300,304 steps for selecting, 307 Analysis of variance (ANOVA), 400-410 calculation formulas for, 739 F-test,402-405 robustness of, 410 t-test,404 Assignable causes of variation, 649 Bar graphs, 28,30 Pareto diagram, 31-32 Base level, 572 Bell Curve, The, 251,624-625 Bell-shaped distribution See Normal distribution Bias in estimates, 15 nonresponse, 16 Binomial distribution, 181-192 experiment, 181 tables, 187-190 Binomial probabilities table, 712-715 using graphing calculator for, 97-99 using normal distribution to approximate, 229-230 Binomial random variable(s), 181-182 characteristics of, 181-182 mean for, 185 standard deviation for, 185 tables, 187-190 variance for, 185 Bivariate relationships, 94-97,505 Bound B, 287 Bound on estimation error, Box plots, 84-91 elements of, 86 interpretation of, 87 using graphing calculator for, 91 Car & Driver's "Road Test Digest," 104 Causal relationship, 508 Census, Centerline, in time series chart, 643 Central Limit Theorem, 242-248 Central tendency, 332 measures of, 52-59 Chebyshev, P L., 70 Chebyshev's Rule, 70,72 for discrete random variables, 175 Children Out of School in America, 104 Chi-square ( X ) critical values of (table), 724-725 test, 438-439,448 Class (of data), 26 Class frequency, 26 Class relative frequency, 26-27 Cluster sampling, 182 Cockroach movement pattern analysis, 16-417 Coded variable (x3),558 Coefficient of correlation, 505-509 of determination, 509-511 practical interpretation of (r2),511 population rank correlation, 536 Spearman's rank correlation coefficient (r,), 533-538 Combinations rule, 705 Combinatorial mathematics, 127 Combinatorial rule, 127 Common causes of variation, 649 Complement, 130 Complementary events, 134 Compound events, 130 Conditional probability, 140-143 formula, 141 Condominium sales case, 634-635 Confidence coefficient, 262 Confidence interval(s) for a p parameter, 568 defined, 262 graphing, 274-275 large-sample for a population mean, 260-265 for a population proportion, 279 -284 for paired difference experiment, 366 in simple linear regression, 519 of slope PI, 499 small-sample, for a population mean, 268-274 using graphing calculator to find population mean, 274-275 population proportion, 284 Confidence level, 262 Conformation to specification, 654 Consumer Price Index (CPI), Contingency table analysis, 445-455 general form of, 450 Continuous random variable(s), 170,201 Control charts, 642-650 constants for (table), 737 logic of, 651-655 for monitoring mean of a process (F-chart), 655-667 for monitoring process variation (R-chart), 672-679 for monitoring proportion of defectives generated by process (p-chart), 683-689 individuals chart (x-chart), 654 pattern-analysis rules for, 660-662 Control group, 15 Control limits, 651 Correction for continuity, 226 Correlated errors, 617 Correlation, 505 coefficient of See Coefficient 752 INDEX n Countable list, 169 CPI See Consumer Price Index (CPI) Critical values of t table, 723 of X table, 724-725 Cumulative binomial probabilities, 187 Cyclical output, 644,647 Data collecting, 14-17 interval, 13 nominal, 13 ordinal, 13 qualitative, 13 quantitative, 13 ratio, 13 sample, statistical, time series, 98 types of 13 Data description, 25 -26 distorting truth with, 100-104 graphical methods for, 26 bar graphs, 28,30,31-32 box plots, 84-91 dot plots, 36,38 histograms, 36,38-41,42-44 pie charts, 28 scattergrams (scatterplots), 94-97 stem-and-leaf displays, 36, 37-38,39,41-42 time series plot, 97-100 numerical methods for, 26 measures of central tendency, 52-59 measures of relative standing, 78-81 measures of variability, 63-67 summation notation, 50-51 Defect Prevention, 31 Deflation, Degrees of freedom (df), 269,351 Deming Prize, 692 Deming, W Edwards, 99,692 Deming's 14 Points, 692-693 Dependent events See Mutually exclusive events Dependent variable, 474 Descriptive statistics defined, l , one-variable, 58-59 Designed experiments, 14,15 Destructive sampling, 272 Deterministic model, 473 Deterministic relationship, 473 Deviation, 64-67,477 See also Error(s); Standard deviation Dimensions (of classification), 445 Discrete random variable(s) binomial tables, 187-190 distribution of, 181-192 Chebyshev's Rule for, 175 defined, 169,170 Empirical Rule for, 175 expected value (mean) of, 174 probability distributions for, 171-177 standard deviation of, 175 variance of, 175 Discrimination in the workplace, 468-469 Distribution(s) for continuous random variables, 20 for discrete random variables, 171-177 exponential, 231-235 mound-shaped, 70,73,81 multinomial, 437 normal See Normal distribution Poisson, 194,195 randomness, 202 sampling See Sampling distribution(s) uniform, 202-204,240 Distribution of the process, 645 Dot plots, 36,38,39 Downtrend, 644,647 Dowsing accuracy experiment, 540-542 Dummy variable, 571 Empirical Rule, 71,72,73,235 for discrete random variables, 175 Equal population variances, 377-38 Error(s), 477 correlated, 617 random, 473 Type I, 301,304 Type 11,303,304 Escalator clauses, Estimates, biased, 15 Estimation error, bound on, Estimator interval, 262 point, 260 unbiased, 66 Ethics, in computer technology and use, 458-459 Events, 123 complementary, 134 compound, 130 Expected cell count, 440 Expected value of discrete random variables, 174 of squared distance from the mean, 175 Experiment, 118-119 Exploding the Pareto diagram, 31 Exponential distribution, 231-235 Exponential random variable, 232 Exponentials table, 722 Extrapolation, 616 Factors, 95 See also Independent variable F-distribution, 377-378 percentage points of (tables), 726-733 First-order model, multiple regression, 559-565 Freak output, 644,647 Frequency function, 201 F statistic (test), 402-405 F-test for equal population variances, 377-38 summary of, 381 Furniture fire case, 257 Gasket manufacturing case, 699-702 GDP See Gross Domestic Product (GW Generalization, 7,8 Global F-test, 583 Gosset, W S., 269 Graphing calculator binomial probabilities on, 97-99 box plots on, 91 confidence interval for population mean on, 274-275 confidence interval for population proportion on, 284 graphing area under standard normal on, 213 histograms on, 42-44 normal probability plots on, 222-223 one-variable descriptive statistics on, 58-59 one-way ANOVA on, 407-408 Poisson probabilities on, 198-199 p-value for a z-test on, 317 p-values for contingency tables on, 453-455 scatterplots (scattergrams) on, 97 straight-line (linear) regression on, 481-483 Gross Domestic Product (GDP), 10,94 Guide to Quality Control, 677 Handicapping sports events, 338-339 Herrnstein, Richard J., 251,624 Higher-order term (x,),558 Hinges, 85,86 Histograms, 36,38-41,42 using graphing calculator for, 42-44 Hypothesis alternative (research), 300,304 null, 300,304 tests of See Tests of hypotheses Increasing variance, 644,647 Independent events, 146-147 Independent random samples, 376 Independent variable, 474 Indicator variable, 571 Individuals chart, 654 Inference,6 Inferential statistics defined, 1,2-3 example of, -4 Inflation, 4,94 Inner fences, 85,86 Insider trading, 326-327 Interquartile range, 85 Intersections, 131-133 Interval data, 13 Intervals, 38 Invalid interval, 271 Ishikawa, Kaoru, 677-679 Kane,V E., 31 Kelting, Herman, 634 Kentucky milk bid rigging case, 114-115,426 Law of Large Numbers, 120 Least squares approach to simple linear regression, 476-483 on graphing calculator, 481-483 estimates, 478 formulas for, 479 line, 477,478 method, 477 prediction equation, 477,562 Level of significance, 304 Level shift output, 644,647 Line of means, 474 Linear regression See Simple linear regression Lot acceptance sampling, 227 Lottery Buster, 158-1 59 Lower control limit, 651 Lower quartile, 84-85 Mann-Whitney U statistic, 386 Marginal probabilities, 445-446 Market basket, Mathematics, combinatorial, 127 Mean square for error (MSE), 402,567 Mean square for treatments (MST), 402 Mean(s) defined, 52 of population See Population mean(s) sample See Sample mean Meandering output, 644,647 Measurement, 5,118 Measurement classes, 38 Measurement processes, 11 Measures of central tendency, 52-59 Median, 54-57 Method of least squares, 477 Middle quartile, 84-85 Modal class 57-58 Mode, 57-58 Model building, 559 Mound-shaped distributions, 70,73 z-score for, 81 Multicollinearity, 615-616 Multinomial experiment, 437-441 properties of, 437 Multiple coefficient of determination (R,), 581 Multiple regression assumptions about, 565-567 checking, 598-614 for estimation and prediction, 593-594 estimating standard error in, 567 extrapolation of independent variables, 616 general model, 558 inferences about p parameters, 568-572 models, 558-559 analyzing, 559 pitfalls in, 614-617 correlated errors, 617 multicollinearity, 615-616 parameter estimability, 614-615 prediction outside experimental region, 616 residual analysis, 598-614 test of an individual parameter coefficient in, 568 time series model, 617 usefulness of, 580-585 adjusted multiple coefficient of determination, 582 analysis of variance F-test, 583-584 global F-test, 583-584 multiple coefficient of determination (R2),581 recommendation for checking, 585 Multiplicative rule of probability, 144-149,703-704 Murray, Charles, 251,624 Mutually exclusive events, 136,148 Nilson survey, 3-4 Nominal data, 13 Nonconforming process outputs, 654 Nonparametric test (method), 332-336 for comparing two populations independent sampling, 384-389 paired difference experiments, 393-397 for correlation in simple linear regression, 532-538 Nonresponse bias, 16 Normal curve areas table, 721 Normal distribution, 206-21 approximating binomial distribution with, 225-230 descriptive methods for assessing normality, 219-223 formula for, 207 graphing area under standard normal, 213 property of, 211 standard, 207-208 using graphing calculator to graph, 213 z-score and, 215-21 Normal probability plot, 219,220, 222-223 See also Normal distribution using graphing calculator to graph, 222-223 Normal random variable, 206 finding probability corresponding to, 212 probability distribution for, 207 Null hypothesis, 300,304 steps for selecting, 307 Numerical methods of data description, 26 measures of central tendency, 52-59 measures of relative standing, 78-81 measures of variability, 63-67 summation notation, 50-51 Observation, 118 Observational data, 14 Observational studies, 15 Observed cell count, 447 Observed significance levels (p-values), 313-317 Odds, 120 One-tailed (one-sided) statistical test, 306 about a population mean, 306-307,310 about a population proportion, 328,431 for comparing large-sample population means, 348 for comparing small-sample population means, 352 for paired difference experiment, 367 sign test, 333 of Spearman's rank correlation, 538 for utility of simple linear regression model, 497 of Wilcoxon rank sum test for large samples, 389 of Wilcoxon signed rank test for a paired difference experiment, 395 One-variable descriptive statistics, on graphing calculator, 58-59 One-way analysis of variance (ANOVA), on graphing calculator, 407-408 Ordinal data, 13 Organizational processes, 640 Oscillating sequence, in time series chart, 643 Out of the Crisis,99 Outer fences, 85,86 Outlier(s), 41,84-91,603 analysis, 606 output, 644,647 rules for detecting, 90 Output distribution of the process, 645 Paired difference experiment(s) appropriate uses of, 366 for comparing two population means, 362 confidence interval for, 366 defined, 365 determining sample size for, 376 nonparametric test for comparing two populations, 393-397 test of hypothesis for, 367 Wilcoxon signed rank test, 393-397 Parameter, 236 Parameter estimability,614-615 Pareto diagram, 31-32 exploding, 31 Pareto principle, 31 Pareto, Vilfredo, 31 Partitions rule, 704 Pattern-analysis rules, 660-662 p-chart, 683-689 constructing zone boundaries for, 686 interpreting, 686 steps in constructing, 685 Pearson product moment of coefficient of correlation (r), 505-509 Perfect negative correlation, 533, 534 Perfect positive correlation, 533, 534 Pie charts, 28 Point estimator, 260 Poisson distribution, 194,195 Poisson probabilities using graphing calculator for, 198-199 table, 716-720 Poisson random variable(s), 194-199 characteristics of, 194 mean, variance, and distribution of, 195 Poisson, SimCon, 194 Pooled sample estimator, 351-352 Population correlation coefficient, 508 Population mean(s), 53-54 comparing determining sample size, 374 - 376 large samples, 346-350 confidence interval for, 348 properties of sampling distribution, 347 test of hypothesis for,348 paired difference experiments, 362-369 small samples, 351-356 confidence interval for, 352 test of hypothesis for, 352 three or more, 400-410 two, 346 confidence intervals for large-sample, 260-265 small-sample,268-274 estimating,286-288 large-sample tests of hypotheses about, 306-31 conclusions for, 310 one-tailed test, 306-307,310 two-tailed test, 306-307,310 nonparametric test for, 332-336 independent sampling, 384-389 paired difference experiments, 393-397 small-sample tests of hypotheses about, 319-323 one-tailed test, 321 t statistic for, 320-321 two-tailed test, 321 testing assumption of equal population variances, 377-381 Population proportion(s) comparing,multinomial experiment, 437-441 comparing two determining sample size for, 435-436 independent sampling, 428-432 contingency table analysis in, 445-455 estimating, 288-290 large-sample confidence interval for, 430 tests of hypothesis about, 326-330 one-tailed, 328 two-tailed, 328 z-test, 327-328 sampling distribution properties, 429 test of hypothesis about, 431 Population rank correlation coefficient, 536 Population variances, 377-381 Population(s) defined, 4-5 self-selected respondents, 16,18 subsets of, Prediction interval, 519 Prediction outside experimental region, 616 Predictor variable, 474 Probabilistic models, 472-475 assumptions about, 489-490 estimating standard error in, 490-492 for estimation and prediction, 516-523 first-order (straight-line), 474 general form of, 474 Probabilistic relationship, 473 Probability density function (pdf), 201 Probability distribution, 172,201 See also Distribution(s) Probability(ies) additive rule of, 135-136 conditional, 140-143 defined, 120 of events, 123-124 calculating, 124-127 complementary events, 134 mutually exclusive events, 136,148 multiplicative rule of, 144-149 Poisson, 198-199 See also Poisson random variable(s) random sampling and, 154-156 of sample points, 119-127 sample space and, 119-121 unconditional, 140,141-142 unions and intersections, 130-133 Process(es) black box defined, 10 illustrated, 11-12 defined, 9,640 measurement, 11 sources of variation in output, 642 Product characteristics, 639 pth percentile, 79 Published sources of data, 14 primary vs secondary, 15 p-values (observed significance level), 313-317 for p coefficients in simple linear regression, 498-499 for contingency tables, 453-455 population median, 333-334 Quadratic model, 602 Quadratic term, 602 Qualitative data, 13,14 description, 28,30,31-32 See also Data description Quality and output variation, 642 See also Control charts defined, 638-639 dimensions of, 639 Quality improvement See also Control charts Deming's contributions to, 692- 693 total quality management (TQM), 638 Quantitative data, 13 graphical methods for describing box plots, 84-91 distorting truth with, 100-104 dot plots, 36,38 histograms, 36,38-41,42-44 scattergrams (scatterplots), 94-97 stem-and-leaf displays, 36, 37-38,39,41-42 time series plot, 97-100 numerical methods for describing measures of central tendency, 52-59 measures of relative standing, 78-81 measures of variability, 63- 67 summation notation, 50-51 Quartiles, 84-85 Random behavior, 647 Random error, 473 Random number generators, 154 Random numbers table, 155, 709-711 Random phenomena, 473 Random sample, 16,154-156 Random variable(s) defined, 168 exponential, 232 Poisson, 194-197 types of, 168-170 See also Continuous random variable(s); Discrete random variable(s) uniform, 202-204 Randomized block experiment, 365 Randomness distribution, 202 Range, 63-64 Rank sums, 385 Rare events, 652 Rare-event approach, 90,248 Rare-event concept, 300 Ratio data, 13 Rational subgroups, 648 R-chart, 672-679 constructing zone boundaries for, 675 interpreting, 675 steps in constructing, 674 using with T-chart, 677 Regression analysis, 474 See also Multiple regression; Simple linear regression robustness of, 609 of statistics in The Bell Curve, 624-625 Regression line, 477 Regression modeling, 474 Regression residuals, 599 See also Residual analysis on graphing calculator, 613 outliers, 603 properties of, 600 Rejection region, 302,304 for common values of a, 307 Relative frequency, 38 histogram 38,39 Relative standing measures of, 78-81 percentile ranking, 78-79 Reliability, Representative sample, 15 Research hypothesis, 300,304 Residual analysis,598-614 steps in, 613-614 Resource constraints, Respondents, self-selected, 16 Response variable, 474 Robust method, 410 Run chart, 98,642 Sample(s) defined, 6,11 random, 16 representative, 15 mean, 52-54 median, 54-57 z-score for, 79-81 Sample points, 119-127 probability rules for, 122 Sample size, 54 for comparing two population means, 374-376 for comparing two population proportions, 435 -436 confidence intervals and, 287-288,289 for independent random samples, 376 for monitoring a process proportion, 684 for paired difference experiments, 376 for test of hypothesis about a population proportion, 329-330 Sample space, 119-121 Sample standard deviations, 673 Sample statistic, 236 Sample surveys, 428 Sampling, lot acceptance, 227 Sampling distribution(s) Central Limit Theorem and, 242-248 defined, 237-238 of least squares estimator, 496 of mean, 245 -247 properties of, 244 Sampling errors, in simple linear regression, 518-519 Scale break, 101 Scallop sampling case, 292 Scattergrams (scatterplots), 94-97, 476-477 using graphing calculator for, 97 s-chart, 673 Second-order term, 602 Securities and Exchange Commission (SEC), 326 Self-selected respondents, 16,18 Shock output, 644,647 Sign test for population median, 332-336 large-sample, 335 summary box, 334 Significance levels, observed, 313-317 See also p-values Simple event, 119 Simple linear regression assessing utility of (slope P , ) , 494-499 assumptions about, 489-490 coefficient of correlation, 505-509 coefficient of determination, 505-509 for estimation and prediction, 516-523 estimating standard error in, 490-492 example of, 529-532 least squares approach, 476-483 nonparametric test for correlation in, 532-538 probabilistic models, 472-475 procedure for, 475 Spearman's rank correlation coefficient (r,),533-538 using graphing calculator in, 481-483 Simple random sample, 154 Skewed data, 56-57 Slope of the line, 474 Spearman's rank correlation coefficient (r;), 533-538 shortcut formula for, 534 critical values for (table), 736 Special causes of variation, 649 Specification limits, 654 Spread of data See Variability Stability, 645 Standard deviation, 65-67 for discrete random variables, 175 interpretation of, 70-74 Chebyshev's Rule, 70,72 Empirical Rule, 71,72,73 Standard error, 347 of the mean, 244 Standard normal distribution, 207-208 Standard normal random variable, 208 Statistical A bstr~ctof the United States, 14 Statistical inference, Statistical process control, 645-649 defined, 649 Statistical science, Statistical thinking, 17,642 Statistics defined, 1,2 fundamental elements of, 4-9 in managerial decision-making, 17 processes of, science of, types of, 2-4 Statistics and the Law, 468 Stem-and-leaf displays,36,37-38, 39,41-42 Straight-line model, 472,474 Sum of errors,477 Sum of squares for errors (SSE), 402,477 Sum of squares for treatments (SST),401-402 Summation notation, 50-51 Survey of Current Business, 15 Surveys,l4,15 misleading and biased, 18-19 sample, 428 Systems, 641 [,criticalvalues of (table), 723 Tables binomial probabilities,712-715 control chart constants, 737 critical values of Spearman's rank correlation coefficient,736 critical values of Toin the Wilcoxon paired difference signed rank test, 735 critical values of TLand Tu for the Wilcoxon rank sum test, 734 normal curve areas, 721 percentage points of the F-distribution a = 01,732-733 a = 025,730-731 a = 05,728-729 a = lo, 726-727 Poisson probabilities, 716-720 random numbers, 709-71 Test statistic, 300-301,304 See also z-test Tests of hypotheses for comparing large-sample population means, 348 elements of, 300-304 summary box, 304 about multinomial probabilities, 439-440 one-tailed (one-sided) statistical test, 306 for paired difference experiment, 367 about a population mean large-sample test, 306-310 small-sample test, 319-323 about a population median, 326-330 one-tailed test, 334 two-tailed test, 334 about a population proportion, 431 large-sample test of, 326-330 sample size for, 329-330 small-sample test of, 330 possible conclusions for, 310 p-values, 313-317 calculating, 314 reporting as, 316 two-tailed (two-sided) statistical test, 306 Three-sigma limits, 651 Time series data, 98 Time series model, 617 Time series plot, 97-100,642-643 Treatment group, 15 Treatments, 400 sampling variability among means of (mean square for treatments), 402 sampling variability around means of (sum of squares for error), 402 sampling variability within means of (mean square for error), 402 sampling variation between means of (sum of squares for treatments), 401-402 Tree diagram, 146 Trial values, 663 Two-tailed (two-sided) statistical test, 306 for comparing large-sample population means, 348,352 for paired difference experiment, 367 about a population mean, 306-307,310 for population proportions, 328, 431 of Spearman's rank correlation, 538 for utility of simple linear regression model, 497 of Wilcoxon rank sum test for large samples,389 of Wilcoxon signed rank test for a paired difference experiment, 395 Two-way table, 445 Type I error, 301,304 Type I1 error, 303,304 Unconditional probability, 140, 141-142 Unethical statistical practice, 18 Uniform distribution, 202,203, 240 Uniform random variable(s), 202-204 mean, deviation, and distribution of, 203 Unions, 131-133 Upper control limit, 651 Upper quartile, 84-85 Uptrend, 644,647 Variability, 54 measures of, 52 numerical measures of, 63-67 Variable(s), 11 coded (x3),558 defined, dependent, 474 dummy, 571 independen 1,474 indicator, 571 predictor, 474 random See Random variables response, 474 Variance(s) for discrete random variables, 175 sample, 65-67 Variance-stabilizingtransformation, 611 Variation common causes of, 649 special causes of, 649 Venn diagrams, 120 for mutually exclusive events, 148 for union and intersection, 131 Waiting time distribution, 231 Whiskers, 85,86 Wilcoxon, Frank, 384 Wilcoxon paired difference signed rank test, critical values of To (table), 735 Wilcoxon rank sum test critical values of TLand Tu (table), 734 defined, 384 for independent samples, 384-389 summary, 386-387 for large samples, 389 Wilcoxon signed rank test, 393-397 for large samples, 397 x-chart, 654 x-chart, 655-667 constructing zone boundaries for, 660 decisions in constructing, 658 interpreting, 662 rational subgrouping strategy for, 658 steps in constructing, 659 zones in, 659 y-intercept of the line, 474 z statistic, 346 z-scores, 79-81,84,89-90 z-test, 332 p-value for, 317