Biostatistics

Recommendations

Info

604 CHAPTER 12 THE CHI-SQUARE DISTRIBUTION AND THE ANALYSIS OF FREQUENCIES equal to zero for each pair of observed and expected frequencies. Such a result would yield a value of X 2 equal to zero, and we would be unable to reject H 0 . When there is disagreement between observed frequencies and the frequencies one would expect given that H 0 is true, at least one of the O i E i terms in Equation 12.2.4 will be a nonzero number. In general, the poorer the agreement between the O i and the E i , the greater or the more frequent will be these nonzero values. As noted previously, if the agreement between the O i and the E i is sufficiently poor (resulting in a sufficiently large X 2 value,) we will be able to reject H 0 . When there is disagreement between a pair of observed and expected frequencies, the difference may be either positive or negative, depending on which of the two frequencies is the larger. Since the measure of agreement, X 2 , is a sum of component quantities whose magnitudes depend on the difference O i E i , positive and negative differences must be given equal weight. This is achieved by squaring each O i E i difference. Dividing the squared differences by the appropriate expected frequency converts the quantity to a term that is measured in original units. Adding these individual ðO i E i Þ 2 =E i terms yields X 2 ,a summary statistic that reflects the extent of the overall agreement between observed and expected frequencies. The Decision Rule The quantity P ½ðO i E i Þ 2 =E i Š will be small if the observed and expected frequencies are close together and will be large if the differences are large. The computed value of X 2 is compared with the tabulated value of x 2 with k r degrees of freedom. The decision rule, then, is: Reject H 0 if X 2 is greater than or equal to the tabulated x 2 for the chosen value of a. Small Expected Frequencies Frequently in applications of the chi-square test the expected frequency for one or more categories will be small, perhaps much less than 1. In the literature the point is frequently made that the approximation of X 2 to x 2 is not strictly valid when some of the expected frequencies are small. There is disagreement among writers, however, over what size expected frequencies are allowable before making some adjustment or abandoning x 2 in favor of some alternative test. Some writers, especially the earlier ones, suggest lower limits of 10, whereas others suggest that all expected frequencies should be no less than 5. Cochran (4,5), suggests that for goodnessof-fit tests of unimodal distributions (such as the normal), the minimum expected frequency can be as low as 1. If, in practice, one encounters one or more expected frequencies less than 1, adjacent categories may be combined to achieve the suggested minimum. Combining reduces the number of categories and, therefore, the number of degrees of freedom. Cochran’s suggestions appear to have been followed extensively by practitioners in recent years. 12.3 TESTS OF GOODNESS-OF-FIT As we have pointed out, a goodness-of-fit test is appropriate when one wishes to decide if an observed distribution of frequencies is incompatible with some preconceived or hypothesized distribution.
12.3 TESTS OF GOODNESS-OF-FIT 605 We may, for example, wish to determine whether or not a sample of observed values of some random variable is compatible with the hypothesis that it was drawn from a population of values that is normally distributed. The procedure for reaching a decision consists of placing the values into mutually exclusive categories or class intervals and noting the frequency of occurrence of values in each category. We then make use of our knowledge of normal distributions to determine the frequencies for each category that one could expect if the sample had come from a normal distribution. If the discrepancy is of such magnitude that it could have come about due to chance, we conclude that the sample may have come from a normal distribution. In a similar manner, tests of goodness-of-fit may be carried out in cases where the hypothesized distribution is the binomial, the Poisson, or any other distribution. Let us illustrate in more detail with some examples of tests of hypotheses of goodness-of-fit. EXAMPLE 12.3.1 The Normal Distribution Cranor and Christensen (A-1) conducted a study to assess short-term clinical, economic, and humanistic outcomes of pharmaceutical care services for patients with diabetes in community pharmacies. For 47 of the subjects in the study, cholesterol levels are summarized in Table 12.3.1. We wish to know whether these data provide sufficient evidence to indicate that the sample did not come from a normally distributed population. Let a ¼ .05 Solution: 1. Data. See Table 12.3.1. 2. Assumptions. We assume that the sample available for analysis is a simple random sample. TABLE 12.3.1 Cholesterol Levels as Described in Example 12.3.1 Cholesterol Level (mg/dl) Number of Subjects 100.0–124.9 1 125.0–149.9 3 150.0–174.9 8 175.0–199.9 18 200.0–224.9 6 225.0–249.9 4 250.0–274.9 4 275.0–299.9 3 Source: Data provided courtesy of Carole W. Cranor, and Dale B. Christensen, “The Asheville Project: Short-Term Outcomes of a Community Pharmacy Diabetes Care Program,” Journal of the American Pharmaceutical Association, 43 (2003), 149–159.
Page 3:
TENTH EDITION BIOSTATISTICS A Found
Page 6 and 7:
VP & EXECUTIVE PUBLISHER: ACQUISITI
Page 9 and 10:
PREFACE This 10th edition of Biosta
Page 11 and 12:
PREFACE ix SUPPLEMENTS Instructor
Page 13:
BRIEF CONTENTS 1 INTRODUCTION TO BI
Page 16 and 17:
xiv CONTENTS 6.3 The t Distribution
Page 19 and 20:
CHAPTER1 INTRODUCTION TO BIOSTATIST
Page 21 and 22:
1.2 SOME BASIC CONCEPTS 3 Sources o
Page 23 and 24:
1.3 MEASUREMENT AND MEASUREMENT SCA
Page 25 and 26:
1.4 SAMPLING AND STATISTICAL INFERE
Page 27 and 28:
Page 29 and 30:
Page 31 and 32:
1.5 THE SCIENTIFIC METHOD AND THE D
Page 33 and 34:
1.6 COMPUTERS AND BIOSTATISTICAL AN
Page 35 and 36:
REVIEW QUESTIONS AND EXERCISES 17 a
Page 37 and 38:
CHAPTER2 DESCRIPTIVE STATISTICS CHA
Page 39 and 40:
2.2 THE ORDERED ARRAY 21 TABLE 2.2.
Page 41 and 42:
2.3 GROUPED DATA: THE FREQUENCY DIS
Page 43 and 44:
Page 45 and 46:
Page 47 and 48:
Page 49 and 50:
EXERCISES 31 3 ¼ an abundance of s
Page 51 and 52:
EXERCISES 33 25.21 30.49 27.38 36.4
Page 53 and 54:
EXERCISES 35 2.3.7 In a study of ph
Page 55 and 56:
EXERCISES 37 0.6870 0.6900 0.6910 0
Page 57 and 58:
2.4 DESCRIPTIVE STATISTICS: MEASURE
Page 59 and 60:
Page 61 and 62:
Page 63 and 64:
Page 65 and 66:
Page 67 and 68:
Page 69 and 70:
Page 71 and 72:
EXERCISES 53 2.5.1 Porcellini et al
Page 73 and 74:
SUMMARY OF FORMULAS FOR CHAPTER 2 5
Page 75 and 76:
REVIEW QUESTIONS AND EXERCISES 57 R
Page 77 and 78:
REVIEW QUESTIONS AND EXERCISES 59 1
Page 79 and 80:
Page 81 and 82:
REFERENCES 63 DRINK TOUNCES TGRAMS
Page 83 and 84:
CHAPTER3 SOME BASIC PROBABILITY CON
Page 85 and 86:
3.2 TWO VIEWS OF PROBABILITY: OBJEC
Page 87 and 88:
3.4 CALCULATING THE PROBABILITY OF
Page 89 and 90:
Page 91 and 92:
Page 93 and 94:
Page 95 and 96:
EXERCISES 77 (d) If we pick a subje
Page 97 and 98:
3.5 BAYES’ THEOREM, SCREENING TES
Page 99 and 100:
3.5 BAYES’ THEOREM, SCREENING TES
Page 101 and 102:
EXERCISES 83 EXERCISES 3.5.1 A medi
Page 103 and 104:
Page 105 and 106:
REVIEW QUESTIONS AND EXERCISES 87 (
Page 107 and 108:
REVIEW QUESTIONS AND EXERCISES 89 i
Page 109 and 110:
REFERENCES 91 Applications Referenc
Page 111 and 112:
4.2 PROBABILITY DISTRIBUTIONS OF DI
Page 113 and 114:
Page 115 and 116:
Page 117 and 118:
4.3 THE BINOMIAL DISTRIBUTION 99 TA
Page 119 and 120:
4.3 THE BINOMIAL DISTRIBUTION 101 E
Page 121 and 122:
4.3 THE BINOMIAL DISTRIBUTION 103 f
Page 123 and 124:
EXAMPLE 4.3.4 According to a June 2
Page 125 and 126:
EXERCISES 107 Data: C1: 0 1 2 3 4 5
Page 127 and 128:
4.4 THE POISSON DISTRIBUTION 109 pr
Page 129 and 130:
4.4 THE POISSON DISTRIBUTION 111 or
Page 131 and 132:
4.5 CONTINUOUS PROBABILITY DISTRIBU
Page 133 and 134:
4.5 CONTINUOUS PROBABILITY DISTRIBU
Page 135 and 136:
4.6 THE NORMAL DISTRIBUTION 117 .68
Page 137 and 138:
4.6 THE NORMAL DISTRIBUTION 119 σ
Page 139 and 140:
4.6 THE NORMAL DISTRIBUTION 121 _ 2
Page 141 and 142:
4.7 NORMAL DISTRIBUTION APPLICATION
Page 143 and 144:
TableD,wefindthattheareatotheleftof
Page 145 and 146:
EXERCISES 127 Data: C1:-3-2-10123 D
Page 147 and 148:
SUMMARY OF FORMULAS FOR CHAPTER 4 S
Page 149 and 150:
REVIEW QUESTIONS AND EXERCISES 131
Page 151 and 152:
REFERENCES 133 REFERENCES Methodolo
Page 153 and 154:
5.2 SAMPLING DISTRIBUTIONS 135 voca
Page 155 and 156:
5.3 DISTRIBUTION OF THE SAMPLE MEAN
Page 157 and 158:
Page 159 and 160:
Page 161 and 162:
Page 163 and 164:
5.4 DISTRIBUTION OF THE DIFFERENCE
Page 165 and 166:
Page 167 and 168:
EXERCISES 149 FIGURE 5.4.2 Sampling
Page 169 and 170:
5.5 DISTRIBUTION OF THE SAMPLE PROP
Page 171 and 172:
EXERCISES 153 51 percent had adequa
Page 173 and 174:
Page 175 and 176:
Page 177 and 178:
Page 179 and 180:
CHAPTER6 ESTIMATION CHAPTER OVERVIE
Page 181 and 182:
6.1 INTRODUCTION 163 We will find t
Page 183 and 184:
6.2 CONFIDENCE INTERVAL FOR A POPUL
Page 185 and 186:
Page 187 and 188:
Page 189 and 190:
6.3 THE t DISTRIBUTION 171 someone
Page 191 and 192:
6.3 THE t DISTRIBUTION 173 Normal d
Page 193 and 194:
EXERCISES 175 Yes Population normal
Page 195 and 196:
6.4 CONFIDENCE INTERVAL FOR THE DIF
Page 197 and 198:
Page 199 and 200:
Page 201 and 202:
EXERCISES 183 EXERCISES R Code: > t
Page 203 and 204:
Page 205 and 206:
Page 207 and 208:
6.7 DETERMINATION OF SAMPLE SIZE FO
Page 209 and 210:
6.8 DETERMINATION OF SAMPLE SIZE FO
Page 211 and 212:
6.9 CONFIDENCE INTERVAL FOR THE VAR
Page 213 and 214:
6.9 CONFIDENCE INTERVAL FOR THE VAR
Page 215 and 216:
EXERCISES 197 x 2 1 ða=2Þ ¼ 14:4
Page 217 and 218:
6.10 CONFIDENCE INTERVAL FOR THE RA
Page 219 and 220:
6.10 CONFIDENCE INTERVAL FOR THE RA
Page 221 and 222:
Page 223 and 224:
Page 225 and 226:
Page 227 and 228:
Page 229 and 230:
REFERENCES 211 3. W. V. BEHRENS,
Page 231 and 232:
REFERENCES 213 A-30. ELENI KOUSTA,
Page 233 and 234:
7.1 INTRODUCTION 215 7.1 INTRODUCTI
Page 235 and 236:
7.1 INTRODUCTION 217 question: Can
Page 237 and 238:
7.1 INTRODUCTION 219 DEFINITION The
Page 239 and 240:
7.1 INTRODUCTION 221 Evaluate data
Page 241 and 242:
7.2 HYPOTHESIS TESTING: A SINGLE PO
Page 243 and 244:
Page 245 and 246:
Page 247 and 248:
Page 249 and 250:
Page 251 and 252:
EXERCISES 233 Dialog box: Session c
Page 253 and 254:
EXERCISES 235 7.2.10 Following a we
Page 255 and 256:
7.3 HYPOTHESIS TESTING: THE DIFFERE
Page 257 and 258:
EXAMPLE 7.3.2 The purpose of a stud
Page 259 and 260:
Page 261 and 262:
Page 263 and 264:
EXERCISES 245 The SAS System The TT
Page 265 and 266:
EXERCISES 247 Primary Hypertensive
Page 267 and 268:
7.4 PAIRED COMPARISONS 249 7.3.10 R
Page 269 and 270:
7.4 PAIRED COMPARISONS 251 where d
Page 271 and 272:
7.4 PAIRED COMPARISONS 253 a = .05
Page 273 and 274:
EXERCISES 255 7.4.1 Ellen Davis Jon
Page 275 and 276:
Page 277 and 278:
Page 279 and 280:
Page 281 and 282:
EXERCISES 263 MINITAB Output Test a
Page 283 and 284:
Page 285 and 286:
7.8 HYPOTHESIS TESTING: THE RATIO O
Page 287 and 288:
7.8 HYPOTHESIS TESTING: THE RATIO O
Page 289 and 290:
EXERCISES 271 EXERCISES In the foll
Page 291 and 292:
7.9 THE TYPE II ERROR AND THE POWER
Page 293 and 294:
7.9 THE TYPE II ERROR AND THE POWER
Page 295 and 296:
7.10 DETERMINING SAMPLE SIZE TO CON
Page 297 and 298:
EXERCISES 279 We set the right-hand
Page 299 and 300:
Page 301 and 302:
Page 303 and 304:
Page 305 and 306:
Page 307 and 308:
Page 309 and 310:
Page 311 and 312:
Page 313 and 314:
Page 315 and 316:
Page 317 and 318:
Page 319 and 320:
REFERENCES 301 A-17. JOHN S. MORLEY
Page 321 and 322:
REFERENCES 303 A-56. LUIGI BENINI,
Page 323 and 324:
8.1 INTRODUCTION 305 8.1 INTRODUCTI
Page 325 and 326:
8.1 INTRODUCTION 307 who received d
Page 327 and 328:
8.2 THE COMPLETELY RANDOMIZED DESIG
Page 329 and 330:
Page 331 and 332:
Page 333 and 334:
Page 335 and 336:
Page 337 and 338:
Page 339 and 340:
Page 341 and 342:
Page 343 and 344:
Page 345 and 346:
Dependent Variable: Selenium Tukey
Page 347 and 348:
EXERCISES 329 Elbow Angle (Degrees)
Page 349 and 350:
EXERCISES 331 Age Groups (Years) Ag
Page 351 and 352:
EXERCISES 333 Group 1 Group 2 Group
Page 353 and 354:
Objective The objective in using th
Page 355 and 356:
8.3 THE RANDOMIZED COMPLETE BLOCK D
Page 357 and 358:
Page 359 and 360:
Page 361 and 362:
EXERCISES 343 The SAS System Analys
Page 363 and 364:
EXERCISES 345 group were randomly a
Page 365 and 366:
8.4 THE REPEATED MEASURES DESIGN 34
Page 367 and 368:
Page 369 and 370:
Page 371 and 372:
Page 373 and 374:
Page 375 and 376:
EXERCISES 357 Is there sufficient e
Page 377 and 378:
8.5 THE FACTORIAL EXPERIMENT 359 In
Page 379 and 380:
8.5 THE FACTORIAL EXPERIMENT 361 In
Page 381 and 382:
8.5 THE FACTORIAL EXPERIMENT 363 Be
Page 383 and 384:
8.5 THE FACTORIAL EXPERIMENT 365 TA
Page 385 and 386:
8.5 THE FACTORIAL EXPERIMENT 367 Ro
Page 387 and 388:
EXERCISES 369 The SAS System Analys
Page 389 and 390:
EXERCISES 371 Physical Psychiatric
Page 391 and 392:
8.6 SUMMARY 373 Percent Change in C
Page 393 and 394:
Page 395 and 396:
Page 397 and 398:
Page 399 and 400:
Page 401 and 402:
Page 403 and 404:
Page 405 and 406:
Page 407 and 408:
Page 409 and 410:
Page 411 and 412:
Page 413 and 414:
Page 415 and 416:
Page 417 and 418:
Page 419 and 420:
Page 421 and 422:
Page 423 and 424:
Page 425 and 426:
Page 427 and 428:
REFERENCES 409 Applications Referen
Page 429 and 430:
REFERENCES 411 Postmenopausal Women
Page 431 and 432:
CHAPTER9 SIMPLE LINEAR REGRESSION A
Page 433 and 434:
9.2 THE REGRESSION MODEL 415 some e
Page 435 and 436:
9.3 THE SAMPLE REGRESSION EQUATION
Page 437 and 438:
Page 439 and 440:
Page 441 and 442:
EXERCISES EXERCISES 423 The Least-S
Page 443 and 444:
EXERCISES 425 CoaguChek Hospital Co
Page 445 and 446:
9.4 EVALUATING THE REGRESSION EQUAT
Page 447 and 448:
Page 449 and 450:
Page 451 and 452:
Page 453 and 454:
Page 455 and 456:
Page 457 and 458:
Page 459 and 460:
9.5 USING THE REGRESSION EQUATION I
Page 461 and 462:
9.5 USING THE REGRESSION EQUATION 4
Page 463 and 464:
9.6 THE CORRELATION MODEL 445 9.6 T
Page 465 and 466:
9.7 THE CORRELATION COEFFICIENT 447
Page 467 and 468:
Page 469 and 470:
Page 471 and 472:
Page 473 and 474:
EXERCISES 455 is employed. We first
Page 475 and 476:
EXERCISES 457 9.7.3 In the study by
Page 477 and 478:
9.8 SOME PRECAUTIONS 459 X Y X Y 5.
Page 479 and 480:
9.9 SUMMARY 461 variance, which tes
Page 481 and 482:
9.7.3 t statistic for correlation c
Page 483 and 484:
Page 485 and 486:
Page 487 and 488:
Page 489 and 490:
Page 491 and 492:
Page 493 and 494:
Page 495 and 496:
Page 497 and 498:
Page 499 and 500:
Page 501 and 502:
Page 503 and 504:
Page 505 and 506:
REFERENCES 487 A-4. ROBERT B. PARKE
Page 507 and 508:
CHAPTER10 MULTIPLE REGRESSION AND C
Page 509 and 510:
Assumptions follows. 10.2 THE MULTI
Page 511 and 512:
10.3 OBTAINING THE MULTIPLE REGRESS
Page 513 and 514:
10.3 OBTAINING THE MULTIPLE REGRESS
Page 515 and 516:
EXERCISES 497 EXERCISES Obtain the
Page 517 and 518:
EXERCISES 499 10.3.3 In a study of
Page 519 and 520:
10.4 EVALUATING THE MULTIPLE REGRES
Page 521 and 522:
Page 523 and 524:
Page 525 and 526:
10.5 USING THE MULTIPLE REGRESSION
Page 527 and 528:
EXERCISES 509 and has an education
Page 529 and 530:
10.6 THE MULTIPLE CORRELATION MODEL
Page 531 and 532:
This equation may be used for estim
Page 533 and 534:
Page 535 and 536:
Page 537 and 538:
Page 539 and 540:
EXERCISES 521 HIV DNA Blood (Y) HIV
Page 541 and 542:
SUMMARY OF FORMULAS FOR CHAPTER 10
Page 543 and 544:
10.6.7 t statistic for testing hypo
Page 545 and 546:
Page 547 and 548:
Page 549 and 550:
Page 551 and 552:
Page 553 and 554:
Page 555 and 556:
REFERENCES 537 4. Refer to the data
Page 557 and 558:
CHAPTER11 REGRESSION ANALYSIS: SOME
Page 559 and 560:
11.1 INTRODUCTION 541 TABLE 11.1.1
Page 561 and 562:
11.2 QUALITATIVE INDEPENDENT VARIAB
Page 563 and 564:
Page 565 and 566:
Page 567 and 568:
Page 569 and 570:
Page 571 and 572: 11.2 QUALITATIVE INDEPENDENT VARIAB
Page 573 and 574: 11.2 QUALITATIVE INDEPENDENT VARIAB
Page 575 and 576: EXERCISES 557 11.2.1 For subjects u
Page 577 and 578: EXERCISES 559 11.2.4 Refer to Exerc
Page 579 and 580: 11.3 VARIABLE SELECTION PROCEDURES
Page 581 and 582: 11.3 VARIABLE SELECTION PROCEDURES
Page 583 and 584: EXERCISES 565 CGAGE CGINCOME CGDUR
Page 585 and 586: EXERCISES 567 11.3.2 Machiel Naeije
Page 587 and 588: 11.4 LOGISTIC REGRESSION 569 REACTI
Page 589 and 590: 11.4 LOGISTIC REGRESSION 571 betwee
Page 591 and 592: 11.4 LOGISTIC REGRESSION 573 The LO
Page 593 and 594: 11.4 LOGISTIC REGRESSION 575 Standa
Page 595 and 596: 11.4 LOGISTIC REGRESSION 577 Parame
Page 597 and 598: 11.4 LOGISTIC REGRESSION 579 Model
Page 599 and 600: EXERCISES EXERCISES 581 Further Rea
Page 601 and 602: SUMMARY OF FORMULAS FOR CHAPTER 11
Page 603 and 604: REVIEW QUESTIONS AND EXERCISES 585
Page 615 and 616: REFERENCES 597 KNOW ¼ Knowledge. S
Page 617 and 618: REFERENCES 599 A-26. EITHNE MULLOYa
Page 619 and 620: 12.1 INTRODUCTION 12.2 THE MATHEMAT
Page 621: 12.2 THE MATHEMATICAL PROPERTIES OF
Page 625 and 626: 12.3 TESTS OF GOODNESS-OF-FIT 607 x
Page 627 and 628: 12.3 TESTS OF GOODNESS-OF-FIT 609 9
Page 629 and 630: that the first expected frequency i
Page 631 and 632: 12.3 TESTS OF GOODNESS-OF-FIT 613 T
Page 633 and 634: 12.3 TESTS OF GOODNESS-OF-FIT 615 V
Page 635 and 636: EXERCISES 617 EXERCISES 12.3.1 The
Page 637 and 638: 12.4 TESTS OF INDEPENDENCE 619 12.3
Page 639 and 640: Observed Versus Expected Frequencie
Page 641 and 642: 12.4 TESTS OF INDEPENDENCE 623 Comp
Page 643 and 644: 12.4 TESTS OF INDEPENDENCE 625 Note
Page 645 and 646: 12.4 TESTS OF INDEPENDENCE 627 9. C
Page 647 and 648: EXERCISES 629 Performed Baseline Te
Page 649 and 650: 12.5 TESTS OF HOMOGENEITY 631 TABLE
Page 651 and 652: 12.5 TESTS OF HOMOGENEITY 633 8. St
Page 653 and 654: EXERCISES 635 12.5.2 Coughlin et al
Page 655 and 656: Hypotheses alternatives. 12.6 THE F
Page 657 and 658: 12.6 THE FISHER EXACT TEST 639 TABL
Page 659 and 660: 12.7 RELATIVE RISK, ODDS RATIO, AND
Page 671 and 672: EXERCISES 653 Smoking_status * Obse
Page 673 and 674:
12.8 SUMMARY 655 collected data on
Page 675 and 676:
Page 677 and 678:
Page 679 and 680:
Page 681 and 682:
Page 683 and 684:
Page 685 and 686:
REFERENCES 667 12. R. LATSCHA, “T
Page 687 and 688:
REFERENCES 669 A-35. CRAIG R. COHEN
Page 689 and 690:
13.1 INTRODUCTION 671 2. be able to
Page 691 and 692:
13.3 THE SIGN TEST 673 13.3 THE SIG
Page 693 and 694:
13.3 THE SIGN TEST 675 TABLE 13.3.2
Page 695 and 696:
13.3 THE SIGN TEST 677 EXAMPLE 13.3
Page 697 and 698:
13.3 THE SIGN TEST 679 one sample i
Page 699 and 700:
13.4 THE WILCOXON SIGNED-RANK TEST
Page 701 and 702:
13.4 THE WILCOXON SIGNED-RANK TEST
Page 703 and 704:
Page 705 and 706:
13.5 THE MEDIAN TEST 687 TABLE 13.5
Page 707 and 708:
Page 709 and 710:
EXAMPLE 13.6.1 A researcher designe
Page 711 and 712:
13.6 THE MANN-WHITNEY TEST 693 to t
Page 713 and 714:
13.6 THE MANN-WHITNEY TEST 695 Dial
Page 715 and 716:
EXERCISES 697 deficit in Sprague-Da
Page 717 and 718:
Assumptions the following: 1. The s
Page 719 and 720:
13.7 THE KOLMOGOROV-SMIRNOV GOODNES
Page 721 and 722:
EXERCISES 703 1.0 .9 Cumulative rel
Page 723 and 724:
13.8 THE KRUSKAL-WALLIS ONE-WAY ANA
Page 725 and 726:
13.8 THE KRUSKAL-WALLIS ONE-WAY ANA
Page 727 and 728:
EXERCISES 709 Data: C1: 12.22 28.44
Page 729 and 730:
EXERCISES 711 Improvement (Control)
Page 731 and 732:
13.9 THE FRIEDMAN TWO-WAY ANALYSIS
Page 733 and 734:
13.9 THE FRIEDMAN TWO-WAY ANALYSIS
Page 735 and 736:
EXERCISES 717 Subject Area Student
Page 737 and 738:
13.10 THE SPEARMAN RANK CORRELATION
Page 739 and 740:
Page 741 and 742:
Page 743 and 744:
EXERCISES 725 Rank by Rank by Area
Page 745 and 746:
13.11 NONPARAMETRIC REGRESSION ANAL
Page 747 and 748:
13.11 NONPARAMETRIC REGRESSION ANAL
Page 749 and 750:
Page 751 and 752:
Page 753 and 754:
Page 755 and 756:
Page 757 and 758:
Page 759 and 760:
Page 761 and 762:
Page 763 and 764:
Page 765 and 766:
REFERENCES 747 cardiac operations.
Page 767 and 768:
REFERENCES 749 A-14. WENDY GANTT an
Page 769 and 770:
14.2 TIME-TO-EVENT DATA AND CENSORI
Page 771 and 772:
Page 773 and 774:
Page 775 and 776:
14.3 THE KAPLAN-MEIER PROCEDURE 757
Page 777 and 778:
14.3 THE KAPLAN-MEIER PROCEDURE 759
Page 779 and 780:
EXERCISES 761 1.0 .8 73% Low-grade
Page 781 and 782:
14.4 COMPARING SURVIVAL CURVES 763
Page 783 and 784:
14.4 COMPARING SURVIVAL CURVES 765
Page 785 and 786:
EXERCISES 767 EXERCISES 14.4.1 If a
Page 787 and 788:
14.5 COX REGRESSION: THE PROPORTION
Page 789 and 790:
14.5 COX REGRESSION: THE PROPORTION
Page 791 and 792:
Page 793 and 794:
Page 795:
REFERENCES 777 REFERENCES Methodolo
Page 798 and 799:
A-2 APPENDIX STATISTICAL TABLES
Page 800 and 801:
Page 802 and 803:
Page 804 and 805:
Page 806 and 807:
Page 808 and 809:
Page 810 and 811:
Page 812 and 813:
Page 814 and 815:
Page 816 and 817:
Page 818 and 819:
Page 820 and 821:
Page 822 and 823:
Page 824 and 825:
Page 826 and 827:
Page 828 and 829:
Page 830 and 831:
Page 832 and 833:
Page 834 and 835:
Page 836 and 837:
Page 838 and 839:
Page 840 and 841:
Page 842 and 843:
Page 844 and 845:
Page 846 and 847:
Page 848 and 849:
Page 850 and 851:
Page 852 and 853:
Page 854 and 855:
Page 856 and 857:
Page 858 and 859:
Page 860 and 861:
Page 862 and 863:
Page 864 and 865:
Page 866 and 867:
Page 868 and 869:
Page 870 and 871:
Page 872 and 873:
Page 874 and 875:
Page 876 and 877:
Page 878 and 879:
Page 880 and 881:
Page 882 and 883:
Page 884 and 885:
Page 886 and 887:
Page 888 and 889:
Page 890 and 891:
Page 892 and 893:
Page 894 and 895:
Page 896 and 897:
Page 898 and 899:
Page 900 and 901:
Page 903 and 904:
ANSWERS TO ODD-NUMBERED EXERCISES C
Page 905 and 906:
ANSWERS TO ODD-NUMBERED EXERCISES A
Page 907 and 908:
Page 909 and 910:
Page 911 and 912:
Page 913 and 914:
Page 915 and 916:
Page 917 and 918:
Page 919 and 920:
Page 921 and 922:
Page 923 and 924:
Page 925 and 926:
Page 927 and 928:
Page 929 and 930:
Page 931 and 932:
Page 933 and 934:
Page 935 and 936:
Page 937 and 938:
Page 939 and 940:
Page 941 and 942:
Page 943 and 944:
Page 945 and 946:
Page 947 and 948:
Page 949:
Page 952 and 953:
I-2 INDEX Confidence coefficient, 1
Page 954 and 955:
I-4 INDEX Median test, 686-689 MINI
Page 956 and 957:
I-6 INDEX SAS: and chi-square analy
show all

Biostatistics

Create successful ePaper yourself

Delete template?

Save as template?