Applied Statistics Using SPSS, STATISTICA, MATLAB and R

More documents

Recommendations

Info

490 Appendix F – Tools (Mahalanobis distance of the means) and for several values of the dimensionality ratio, n/d: Bayes error; Expected design set error (resubstitution method); Expected test set error (holdout method). Both classes are assumed to be represented by the same number of patterns per class, n. The user only has to specify the dimension d and the square of the Battacharrya distance (computable by several statistical software products). For any chosen value of n/d, the program also displays the standard deviations of the error estimates when the mouse is clicked over a selected point of the picture box. The expected design and test set errors are computed using the formulas presented in the work of Foley (Foley, 1972). The formula for the expected test set error is an approximation formula, which can produce slightly erroneous values, below the Bayes error, for certain n/d ratios. The program is installed in the Windows standard way.
Page 2:
Applied Statistics Using SPSS, STAT
Page 6:
E d itors Prof. Dr. Joaquim P. Marq
Page 10:
Contents Preface to the Second Edit
Page 14:
Contents ix 5.2.3 The Chi-Square Te
Page 18:
Contents xi Appendix A - Short Surv
Page 22:
Contents xiii E.26 Soil Pollution .
Page 26:
Preface to the First Edition This b
Page 30:
Symbols and Abbreviations Sample Se
Page 34:
|A| determinant of matrix A tr(A) t
Page 38:
Σ covariance matrix x arithmetic m
Page 42:
1 Introduction 1.1 Deterministic Da
Page 46:
18 h 16 14 12 10 8 6 4 2 0 1.1 Dete
Page 50:
1.2 Population, Sample and Statisti
Page 54:
Table 1.3 1.2 Population, Sample an
Page 58:
Table 1.4 1.3 Random Variables 9 Da
Page 62:
1.4 Probabilities and Distributions
Page 66:
1.5 Beyond a Reasonable Doubt... 13
Page 70:
1.5 Beyond a Reasonable Doubt... 15
Page 74:
1.6 Statistical Significance and Ot
Page 78:
1.8 Software Tools 19 book we will
Page 82:
1.8 Software Tools 21 In the follow
Page 86:
1.8 Software Tools 23 illustrates t
Page 90:
1.8 Software Tools 25 On-line help
Page 94:
1.8 Software Tools 27 Figure 1.12.
Page 98:
2 Presenting and Summarising the Da
Page 102:
2.1 Preliminaries 31 The data can t
Page 106:
» meteo=[ 181 143 36 39 37 % Pasti
Page 110:
2.1 Preliminaries 35 are interested
Page 114:
2.1 Preliminaries 37 Besides the in
Page 118:
2.2 Presenting the Data 39 Sorting
Page 122:
2.2 Presenting the Data 41 In Table
Page 126:
2.2 Presenting the Data 43 With SPS
Page 130:
2.2 Presenting the Data 45 Figure 2
Page 134:
2.2 Presenting the Data 47 Figure 2
Page 138:
2.2 Presenting the Data 49 Let X de
Page 142:
2.2 Presenting the Data 51 Commands
Page 146:
2.2 Presenting the Data 53 A: The c
Page 150:
2.2 Presenting the Data 55 The s, c
Page 154:
2.2 Presenting the Data 57 histogra
Page 158:
2.3 Summarising the Data 59 type da
Page 162:
2.3 Summarising the Data 61 delimit
Page 166:
2.3 Summarising the Data 63 The sam
Page 170:
Note that: 2.3 Summarising the Data
Page 174:
where sXY, the sample covariance of
Page 178:
2.3 Summarising the Data 69 STATIST
Page 182:
2.3 Summarising the Data 71 A: The
Page 186:
2.3.6 Measures of Association for N
Page 190:
2.3 Summarising the Data 75 with th
Page 194:
Exercises 77 A: We use the N, S and
Page 198:
Exercises 79 2.13 Determine the box
Page 202:
3 Estimating Data Parameters Making
Page 206:
3.1 Point Estimation and Interval E
Page 210:
3.2 Estimating a Mean 85 In Chapter
Page 214:
3.2 Estimating a Mean 87 There are
Page 218:
3.2 Estimating a Mean 89 A: Using M
Page 222:
3.2 Estimating a Mean 91 Figure 3.5
Page 226:
3.3 Estimating a Proportion 93 esti
Page 230:
3.4 Estimating a Variance 95 is to
Page 234:
3.5 Estimating a Variance Ratio 97
Page 238:
3.6 Bootstrap Estimation 99 i. F df
Page 242:
3.6 Bootstrap Estimation 101 about
Page 246:
3.6 Bootstrap Estimation 103 The bi
Page 250:
3.6 Bootstrap Estimation 105 In the
Page 254:
Exercises 107 In order to obtain bo
Page 258:
Exercises 109 3.14 Consider the CTG
Page 262:
112 4 Parametric Tests of Hypothese
Page 266:
Page 270:
Page 274:
Page 278:
Page 282:
Page 286:
Page 290:
Page 294:
Page 298:
Page 302:
Page 306:
Page 310:
Page 314:
Page 318:
Page 322:
Page 326:
Page 330:
Page 334:
Page 338:
Page 342:
Page 346:
Page 350:
Page 354:
Page 358:
Page 362:
Page 366:
Page 370:
Page 374:
Page 378:
5 Non-Parametric Tests of Hypothese
Page 382:
5.1 Inference on One Population 173
Page 386:
Page 390:
s = npq = 224× 0. 75× 0. 25 = 6.4
Page 394:
5.1.3 The Chi-Square Goodness of Fi
Page 398:
Page 402:
Page 406:
1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 F
Page 410:
5.1.5 The Lilliefors Test for Norma
Page 414:
5.2 Contingency Tables 189 fewer mi
Page 418:
2 1 5.2 Contingency Tables 191 degr
Page 422:
5.2 Contingency Tables 193 An alter
Page 426:
5.2 Contingency Tables 195 male and
Page 430:
5.2 Contingency Tables 197 first ca
Page 434:
5.2 Contingency Tables 199 very low
Page 438:
5.3.1 Tests for Two Independent Sam
Page 442:
5.3 Inference on Two Populations 20
Page 446:
Page 450:
Page 454:
Page 458:
Example 5.19 5.3 Inference on Two P
Page 462:
3 ( N − N ) 5.4 Inference on More
Page 466:
5.4 Inference on More Than Two Popu
Page 470:
5.4 Inference on More Than Two Popu
Page 474:
Exercises 219 5.7 Several previous
Page 478:
Exercises 221 5.23 Run the non-para
Page 482:
6 Statistical Classification Statis
Page 486:
x 2 o o o o o o o o oo o o o o o o
Page 490:
6.2 Linear Discriminants 227 Figure
Page 494:
6.2 Linear Discriminants 229 Figure
Page 498:
6.2 Linear Discriminants 231 Let us
Page 502:
Table 6.5. Summary of minimum dista
Page 506:
6.3 Bayesian Classification 235 Not
Page 510:
6.3 Bayesian Classification 237 Fig
Page 514:
6.3 Bayesian Classification 239 Let
Page 518:
6.3 Bayesian Classification 241 Not
Page 522:
0.5 0.45 0.4 0.35 0.3 0.25 0.2 0.15
Page 526:
6.3 Bayesian Classification 245 For
Page 530:
Reality A N Decision A N a b c d 6.
Page 534:
6.4 The ROC Curve 249 Figure 6.17.
Page 538:
6.4 The ROC Curve 251 In order to o
Page 542:
6.5 Feature Selection 253 A: The RO
Page 546:
6.5 Feature Selection 255 comfortab
Page 550:
6.6 Classifier Evaluation 257 Resub
Page 554:
259 A: Table 6.12 shows the leave-o
Page 558:
6.7 Tree Classifiers 261 Figure 6.2
Page 562:
6.7 Tree Classifiers 263 or not a g
Page 566:
6.7 Tree Classifiers 265 The classi
Page 570:
6.7 Tree Classifiers 267 The classi
Page 574:
Exercises 269 6.2 Repeat the previo
Page 578:
7 Data Regression An important obje
Page 582:
7.1.2 Estimating the Regression Fun
Page 586:
7.1 Simple Linear Regression 275 Fi
Page 590:
2 2 2 7.1 Simple Linear Regression
Page 594:
7.1 Simple Linear Regression 279 Co
Page 598:
Example 7.3 7.1 Simple Linear Regre
Page 602:
7.1 Simple Linear Regression 283 Th
Page 606:
7.1 Simple Linear Regression 285 Fi
Page 610:
7.1 Simple Linear Regression 287 Le
Page 614:
7.2 Multiple Regression 7.2.1 Gener
Page 618:
7.2 Multiple Regression 291 For the
Page 622:
YYˆ = ∑ ∑ ( y ( y − y)( yˆ
Page 626:
7.2 Multiple Regression 295 Figure
Page 630:
7.2 Multiple Regression 297 Table 7
Page 634:
7.2.5.1 Tests for Regression Coeffi
Page 638:
The Yi can also be linearly modelle
Page 642:
3000 2000 1000 0 -1000 -2000 Raw Re
Page 646:
7.3 Building and Evaluating the Reg
Page 650:
There are other ways to detect outl
Page 654:
a 4 3 2 1 0 -1 -2 -3 Expected Norma
Page 658:
Page 662:
Page 666:
Example 7.19 7.4 Regression Through
Page 670:
6 y 4 2 0 -2 -4 -6 -8 -10 x 7.5 Rid
Page 674:
7.5 Ridge Regression 319 The b vect
Page 678:
Example 7.20 7.5 Ridge Regression 3
Page 682:
7.5 Logit and Probit Models 323 Let
Page 686:
7.5 Logit and Probit Models 325 sec
Page 690:
Exercises 327 Commands 7.7. SPSS an
Page 694:
8 Data Structure Analysis In the pr
Page 698:
8.1 Principal Components 331 where
Page 702:
8.1 Principal Components 333 » % E
Page 706:
8.1 Principal Components 335 A: The
Page 710:
8.2 Dimensional Reduction 8.2 Dimen
Page 714:
12000 10000 8000 6000 4000 2000 eig
Page 718:
0.4 0.3 0.2 0.1 0.0 -0.1 -0.2 U2 8.
Page 722:
8.3 Principal Components of Correla
Page 726:
8.3 Principal Components of Correla
Page 730:
8.4 Factor Analysis 8.4 Factor Anal
Page 734:
8.4 Factor Analysis 349 The main be
Page 738:
Exercises 351 c) The scatter plot o
Page 742:
9 Survival Analysis In medical stud
Page 746:
9.2 Non-Parametric Analysis of Surv
Page 750:
Page 754:
Page 758:
Page 762:
Page 766:
1. The Log-Rank Test. 9.3 Comparing
Page 770:
9.4 Models for Survival Data 367 Th
Page 774:
9.4 Models for Survival Data 369 ST
Page 778:
9.4 Models for Survival Data 371 Ex
Page 782:
1.0 0.9 0.8 0.7 0.6 0.5 0.4 Cumulat
Page 786:
10 Directional Data The analysis an
Page 790:
10.1 Representing Directional Data
Page 794:
10.1 Representing Directional Data
Page 798:
10.2 Descriptive Statistics 381 com
Page 802:
10.3 The von Mises Distributions 38
Page 806:
10.3 The von Mises Distributions 38
Page 810:
10.4 Assessing the Distribution of
Page 814:
Example 10.10 10.4 Assessing the Di
Page 818:
Page 822:
Page 826:
10.5 Tests on von Mises Distributio
Page 830:
10.6 Non-Parametric Tests 10.6 Non-
Page 834:
10.6 Non-Parametric Tests 399 Examp
Page 838:
Exercises 401 10.8 Compare the two
Page 842:
404 Appendix A - Short Survey on Pr
Page 846:
Page 850:
Page 854:
Page 858:
Page 862:
Page 866:
Page 870:
Page 874:
Page 878:
Page 882:
Page 886:
Page 890:
Page 894:
Appendix B - Distributions B.1 Disc
Page 898:
51 50 52 − ( k −1) 1 1 p ( k) =
Page 902:
0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 h 100
Page 906:
B.1 Discrete Distributions 437 Prob
Page 910:
2. b n, p 0.4 0.35 0.3 0.25 0.2 0.1
Page 914:
B.2.2 Normal Distribution B.2 Conti
Page 918:
Distribution function: ∫ − = =
Page 922:
Properties: 1. w1,1/λ(x) ≡ ελ(
Page 926:
Properties: 1. β1,1(x) ≡ u(x). 2
Page 930:
B.2 Continuous Distributions 449 Pr
Page 934:
B.2.9 F Distribution B.2 Continuous
Page 938:
B.2 Continuous Distributions 453 ci
Page 942:
Appendix C - Point Estimation In Ap
Page 946:
dL( n−1 n−2 p) = q dp − ( n
Page 950:
Appendix D - Tables D.1 Binomial Di
Page 954:
Appendix D - Tables 461 p n k 0.05
Page 958: Appendix D - Tables 463 p n k 0.05
Page 962: D.2 Normal Distribution The followi
Page 966: D.4 Chi-Square Distribution Appendi
Page 970: Appendix E - Datasets Datasets incl
Page 974: Appendix E - Datasets 471 Age: Geol
Page 978: Appendix E - Datasets 473 Source: J
Page 982: E.11 Firms Appendix E - Datasets 47
Page 986: Appendix E - Datasets 477 Q7: I par
Page 990: Appendix E - Datasets 479 x, y, z:
Page 994: E.24 Rocks Appendix E - Datasets 48
Page 998: Appendix E - Datasets 483 Source: W
Page 1002: Appendix E - Datasets 485 Data 2: W
Page 1006: 488 Appendix F - Tools t=scattermx(
Page 1012: References Chapters 1 and 2 Anderso
Page 1016: References 493 Gardner MJ, Altman D
Page 1020: References 495 Raudys S, Pikelis V
Page 1024: References 497 Mardia KV, Jupp PE (
Page 1028: 500 Index 5.9 (two paired samples t
Page 1032: 502 Index H hazard function, 353 ha
Page 1036: 504 Index S sample, 5 mean, 416 siz
show all

Applied Statistics Using SPSS, STATISTICA, MATLAB and R

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?