Statistical analysis and data display an intermediate course with examples in S-plus, R, and SAS
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| 100 | 1 | |a Heiberger, Richard M. |d 1945- |e Verfasser |0 (DE-588)141419997 |4 aut | |
| 245 | 1 | 0 | |a Statistical analysis and data display |b an intermediate course with examples in S-plus, R, and SAS |c Richard M. Heiberger ; Burt Holland |
| 264 | 1 | |a New York, NY |b Springer |c 2004 | |
| 300 | |a XXIV, 729 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 490 | 0 | |a Springer texts in statistics | |
| 500 | |a Includes bibliographical references and index | ||
| 630 | 0 | 4 | |a S-Plus |
| 630 | 0 | 4 | |a SAS (Computer file) |
| 650 | 7 | |a Grafische methoden |2 gtt | |
| 650 | 4 | |a R (Langage de programmation) | |
| 650 | 7 | |a R (computerprogramma) |2 gtt | |
| 650 | 7 | |a S-Plus |2 gtt | |
| 650 | 7 | |a SAS (software) |2 gtt | |
| 650 | 7 | |a Statistiek |2 gtt | |
| 650 | 4 | |a Statistique - Informatique | |
| 650 | 4 | |a Statistique mathématique - Informatique | |
| 650 | 4 | |a Datenverarbeitung | |
| 650 | 4 | |a Statistik | |
| 650 | 4 | |a Mathematical statistics |x Data processing | |
| 650 | 4 | |a R (Computer program language) | |
| 650 | 4 | |a Statistics |x Data processing | |
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| 700 | 1 | |a Holland, Burt |e Sonstige |4 oth | |
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Datensatz im Suchindex
| DE-BY-TUM_call_number | 0102 MAT 620 2001 A 727 1002 MAT 620 2005 A 5842 |
|---|---|
| DE-BY-TUM_katkey | 1484941 |
| DE-BY-TUM_location | 01 10 |
| DE-BY-TUM_media_number | 040020072608 040050451315 |
| _version_ | 1820804533009252352 |
| adam_text | Titel: Statistical analysis and data display
Autor: Heiberger, Richard M
Jahr: 2004
Contents
Preface vii
1 Audience ....................................... vii
2 Structure....................................... viii
3 Data and Programs................................. ix
4 Software........................................ x
4.1 Microsoft Windows............................. xi
4.2 Unix..................................... xi
5 Exercises ....................................... xi
1 Introduction and Motivation 1
1.1 Statistics in Context................................. 3
1.2 Examples of Uses of Statistics........................... 4
1.2.1 Investigation of Salary Discrimination.................. 4
1.2.2 Measuring Body Fat............................ 5
1.2.3 Minimizing Film Thickness........................ 5
1.2.4 Surveys ................................... 6
1.2.5 Bringing Pharmaceutical Products to Market ............. 6
1.3 The Rest of the Book................................ 7
1.3.1 Fundamentals................................ 7
1.3.2 Linear Models................................ 7
1.3.3 Other Techniques.............................. 9
1.3.4 New Graphical Display Techniques.................... 9
2 Data and Statistics 11
2.1 Types of Data .................................... 11
2.2 Data Display and Calculation ........................... 12
2.2.1 Presentation................................. 13
Contents
2.2.2 Rounding.................................. 13
2.3 Importing Data.................................... 14
2.3.1 S-Plus.................................... 14
2.3.2 SAS..................................... 15
2.3.3 Data Rearrangement............................ 15
2.4 Analysis with Missing Data............................. 16
2.4.1 Missing Data in S-Plus.......................... 16
2.4.2 Missing Data in SAS............................ 17
2.5 Tables and Graphs.................................. 17
2.6 Files for Statistical Analysis and Data Display (HH) .............. 18
2.6.1 Datasets................................... 18
2.6.2 Code, Transcripts, and Figures...................... 18
2.6.3 Functions and Macros........................... 19
2.6.4 Software................................... 19
Statistics Concepts 21
3.1 A Brief Introduction to Probability........................ 21
3.2 Random Variables and Probability Distributions ................ 22
3.2.1 Discrete Versus Continuous Probability Distributions......... 23
3.2.2 Displaying Probability Distributions................... 24
3.3 Concepts That Are Used When Discussing Distributions............ 27
3.3.1 Expectation and Variance of Random Variables............ 27
3.3.2 Median of Random Variables....................... 28
3.3.3 Symmetric and Skewed Distributions.................. 28
3.3.4 Displays of Univariate Data........................ 30
3.3.5 Multivariate Distributions?Covaxiance and Correlation....... 34
3.4 Three Probability Distributions.......................... 37
3.4.1 The Binomial Distribution ........................ 37
3.4.2 The Normal Distribution ......................... 38
3.4.3 The (Student s) t Distribution...................... 39
3.5 Sampling Distributions............................... 40
3.6 Estimation...................................... 41
3.6.1 Statistical Models.............................. 41
3.6.2 Point and Interval Estimators....................... 42
3.6.3 Criteria for Point Estimators....................... 42
3.6.4 Confidence Interval Estimation...................... 43
3.6.5 Example?Confidence Interval on the Mean p. of a Population
Having Known Standard Deviation ................... 44
3.6.6 Example?One-Sided Confidence Intervals............... 44
3.7 Hypothesis Testing.................................. 45
3.8 Examples of Statistical Tests............................ 47
3.9 Power and Operating Characteristic (O.C.) Curves............... 49
3.10 Sampling ....................................... 52
3.10.1 Simple Random Sampling......................... 53
Contents xv
3.10.2 Stratified Random Sampling....................... 53
3.10.3 Cluster Random Sampling......................... 54
3.10.4 Systematic Random Sampling ...................... 55
3.10.5 Standard Errors of Sample Means.................... 56
3.10.6 Sources of Bias in Samples........................ 56
3.11 Exercises ....................................... 57
Graphs 63
4.1 Definition....................................... 64
4.2 Example?Ecological Correlation ......................... 64
4.3 Scatterplots...................................... 65
4.4 Scatterplot Matrix.................................. 67
4.5 Example?Life Expectancy............................. 71
4.6 Scatterplot Matrices?Continued.......................... 74
4.7 Data Transformations................................ 78
4.8 Life Expectancy Example?Continued ...................... 82
4.9 SAS Graphics..................................... 85
4.10 Exercises ....................................... 87
Introductory Inference 91
5.1 Normal (z) Intervals and Tests........................... 91
5.1.1 Test of a Hypothesis Concerning the Mean of a Population Having
Known Standard Deviation........................ 92
5.1.2 Confidence Intervals for Unknown Population Proportion p..... 93
5.1.3 Tests on an Unknown Population Proportion p............. 94
5.1.4 Example?One-Sided Hypothesis Test Concerning a Population
Proportion.................................. 94
5.2 ^-intervals and Tests for the Mean of a Population Having Unknown
Standard Deviation................................. 95
5.3 Confidence Interval on the Variance or Standard Deviation of a Normal
Population...................................... 96
5.4 Comparisons of Two Populations Based on Independent Samples....... 97
5.4.1 Confidence Intervals on the Difference Between Two Population
Proportions................................. 98
5.4.2 Confidence Interval on the Difference of Between Two Means .... 98
5.4.3 Tests Comparing Two Population Means WTien the Samples Are
Independent................................. 99
5.4.4 Comparing the Variances of Two Normal Populations......... 100
5.5 Paired Data...................................... 101
5.6 Sample Size Determination............................. 105
5.6.1 Sample Size for Estimation........................ 105
5.6.2 Sample Size for Hypothesis Testing................... 106
5.7 Goodness of Fit ................................... 106
5.7.1 Chi-square Goodness-of-Fit Test..................... 107
xvi Contents
5.7.2 Example?Test of Goodness-of-Fit to a Discrete Uniform
Distribution................................. 108
5.7.3 Example?Test of Goodness-of-Fit to a Binomial Distribution .... 108
5.8 Normal Probability Plots and Quantile Plots................... 110
5.8.1 Normal Probability Plots......................... 112
5.8.2 Example?Comparing ^-Distributions.................. 113
5.9 Kolmogorov-Smirnov Goodness-of-Fit Tests................... 114
5.10 Maximum Likelihood................................ 117
5.10.1 Maximum Likelihood Estimation..................... 118
5.10.2 Likelihood Ratio Tests........................... 119
5.11 Exercises ....................................... 119
One-Way Analysis of Variance 123
6.1 Example?Catalyst Data.............................. 123
6.2 Fixed Effects..................................... 127
6.3 Multiple Comparisons?Tukey Procedure for Comparing All Pairs of Means . 130
6.4 Random Effects ................................... 135
6.5 Expected Mean Squares (EMS) .......................... 135
6.6 Example?Catalyst Data?Continued....................... 136
6.7 Example?Batch Data................................ 137
6.8 Example?Turkey Data............................... 137
6.8.1 Analysis................................... 139
6.8.2 Interpretation................................ 143
6.8.3 Specification of Analysis.......................... 143
6.9 Contrasts....................................... 144
6.9.1 Mathematics of Contrasts......................... 144
6.9.2 Scaling.................................... 146
6.10 Tests of Homogeneity of Variance......................... 147
6.11 Exercises ....................................... 148
6.A Appendix: Computation for the Analysis of Variance.............. 153
6.A.1 Computing Notes.............................. 153
6.A.2 Computation................................ 153
Multiple Comparisons 155
7.1 Multiple Comparison Procedures.......................... 156
7.1.1 Bonferroni Method............................. 156
7.1.2 Tukey Procedure for All Pairwise Comparisons............. 157
7.1.3 The Dunnett Procedure for Comparing One Mean with All Others . 157
7.1.4 Simultaneously Comparing All Possible Contrasts?Scheffe and
Extended Tukey .............................. 162
7.2 The Mean-Mean Multiple Comparisons Display (MMC Plot)......... 168
7.2.1 Difficulties with Standard Displays.................... 168
7.2.2 Hsu and Peruggia s Mean-Mean Scatterplot.............. 173
7.2.3 Extensions of the Mean-Mean Display to Arbitrary Contrasts .... 178
Contents xvii
7.2.4 Display of an Orthogonal Basis Set of Contrasts............ 180
7.2.5 Hsu and Peruggia s Pulmonary Example................ 182
7.3 Exercises ....................................... 184
Linear Regression by Least Squares 187
8.1 Introduction ..................................... 187
8.2 Example?Body Fat Data.............................. 188
8.3 Simple Linear Regression.............................. 190
8.3.1 Algebra................................... 190
8.3.2 Normal Distribution Theory ....................... 192
8.3.3 Calculations................................. 193
8.3.4 Residual Mean Square in Regression Printout............. 199
8.3.5 New Observations ............................. 199
8.4 Diagnostics...................................... 205
8.5 Graphics........................................ 209
8.6 Exercises ....................................... 210
8.A Appendix: Computation for Regression Analysis................. 213
8.A.1 S-Plus Functions ............................. 213
8.A.2 SAS Macros and Procs.......................... 213
Multiple Regression?More Than One Predictor 215
9.1 Regression with Two Predictors?Least-Squares Geometry........... 215
9.2 Multiple Regression?Algebra........................... 217
9.2.1 The Hat Matrix and Leverage ...................... 220
9.3 Multiple Regression?Two-X Analysis...................... 221
9.4 Geometry of Multiple Regression ......................... 223
9.5 Programming..................................... 223
9.5.1 Model Specification............................. 223
9.5.2 Printout Idiosyncrasies........................... 224
9.6 Example?Albuquerque Home Price Data.................... 225
9.7 Partial F-Tests.................................... 228
9.8 Polynomial Models.................................. 230
9.9 Models Without a Constant Term......................... 233
9.10 Prediction....................................... 235
9.11 Example?Longley Data .............................. 236
9.12 Collmearity...................................... 241
9.13 Variable Selection.................................. 243
9.13.1 Manual Use of the Stepwise Philosophy................. 244
9.13.2 Automated Stepwise Regression..................... 247
9.13.3 Automated Stepwise Modeling of the Longley Data.......... 250
9.14 Residual Plots.................................... 254
9.14.1 Partial Residuals.............................. 254
9.14.2 Partial Residual Plots........................... 256
9.14.3 Partial Correlation............................. 256
xviii Contents
9.14.4 Added Variable Plots ........................... 256
9.14.5 Interpretation of Residual Plots ..................... 257
9.15 Example U.S. Air Pollution Data ........................ 259
9.16 Exercises ....................................... 264
10 Multiple Regression?Dummy Variables and Contrasts 267
10.1 Dummy (Indicator) Variables............................ 267
10.2 Example Height and Weight ........................... 268
10.3 Equivalence of Linear Independent JV-Variables for Regression........ 275
10.4 Polynomial Contrasts and Orthogonal Polynomials............... 277
10.4.1 Specification and Interpretation of Interaction Terms......... 282
10.5 Analysis Using a Concomitant Variable (Analysis of Covarianro)....... 283
10.6 Example Hot Dog Data.............................. 284
10.6.1 One-Way ANOVA............................. 284
10.6.2 Concomitant Explanatory Variable.................... 286
10.6.3 Tests of Equality of Regression Lines .................. 292
10.7 ancova Function................................... 294
10.8 Exercises ....................................... 294
11 Multiple Regression?Regression Diagnostics 297
11.1 Example Rent Data................................ 297
11.1.1 Rent Levels................................. 298
11.1.2 Alfalfa Rent Relative to Other Rent................... 303
11.2 Checks on Model Assumptions........................... 309
11.2.1 Scatterplot Matrix............................. 309
11.2.2 Residual Plots................................ 309
11.3 Case Statistics.................................... 312
11.3.1 Leverage................................... 315
11.3.2 Deleted Standard Deviation........................ 316
11.3.3 Standardized and Studentized Deleted Residuals............ 317
11.3.4 Cook s Distance............................... 319
11.3.5 DFFITS................................... 321
11.3.6 DFBETAS.................................. 322
11.3.7 Calculation of Regression Diagnostics.................. 323
11.4 Exercises ....................................... 324
12 Two-Way Analysis of Variance 329
12.1 Example?Display Panel Data........................... 329
12.2 Statistical Model................................... 336
12.3 Main Effects and Interactions ........................... 336
12.4 Two-Way Interaction Plot ............................. 338
12.5 Sums of Squares in the Two-Way ANOVA Table................ 339
12.6 Treatment and Blocking Factors.......................... 339
12.7 Fixed and Random Effects............................. 341
_____________________________________________________________________________Contents xix
12.8 Randomized Complete Block Designs....................... 342
12.9 Example The Blood Plasma Data........................ 344
12.10 Random Effects Models and Mixed Models.................... 346
12.11 Introduction to Nesting............................... 347
12.11.1 Example Workstation Data....................... 347
12.12 Example Display Panel Data?Continued.................... 349
12.13 Example The Rhizobium Data.......................... 353
12.13.1 First Rhizobium Experiment: Alfalfa Plants............... 354
12.13.2 Second Rhizobium Experiment: Clover Plants ............. 354
12.13.3 Initial Plots................................. 355
12.13.4 Alfalfa Analysis............................... 357
12.13.5 Clover Analysis............................... 362
12.14 Models Without Interaction ............................ 371
12.15 Example Animal Feed Data............................ 372
12.16 Exercises ....................................... 374
12.A Appendix: Computation for the Analysis of Variance.............. 379
13 Design of Experiments?Factorial Designs 381
13.1 A Three-Way ANOVA-Muscle Data....................... 381
13.2 Latin Square Designs ................................ 389
13.2.1 Example^ -Latin Square.......................... 390
13.3 Simple Effects for Interaction Analyses...................... 396
13.3.1 Example The f ilmcoat Data...................... 397
13.4 Nested Factorial Experiment............................ 401
13.4.1 Example?Gunload Data......................... 401
13.4.2 Example?Turkey Data (continued)................... 410
13.5 Specification of Model Formulas.......................... 413
13.6 Squential and Conditional Tests.......................... 417
13.6.1 SAS Types of Sums of Squares...................... 418
13.6.2 Example?Application to Body Fat Data................ 419
13.7 Exercises ....................................... 421
13.A Appendix: Orientation for Boxplots........................ 427
14 Design of Experiments?Complex Designs 429
14.1 Confounding..................................... 429
14.2 Split Plot Designs.................................. 431
14.3 Example- Yates Oat Data............................. 432
14.3.1 Alternate Specification........................... 439
14.3.2 Polynomial Effects for Nitrogen...................... 440
14.4 Introduction to Fractional Factorial Designs................... 442
14.4.1 Example-28-2 Design........................... 442
14.4.2 Example -25-1 Design........................... 444
14.5 Introduction to Crossover Designs......................... 448
14.6 Example?Apple Tree Data............................. 452
xx Contents_______________________________________________________________________________
14.6.1 Models in Table 14.17........................... 453
14.6.2 Multiple Comparisons........................... 458
14.6.3 Models in Figure 14.5........................... 460
14.7 Example testscore.dat............................. 466
14.8 The Tukey One Degree of Freedom for Nonadditivity.............. 472
14.8.1 Example Crash Data........................... 472
14.8.2 Theory.................................... 481
14.9 Exercises ....................................... 483
15 Bivariate Statistics?Discrete Data 487
15.1 Two Dimensional Contingency Tables Chi-Square Analysis.......... 487
15.1.1 Example Drunkenness Data....................... 487
15.1.2 Chi-Square Analysis............................ 490
15.2 Two-Dimensional Contingency Tables Fisher s Exact Test.......... 492
15.2.1 Example Do Juvenile Delinquents Eschew Wearing Eyeglasses? . . 493
15.3 Simpson s Paradox.................................. 495
15.4 Relative Risk and Odds Ratios........................... 498
15.4.1 Glasses (Again)............................... 499
15.4.2 Large Sample Approximations...................... 500
15.4.3 Example Treating Cardiac Arrest with Therapeutic Hypothermia . 500
15.5 Retrospective and Prospective Studies ...................... 503
15.6 Mantel Haenszel Test................................ 504
15.7 Example Salk Polio Vaccine............................ 506
15.8 Exercises ....................................... 508
16 Nonparametries 511
16.1 Introduction ..................................... 511
16.2 Sign Test for the Location of a Single Population................ 512
16.3 Comparing the Locations of Paired Populations................. 514
16.3.1 Sign Test................................... 514
16.3.2 Wilcoxon Signed-Ranks Test....................... 516
16.4 Mann Whitney Test for Two Independent Samples............... 520
16.5 Kruskal Wallis Test for Comparing the Locations of at Least Three
Populations...................................... 523
16.6 Exercises ....................................... 526
17 Logistic Regression 527
17.1 Example?The Space Shuttle Challenger Disaster................ 529
17.1.1 Graphical Display ............................. 530
17.1.2 Numerical Display............................. 533
17.2 Estimation ...................................... 537
17.3 Example? Budworm Data............................. 540
17.4 Example -Lymph Nodes.............................. 542
17.4.1 Data..................................... 542
Contents xxi
17.4.2 Data Analysis................................ 543
17.4.3 Additional Techniques........................... 546
17.4.4 Diagnostics................................. 553
17.5 Numerical Printout ................................. 553
17.6 Graphics........................................ 553
17.6.1 Conditioned Scatterplots ......................... 553
17.6.2 Scatterplot Matrix............................. 554
17.6.3 Common Scaling in Comparable Plots.................. 554
17.6.4 Functions of Predicted Values....................... 555
17.7 Model Specification ................................. 556
17.7.1 S-Pn-s.................................... 556
17.7.2 SAS..................................... 557
17.8 Fitting Models When the Response Is a Sample Proportion.......... 557
17.9 LogXact........................................ 558
17.10 Exercises ....................................... 558
18 Time Series Analysis 565
18.1 Introduction ..................................... 565
18.2 The ARIMA Approach to Time Series Modeling................. 567
18.3 Autocorrelation.................................... 570
18.3.1 Autocorrelation Function (ACF)..................... 570
18.3.2 Partial Autocorrelation Function (PACF)................ 570
18.4 Analysis Steps.................................... 571
18.5 Some Algebraic Development. Including Forecasting.............. 573
18.5.1 The General ARIMA Model ....................... 573
18.5.2 Special case The AR(1) model..................... 574
18.5.3 Special Case- The MA(1) Model..................... 575
18.6 Graphical Displays for Time Series Analysis................... 575
18.7 Models with Seasonal Components ........................ 580
18.7.1 Multiplicative Seasonal ARIMA Models................. 580
18.7.2 Example co2ARIMA(0.1.1) x (0.1. l)i2 Model........... 581
18.7.3 Determining the Seasonal AR and MA Parameters.......... 581
18.8 Example of a Seasonal Model The Monthly co2 Data............. 582
18.8.1 Identification of the Model ........................ 582
18.8.2 Parameter Estimation and Diagnostic Checking............ 584
18.8.3 Forecasting................................. 589
18.9 Exercises ....................................... 589
18.A Appendix: Graphical Displays for Time Series Analysis............. 618
18.A.1 Characteristics of This Presentation of the Time Series Plot..... 619
18.A.2 Characteristics of This Presentation of the Sample ACF and PACF
Plots..................................... 619
18.A.3 Construction of Graphical Displays ................... 620
18.A.4 User Functions Written for S-Pms................... 620
xxii Contents_____________________________________________________________________.
A Software 623
A. I Statistical Software ................................. 623
A.2 Text Editing Software................................ 624
A.2.1 Emacs.................................... 624
A.2.2 Microsoft Word............................... 625
A3 Word Processing Software.............................. 625
A.3.1 MeX..................................... 626
A.3.2 Microsoft Word............................... 626
A.4 Graphics Display Software............................. 626
A.5 Operating Systems.................................. 627
A.6 Mathematical Fonts................................. 627
A.7 Directory Structure................................. 627
A.7.1 HOME Directory............................... 627
A.7.2 HH Book Online Files........................... 629
B S-Pli-s and R 631
B.l Create Your Working Directory and Make the HH Library Available..... 632
B.l.l Windows Both S-Pus and R..................... 632
B.1.2 Windows and S-PLfs........................... 633
B.1.3 Windows and R............................... 634
B.1.4 Unix Both S-Pu s and R........................ 635
B.1.5 Unix and S-PU S.............................. 636
B.1.6 Unix and R................................. 636
B.2 Using S-PLts and R with HH........................... 637
B.3 S-PlJ S for Windows Recommended Options.................. 638
B.4 HH Library Functions................................ 640
B.5 Learning the S Language.............................. 640
B.6 S Language Style................................... 643
B.7 S-PU S Inexplicable Error Messages........................ 645
B.8 Using S-PLfs with Emacs and ESS........................ 647
B.9 Constructing the HH Library with S-Plus and R................ 647
C SAS 649
C.l Make the HH Library Available.......................... 649
C.l.l Windows................................... 649
C.l.2 Unix..................................... 650
C.2 Using SAS with HH................................. 652
C.2.1 Reading HH Datasets........................... 652
C.2.2 Any Other Data Files........................... 653
C.2.3 ASCII Data Files with tab Characters................. 653
C.2.4 Windows and Unix EOL (End-of-Line) Conventions.......... 654
C.3 Macros......................................... 655
C.4 Learning the SAS Language............................ 655
C.5 SAS Coding Conventions.............................. 656
Contents xxiii
D Probability Distributions 657
D. 1 Common Probability Distributions with S-Plus and SAS Commands .... 657
D.l.l An Example Involving Calculations with the Binomial Distribution . 661
D.2 Noncentral Probability Distributions....................... 661
E Editors 663
E.l Working Style..................................... 664
E.2 Typography...................................... 665
E.3 Emacs and ESS.................................... 667
E.3.1 ESS...................................... 670
E.3.2 Mouse and Keyboard ........................... 671
E.3.3 Learning Emacs............................... 672
E.3.4 Requirements................................ 672
E.4 Microsoft Word.................................... 673
E.4.1 Learning Word............................... 673
E.4.2 Requirements................................ 673
E.5 Microsoft Excel.................................... 674
E.5.1 Database Management........................... 674
E.5.2 Organizing Calculations.......................... 674
E.5.3 Excel as a Statistical Calculator..................... 674
E.6 Exhortations. Some of Which Are Writing Style................. 677
E.6.1 Writing Style................................ 677
E.6.2 Programming Style and Common Errors................ 678
E.6.3 Presentation of Results .......................... 679
F Mathematics Preliminaries 683
F.l Algebra Review.................................... 683
F.2 Elementary Differential Calculus.......................... 685
F.3 An Application of Differential Calculus...................... 686
F.4 Topics in Matrix Algebra.............................. 687
F.4.1 Elementary Operations .......................... 688
F.4.2 Linear Independence............................ 690
F.4.3 Rank..................................... 691
F.4.4 Quadratic Forms.............................. 692
F.4.5 Orthogonal Transformations....................... 692
F.4.6 Orthogonal Basis.............................. 693
F.4.7 Matrix Factorization -QR ........................ 693
F.4.8 Matrix Factorization?Cholesky..................... 695
F.4.9 Orthogonal Polynomials.......................... 695
F.4.10 Projection Matrices ............................ 695
F.4.11 Geometry of Matrices........................... 695
F.4.12 Eigenvalues and Eigenvectors....................... 696
F.4.13 Singular Value Decomposition...................... 698
F.4.14 Generalized Inverse............................. 698
xxiv Contents
F.4.15 Solving Linear Equations......................... 699
F.5 Combinations and Permutations.......................... 700
F.5.1 Factorial................................... 700
F.5.2 Permutations................................ 700
F.5.3 Combinations................................ 700
F.6 Exercises ....................................... 701
G Graphs Based on Cartesian Products 703
G.l Structured Sets of Graphs.............................. 704
G.l.l Cartesian Products............................. 704
G.1.2 Trellis Paradigm.............................. 704
G.2 Scatterplot Matrices: splom and xysplom..................... 705
G.3 Cartesian Products of Sets of Functions...................... 706
G.l Graphs Requiring Multiple Calls to xysplom................... 06
G.5 Asymmetric Roles for the Row and Column Sets................ 707
G.6 Rotated Plots..................................... 707
G.7 Squared Residual Plots............................... 708
G.8 Alternate Presentations............................... 708
References 709
List of Datasets 721
Index 723
|
| any_adam_object | 1 |
| author | Heiberger, Richard M. 1945- |
| author_GND | (DE-588)141419997 |
| author_facet | Heiberger, Richard M. 1945- |
| author_role | aut |
| author_sort | Heiberger, Richard M. 1945- |
| author_variant | r m h rm rmh |
| building | Verbundindex |
| bvnumber | BV019401595 |
| callnumber-first | Q - Science |
| callnumber-label | QA276 |
| callnumber-raw | QA276.4 |
| callnumber-search | QA276.4 |
| callnumber-sort | QA 3276.4 |
| callnumber-subject | QA - Mathematics |
| classification_rvk | QH 500 SK 850 |
| classification_tum | DAT 368f DAT 307f MAT 620f |
| ctrlnum | (OCoLC)55067119 (DE-599)BVBBV019401595 |
| dewey-full | 519.5/0285 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.5/0285 |
| dewey-search | 519.5/0285 |
| dewey-sort | 3519.5 3285 |
| dewey-tens | 510 - Mathematics |
| discipline | Informatik Mathematik Wirtschaftswissenschaften |
| format | Book |
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| id | DE-604.BV019401595 |
| illustrated | Illustrated |
| indexdate | 2024-12-23T17:47:21Z |
| institution | BVB |
| isbn | 0387402705 9780387402703 |
| language | English |
| lccn | 2004048200 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-012863880 |
| oclc_num | 55067119 |
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| physical | XXIV, 729 S. graph. Darst. |
| publishDate | 2004 |
| publishDateSearch | 2004 |
| publishDateSort | 2004 |
| publisher | Springer |
| record_format | marc |
| series2 | Springer texts in statistics |
| spellingShingle | Heiberger, Richard M. 1945- Statistical analysis and data display an intermediate course with examples in S-plus, R, and SAS S-Plus SAS (Computer file) Grafische methoden gtt R (Langage de programmation) R (computerprogramma) gtt S-Plus gtt SAS (software) gtt Statistiek gtt Statistique - Informatique Statistique mathématique - Informatique Datenverarbeitung Statistik Mathematical statistics Data processing R (Computer program language) Statistics Data processing S-PLUS (DE-588)4321162-8 gnd SAS Programm (DE-588)4195685-0 gnd Statistische Analyse (DE-588)4116599-8 gnd R Programm (DE-588)4705956-4 gnd |
| subject_GND | (DE-588)4321162-8 (DE-588)4195685-0 (DE-588)4116599-8 (DE-588)4705956-4 |
| title | Statistical analysis and data display an intermediate course with examples in S-plus, R, and SAS |
| title_auth | Statistical analysis and data display an intermediate course with examples in S-plus, R, and SAS |
| title_exact_search | Statistical analysis and data display an intermediate course with examples in S-plus, R, and SAS |
| title_full | Statistical analysis and data display an intermediate course with examples in S-plus, R, and SAS Richard M. Heiberger ; Burt Holland |
| title_fullStr | Statistical analysis and data display an intermediate course with examples in S-plus, R, and SAS Richard M. Heiberger ; Burt Holland |
| title_full_unstemmed | Statistical analysis and data display an intermediate course with examples in S-plus, R, and SAS Richard M. Heiberger ; Burt Holland |
| title_short | Statistical analysis and data display |
| title_sort | statistical analysis and data display an intermediate course with examples in s plus r and sas |
| title_sub | an intermediate course with examples in S-plus, R, and SAS |
| topic | S-Plus SAS (Computer file) Grafische methoden gtt R (Langage de programmation) R (computerprogramma) gtt S-Plus gtt SAS (software) gtt Statistiek gtt Statistique - Informatique Statistique mathématique - Informatique Datenverarbeitung Statistik Mathematical statistics Data processing R (Computer program language) Statistics Data processing S-PLUS (DE-588)4321162-8 gnd SAS Programm (DE-588)4195685-0 gnd Statistische Analyse (DE-588)4116599-8 gnd R Programm (DE-588)4705956-4 gnd |
| topic_facet | S-Plus SAS (Computer file) Grafische methoden R (Langage de programmation) R (computerprogramma) SAS (software) Statistiek Statistique - Informatique Statistique mathématique - Informatique Datenverarbeitung Statistik Mathematical statistics Data processing R (Computer program language) Statistics Data processing S-PLUS SAS Programm Statistische Analyse R Programm |
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