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Bibliografická citace

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0 (hodnocen0 x )
(0.5) Půjčeno:1x 
BK
1st pub.
Chichester : Wiley, 2008
xviii, 599 s. : il., grafy ; 25 cm

objednat
ISBN 978-0-470-75805-2 (váz.)
Obsahuje bibliografii na s. [563]-568 a rejstříky
000042730
Contents // Preface xv // Part I Data in statistics and R // 1 Basic R 3 // 1.1 Preliminaries 4 // 1.1.1 An R session 4 // 1.1.2 Editing statements 8 // 1.1.3 The functions help ?, help. searchO and example () 8 // 1.1.4 Expressions 10 // 1.1.5 Comments, line continuation and Esc 11 // 1.1.6 source(), sink() and history0 11 // 1.2 Modes 13 // 1.3 Vectors 14 // 1.3.1 Creating vectors 14 // 1.3.2 Useful vector functions 15 // 1.3.3 Vector arithmetic 15 // 1.3.4 Character vectors 17 // 1.3.5 Subsets and index vectors 18 // 1.4 Arithmetic operators and special values 20 // 1.4.1 Arithmetic operators 20 // 1.4.2 Logical operators 21 // 1.4.3 Special values 22 // 1.5 Objects 24 // 1.5.1 Orientation 24 // 1.5.2 Object attributes 26 // 1.6 Programming 28 // 1.6.1 Execution controls 28 // 1.6.2 Functions 30 // 1.7 Packages 33 // viii Contents // 1.8 Graphics 34 // 1.8.1 High-level plotting functions 35 // 1.8.2 Low-level plotting functions 36 // 1.8.3 Interactive plotting functions 36 // 1.8.4 Dynamic plotting 36 // 1.9 Customizing the workspace 36 // 1.10 Projects 37 // 1.11 A note about producing figures and output 39 // 1.11.1 opengO 39 // 1.11.2 savegO 40 // 1.11.3 h() 40 // 1.11.4 nqdO 40 // 1.12 Assignments 41 // 2 Data in statistics and in R 45 // 2.1 Types of data 45 // 2.1.1 Factors 45 // 2.1.2 Ordered factors 48 // 2.1.3 Numerical variables 49 // 2.1.4 Character variables 50 // 2.1.5 Dates in R 50 // 2.2 Objects that hold data 50 // 2.2.1 Arrays and matrices 51 // 2.2.2 Lists 52
// 2.2.3 Data frames 54 // 2.3 Data organization 55 // 2.3.1 Data tables 55 // 2.3.2 Relationships among tables 57 // 2.4 Data import, export and connections 58 // 2.4.1 Import and export 58 // 2.4.2 Data connections 60 // 2.5 Data manipulation 63 // 2.5.1 Flat tables and expand tables 63 // 2.5.2 Stack, unstack and reshape 64 // 2.5.3 Split, unsplit and unlist 66 // 2.5.4 Cut 66 // 2.5.5 Merge, union and intersect 68 // 2.5.6 is. element () 69 // 2.6 Manipulating strings 71 // 2.7 Assignments 72 // 3 Presenting data 75 // 3.1 Tables and the flavors of apply () 75 // 3.2 Bar plots 77 // 3.3 Histograms 81 // 3.4 Dot charts 85 // 3.5 Scatter plots 86 // 3.6 Lattice plots 88 // Contents ix // 3.7 Three-dimensional plots and contours 90 // 3.8 Assignments 90 // Part II Probability, densities and distributions // 4 Probability and random variables 97 // 4.1 Set theory 98 // 4.1.1 Sets and algebra of sets 98 // 4.1.2 Set theory in R 103 // 4.2 Trials, events and experiments 103 // 4.3 Definitions and properties of probability 108 // 4.3.1 Definitions of probability 108 // 4.3.2 Properties of probability 111 // 4.3.3 Equally likely events 112 // 4.3.4 Probability and set theory 112 // 4.4 Conditional probability and independence 113 // 4.4.1 Conditional probability 114 // 4.4.2 Independence 116 // 4.5 Algebra with probabilities 118 // 4.5.1 Sampling with and without replacement 118 // 4.5.2 Addition 119 // 4.5.3 Multiplication 120 // 4.5.4 Counting rules 120 // 4.6 Random variables 127
// 4.7 Assignments 128 // 5 Discrete densities and distributions 137 // 5.1 Densities 137 // 5.2 Distributions 141 // 5.3 Properties 143 // 5.3.1 Densities 144 // 5.3.2 Distributions 144 // 5.4 Expected values 144 // 5.5 Variance and standard deviation 146 // 5.6 The binomial 147 // 5.6.1 Expectation and variance 151 // 5.6.2 Decision making with the binomial 151 // 5.7 The Poisson 153 // 5.7.1 The Poisson approximation to the binomial 155 // 5.7.2 Expectation and variance 156 // 5.7.3 Variance of the Poisson density 157 // 5.8 Estimating parameters 161 // 5.9 Some useful discrete densities 163 // 5.9.1 Multinomial 163 // 5.9.2 Negative binomial 165 // 5.9.3 Hyper geometric 168 // 5.10 Assignments 171 // x Contents // 6 Continuous distributions and densities 177 // 6.1 Distributions 177 // 6.2 Densities 180 // 6.3 Properties 181 // 6.3.1 Distributions 181 // 6.3.2 Densities 182 // 6.4 Expected values 183 // 6.5 Variance and standard deviation 184 // 6.6 Areas under density curves 185 // 6.7 Inverse distributions and simulations 187 // 6.8 Some useful continuous densities 189 // 6.8.1 Double exponential (Laplace) 189 // 6.8.2 Normal 191 // 6.8.3 x2 193 // 6.8.4 Student-t 195 // 6.8.5 F 197 // 6.8.6 Lognormal 198 // 6.8.7 Gamma 199 // 6.8.8 Beta 201 // 6.9 Assignments 203 // 7 The normal and sampling densities 205 // 7.1 The normal density 205 // 7.1.1 The standard normal 207 // 7.1.2 Arbitrary normal 210 // 7.1.3 Expectation and variance of the normal 212 // 7.2 Applications of
the normal 213 // 7.2.1 The normal approximation of discrete densities 214 // 7.2.2 Normal approximation to the binomial 215 // 7.2.3 The normal approximation to the Poisson 218 // 7.2.4 Testing for normality 220 // 7.3 Data transformations 225 // 7.4 Random samples and sampling densities 226 // 7.4.1 Random samples 227 // 7.4.2 Sampling densities 228 // 7.5 A detour: using R efficiently 230 // 7.5.1 Avoiding loops 230 // 7.5.2 Timing execution 230 // 7.6 The sampling density of the mean 232 // 7.6.1 The central limit theorem 232 // 7.6.2 The sampling density 232 // 7.6.3 Consequences of the central limit theorem 234 // 7.7 The sampling density of proportion 235 // 7.7.1 The sampling density 236 // 7.7.2 Consequence of the central limit theorem 238 // 7.8 The sampling density of intensity 239 // 7.8.1 The sampling density 239 // Contents xi // 7.8.2 Consequences of the central limit theorem // 7.9 The sampling density of variance // 7.10 Bootstrap: arbitrary parameters of arbitrary densities // 7.11 Assignments // 241 // 241 // 242 // 243 // Part III Statistics // 8 Exploratory data analysis // 8.1 Graphical methods // 8.2 Numerical summaries // 8.2.1 Measures of the center of the data // 8.2.2 Measures of the spread of data // 8.2.3 The Chebyshev and empirical rules // 8.2.4 Measures of association between variables // 8.3 Visual summaries // 8.3.1 Box plots // 8.3.2 Lag plots // 8.4 Assignments // 251 // 252 // 253 253 261 267 269 275 // 275 // 276 // 277 // 9 Point and interval
estimation // 9.1 Point estimation // 9.1.1 Maximum likelihood estimators // 9.1.2 Desired properties of point estimators // 9.1.3 Point estimates for useful densities // 9.1.4 Point estimate of population variance // 9.1.5 Finding MLE numerically // 9.2 Interval estimation // 9.2.1 Large sample confidence intervals // 9.2.2 Small sample confidence intervals // 9.3 Point and interval estimation for arbitrary densities // 9.4 Assignments // 283 // 284 // 284 // 285 288 // 292 // 293 // 294 // 295 301 304 307 // 10 Single sample hypotheses testing // 10.1 Null and alternative hypotheses // 10.1.1 Formulating hypotheses // 10.1.2 Types of errors in hypothesis testing // 10.1.3 Choosing a significance level // 10.2 Large sample hypothesis testing // 10.2.1 Means // 10.2.2 Proportions // 10.2.3 Intensities // 10.2.4 Common sense significance // 10.3 Small sample hypotheses testing // 10.3.1 Means // 10.3.2 Proportions // 10.3.3 Intensities // 313 // 313 // 314 // 316 // 317 // 318 318 // 323 // 324 // 325 // 326 // 326 // 327 // 328 // xii Contents // 10.4 Arbitrary statistics of arbitrary densities 329 // 10.5 p-values 330 // 10.6 Assignments 333 // 11 Power and sample size for single samples 341 // 11.1 Large sample 341 // 11.1.1 Means 342 // 11.1.2 Proportions 352 // 11.1.3 Intensities 356 // 11.2 Small samples 359 // 11.2.1 Means 359 // 11.2.2 Proportions 361 // 11.2.3 Intensities 363 // 11.3 Power and sample size for arbitrary densities 365 // 11.4 Assignments 365 // 12 Two samples 369
// 12.1 Large samples 370 // 12.1.1 Means 370 // 12.1.2 Proportions 375 // 12.1.3 Intensities 379 // 12.2 Small samples 380 // 12.2.1 Estimating variance and standard error 380 // 12.2.2 Hypothesis testing and confidence intervals for variance 382 // 12.2.3 Means 384 // 12.2.4 Proportions 386 // 12.2.5 Intensities 387 // 12.3 Unknown densities 388 // 12.3.1 Rank sum test 389 // 12.3.2 t vs. rank sum 392 // 12.3.3 Signed rank test 392 // 12.3.4 Bootstrap 394 // 12.4 Assignments 396 // 13 Power and sample size for two samples 401 // 13.1 Two means from normal populations 401 // 13.1.1 Power 401 // 13.1.2 Sample size 404 // 13.2 Two proportions 406 // 13.2.1 Power 407 // 13.2.2 Sample size 409 // 13.3 Two rates 410 // 13.4 Assignments 415 // 14 Simple linear regression 417 // 14.1 Simple linear models 417 // 14.1.1 The regression line 418 // 14.1.2 Interpretation of simple linear models 419 // Contents xiii // 14.2 Estimating regression coefficients 422 // 14.3 The model goodness of fit 428 // 14.3.1 The F test 428 // 14.3.2 The correlation coefficient 433 // 14.3.3 The correlation coefficient vs. the slope 434 // 14.4 Hypothesis testing and confidence intervals 434 // 14.4.1 i-test for model coefficients 435 // 14.4.2 Confidence intervals for model coefficients 435 // 14.4.3 Confidence intervals for model predictions 436 // 14.4.4 t-test for the correlation coefficient 438 // 14.4.5 2 tests for the correlation coefficient 439 // 14.4.6 Confidence intervals for the correlation coefficient 441
// 14.5 Model assumptions 442 // 14.6 Model diagnostics 443 // 14.6.1 The hat matrix 445 // 14.6.2 Standardized residuals 447 // 14.6.3 Studentized residuals 448 // 14.6.4 The RSTUDENT residuals 449 // 14.6.5 The DFFITS residuals 453 // 14.6.6 The DFBETAS residuals 454 // 14.6.7 Cooke’s distance 456 // 14.6.8 Conclusions 457 // 14.7 Power and sample size for the correlation coefficient 458 // 14.8 Assignments 459 // 15 Analysis of variance 463 // 15.1 One-way, fixed-effects ANOVA 463 // 15.1.1 The model and assumptions 464 // 15.1.2 The F-test 469 // 15.1.3 Paired group comparisons 475 // 15.1.4 Comparing sets of groups 484 // 15.2 Non-parametrie one-way ANOVA 488 // 15.2.1 The Kruskal-Wallis test 488 // 15.2.2 Multiple comparisons 491 // 15.3 One-way, random-effects ANOVA 492 // 15.4 Two-way ANOVA 495 // 15.4.1 Two-way, fixed-effects ANOVA 496 // 15.4.2 The model and assumptions 496 // 15.4.3 Hypothesis testing and the F-test 500 // 15.5 Two-way linear mixed effects models 505 // 15.6 Assignments 509 // 16 Simple logistic regression 511 // 16.1 Simple binomial logistic regression 511 // 16.2 Fitting and selecting models 519 // 16.2.1 The log likelihood function 519 // 16.2.2 Standard errors of coefficients and predictions 521 // 16.2.3 Nested models 524 // xiv Contents // 16.3 Assessing goodness of fit 525 // 16.3.1 The Pearson %2 statistic 526 // 16.3.2 The deviance %2 statistic 527 // 16.3.3 The group adjusted x2 statistic 528 // 16.3.4 The ROC curve 529 // 16.4 Diagnostics 533
// 16.4.1 Analysis of residuals 533 // 16.4.2 Validation 536 // 16.4.3 Applications of simple logistic regression to 2 x 2 tables 536 // 16.5 Assignments 539 // 17 Application: the shape of wars to come 541 // 17.1 A statistical profile of the war in Iraq 541 // 17.1.1 Introduction 542 // 17.1.2 The data 542 // 17.1.3 Results 543 // 17.1.4 Conclusions 550 // 17.2 A statistical profile of the second Intifada 552 // 17.2.1 Introduction 552 // 17.2.2 The data 553 // 17.2.3 Results 553 // 17.2.4 Conclusions 561 // References 563 // R Index 569 // General Index 583

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