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BK

Boca Raton : Chapman & Hall/CRC, c2005

xvi, 414 s. : il., grafy ; 25 cm

ISBN 1-58488-450-9 (váz.)

Obsahuje rejstřík

Statistika matematická - R (jazyk) - učebnice vysokošk.

000006848

1 Data 1 // 1.1 What is data? 1 // 1.1.1 Problems 4 // 1.2 Some R essentials 4 // 1.2.1 Starting R 5 // 1.2.2 Using R as a calculator 6 // 1.2.3 Assignment 7 // 1.2.4 Using ..() to enter data 9 // 1.2.5 Using functions on a data vector 10 // 1.2.6 Creating structured data 13 // 1.2.7 Problems 14 // 1.3 Accessing data by using indices 16 // 1.3.1 Assigning values to data vector 17 // 1.3.2 Logical values 18 // 1.3.3 Missing values 20 // 1.3.4 Managing the work environment 21 // 1.3.5 Problems 21 // 1.4 Reading in other sources of data 23 // 1.4.1 Using R’s built-in libraries and data sets 23 // 1.4.2 Using the data sets that accompany this book 26 // 1.4.3 Other methods of data entry 27 // 1.4.4 Problems 29 // 2 Univariate data 31 // 2.1 Categorical data 32 // 2.1.1 Tables 32 // 2.1.2 Barplots 33 // 2.1.3 Pie charts 36 // 2.1.4 Dot charts 37 // 2.1.5 Factors 38 // 2.1.6 Problems 39 // 2.2 Numeric data 41 // 2.2.1 Stem-and-leaf plots 41 // 2.2.2 Strip charts 42 // 2.2.3 The center: mean, median, and mode 43 // 2.2.4 Variation: the variance, standard deviation, and IQR 48 // 2.2.5 Problems 53 // 2.3 Shape of a distribution 56 // 2.3.1 Histogram 56 // 2.3.2 Modes, symmetry, and skew 61 // 2.3.3 Boxplots 64 // 2.3.4 Problems 66 // 3 Bivariate data 69 // 3.1 Pairs of categorical variables 69 // 3.1.1 Making two-way tables from summarized data 70 // 3.1.2 Making two-way tables from unsummarized data 71 // 3.1.3 Marginal distributions of two-way tables 72 // 3.1.4 Conditional distributions of two-way tables 73 // 3.1.5 Graphical summaries of two-way contingency tables 74 // 3.1.6 Problems 75 // 3.2 Comparing independent samples 77 // 3.2.1 Side-by-side boxplots 77 // 3.2.2 Densityplots 78 // 3.2.3 Strip charts 79 // 3.2.4 Quantile-quantile plots 79 // 3.2.5 Problems 80 // 3.3 Relationships in numeric data 82 // 3.3.1 Using scatterplots to investigate relationships 82 //

3.3.2 The correlation between two variables 86 // 3.3.3 Problems 89 // 3.4 Simple linear regression 90 // 3.4.1 Using the regression model for prediction 92 // 3.4.2 Finding the regression coefficients using lm() 92 // 3.4.3 Transformations of the data 95 // 3.4.4 Interacting with a scatterplot 96 // 3.4.5 Outliers in the regression model 98 // 3.4.6 Resistant regression fines: lqs() and rim() 99 // 3.4.7 Trendlines 101 // 3.4.8 Problems 102 // 4 Multivariate Data // 4.1 Viewing multivariate data 105 // 105 // Contents Ш vii // 4.1.1 Summarizing categorical data 105 // 4.1.2 Comparing independent samples 108 // 4.1.3 Comparing relationships 109 // 4.1.4 Problems 112 // 4.2 R basics: data frames and lists 113 // 4.2.1 Creating a data frame or list 114 // 4.2.2 Accessing values in a data frame 115 // 4.2.3 Setting values in a data frame or list 122 // 4.2.4 Applying functions to a data frame or list 123 // 4.2.5 Problems 124 // 4.3 Using model formula with multivariate data 125 // 4.3.1 Boxplots from a model formula 126 // 4.3.2 The plotO function with model formula 126 // 4.3.3 Creating contingency tables with xtabsO 127 // 4.3.4 Manipulating data frames: split () and stack О 129 // 4.3.5 Problems 130 // 4.4 Lattice graphics 131 // 4.4.1 Problems 134 // 4.5 Types of data in R 135 // 4.5.1 Factors 136 // 4.5.2 Coercion of objects 138 // 5 Describing populations 141 // 5.1 Populations 141 // 5.1.1 Discrete random variables 142 // 5.3.2 Continuous random variables 144 // 5.1.3 Sampling from a population 146 // 5.1.4 Sampling distributions 148 // 5.1.5 Problems 148 // 5.2 Families of distributions 149 // 5.2.1 The d, p, q, and r functions 149 // 5.2.2 Binomial, normal, and some other named distributions 150 // 5.2.3 Popular distributions to describe populations 155 // 5.2.4 Sampling distributions 157 // 5.2.5 Problems 158 // 5.3 The central limit theorem 160 //

5.3.1 Normal parent population 160 // 5.3.2 Nonnormal parent population 161 // 5.3.3 Problems 163 // 6 Simulation 165 // 6.1 The normal approximation for the binomial 165 // 6.2 for loops 166 // 6.3 Simulations related to the central limit theorem 168 // viii â–  Contents // 6.4 Defining a function 169 // 6.4.1 Editing a function 169 // 6.4.2 Function arguments 170 // 6.4.3 The function body 170 // 6.5 Investigating distributions 172 // 6.5.1 Script files and source () 173 // 6.5.2 The geometric distribution 174 // 6.6 Bootstrap samples 176 // 6.7 Alternates to for loops 177 // 7 Confidence intervals 181 // 7.1 Confidence interval ideas 181 // 7.1.1 Finding confidence intervals using simulation 181 // 7.2 Confidence intervals for a population proportion, p 184 // 7.2.1 Using prop. test () to find confidence intervals 187 // 7.2.2 Problems 188 // 7.3 Confidence intervals for the population mean 190 // 7.3.1 One-sided confidence intervals 193 // 7.3.2 Problems 194 // 7.4 Other confidence intervals 197 // 7.4.1 Confidence interval for a2 197 // 7.4.2 Problems 198 // 7.5 Confidence intervals for differences 199 // 7.5.1 Difference of proportions 199 // 7.5.2 Difference of means 201 // 7.5.3 Matched samples 204 // 7.5.4 Problems 205 // 7.6 Confidence intervals for the median 207 // 7.6.1 Confidence intervals based on the binomial 207 // 7.6.2 Confidence intervals based on signed-rank statistic 208 // 7.6.3 Confidence intervals based on the rank-sum statistic 209 // 7.6.4 Problems 211 // 8 Significance tests 213 // 8.1 Significance test for a population proportion 218 // 8.1.1 Using prop, test() to compute p-values 220 // 8.1.2 Problems 221 // 8.2 Significance test for the mean (ř-tests) 222 // 8.2.1 Problems 225 // 8.3 Significance tests and confidence intervals 227 // 8.4 Significance tests for the median 228 // 8.4.1 The sign test 228 //

8.4.2 The signed-rank test 230 // 8.4.3 Problems 232 // 8.5 Two-sample tests of proportion 233 // 8.5.1 Problems 235 // 8.6 Two-sample tests of center 237 // 8.6.1 Two sample tests of center with normal populations 237 // 8.6.2 Matched samples 241 // 8.6.3 The Wilcoxon rank-sum test for equality of center 244 // 8.6.4 Problems 246 // 9 Goodness of fit // 9.1 The chi-squared goodness-of-fit test 249 // 9.1.1 The multinomial distribution 249 // 9.1.2 Pearson’s %2 statistic 250 // 9.1.3 Problems 254 // 9.2 The chi-squared test of independence 258 // 9.2.1 The chi-squared test of homogeneity 262 // 9.2.2 Problems 264 // 9.3 Goodness-of-fit tests for continuous distributions 266 // 9.3.1 Kolmogorov-Smimov test 266 // 9.3.2 The Shapiro-Wilk test for normality 270 // 9.3.3 Finding parameter values using fitdistrO 273 // 9.3.4 Problems 275 // 10 Linear regression // 10.1 The simple linear regression model 277 // 10.1.1 Model formulas for linear models 278 // 10.1.2 Examples of the linear model 278 // 10.1.3 Estimating the parameters in simple linear regression 279 // 10.1.4 Using lm() to find the estimates 280 // 10.1.5 Problems 283 // 10.2 Statistical inference for simple linear regression 284 // 10.2.1 Testing the model assumptions 284 // 10.2.2 Statistical inferences 288 // 10.2.3 Using lm() to find values for a regression model 292 // 10.2.4 Problems 297 // 10.3 Multiple linear regression 300 // 10.3.1 Types of models 301 // 10.3.2 Fitting the multiple regression model using ImO 302 // 10.3.3 Interpreting the regression parameters 305 // 10.3.4 Statistical inferences 306 // 10.3.5 Model selection 307 // 10.3.6 Problems 310 // 11 Analysis of variance // 11.1 One-way A NOVA 313 // 11.1.1 Using R’s model formulas to specify ANOVA models 316 // 11.1.2 Using oneway .test () to perform ANOVA 317 // 11.1.3 Using aovQ for ANOVA 317 //

11.3.4 The nonparametric Kruskal-Wallis test 319 // 11.1.5 Problems 322 // 11.2 Using ImO for ANOVA 323 // 11.2.1 Treatment coding for analysis of variance 326 // 11.2.2 Comparing multiple differences 328 // 11.2.3 Problems 330 // 11.3 ANCOVA 332 // 11.3.1 Problems 334 // 11.4 Two-way ANOVA 335 // 11.4.1 Treatment coding for additive two-way ANOVA 336 // 11.4.2 Testing for row or column effects 336 // 11.4.3 Testing for interactions 337 // 11.4.4 Problems 341 // 12 Two extensions of the linear model 343 // 12.1 Logistic regression 343 // 12.1.1 Generalized linear models 346 // 12.1.2 Fitting the model using glm() 346 // 12.2 Nonlinear models 351 // 12.2.1 Fitting nonlinear models with nls () 352 // A Getting, installing, and running R 359 // A.l Installing and starting R 359 // A. 1.1 Binary installation under Windows 360 A. 1.2 Binary installation under Linux 360 A. 1.3 Binary installation under Mac OS X 361 A. 1.4 Installing from the source code 361 // A. 1.5 Startup files 361 // A.2 Extending R using additional packages 362 // A.2.1 Upgrading an existing installation 363 // B Graphical user interfaces and R 365 // B.1 The Windows GUI 365 // B.2 The Mac OS X GUI 367 // B.3 Rcdmr 368 // C Teaching with R 371 // D More on graphics with R 373 // D.1 Low- and high-level graphic functions 373 // D.1.1 Setting up a plot figure 374 // D.1.2 Adding to a figure 378 // C D. 1.3 Printing or saving a figure 381 // D.2 Creating new graphics in R 381 // E Programming in R // E.1 Editing functions 387 // E.1.1 Using edit () 387 E.2 Using functions 388 // E.2.1 Function arguments 389 E.2.2 Function body and return values 391 E.2.3 Conditional evaluation 393 E.2.4 Looping 394 E.3 Using files and a better editor 395 E.3.1 Using an external editor 395 // E.4 Object-oriented programming with R 396 // E.4.1 Method dispatch 397 // E.4.2 S3 methods, S4 methods, and the OOP package 399 // Index 409