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Using R for introductory statistics / John Verzani.

By: Verzani, John [author.].
Material type: materialTypeLabelBookSeries: Chapman & Hall/CRC the R series (CRC Press): Publisher: Boca Raton : CRC Press/Taylor & Francis Group, [2014]Copyright date: ©2014Edition: Second edition.Description: xvii, 502 pages ; 25 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781466590731 .Subject(s): Statistics -- Data processing | R (Computer program language)DDC classification: 519.5
Contents:
Getting started -- Univariate data -- Bivariate data -- Multivariate data -- Multivariate graphics -- Populations -- Statistical inference -- Confidence intervals -- Significance tests -- Goodness of fit -- Linear regression -- Analysis of variance -- Extensions of the linear model.
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Enhanced descriptions from Syndetics:

The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version.

See What's New in the Second Edition:

Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical.

The book has an accompanying package, UsingR, available from CRAN, R's repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text.

The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.

Bibliography: (pages 489-493) and index.

Getting started -- Univariate data -- Bivariate data -- Multivariate data -- Multivariate graphics -- Populations -- Statistical inference -- Confidence intervals -- Significance tests -- Goodness of fit -- Linear regression -- Analysis of variance -- Extensions of the linear model.

CIT Module STAT 8006 - Supplementary reading.

Table of contents provided by Syndetics

  • Preface (p. xv)
  • 1 Getting started (p. 1)
  • 1.1 What is data? (p. 1)
  • 1.2 Getting started with R (p. 3)
  • Installing R (p. 3)
  • Installing RSTUDIO (p. 4)
  • R's command line (p. 5)
  • Variables (p. 7)
  • Functions (p. 8)
  • The workspace (p. 12)
  • External packages (p. 15)
  • Data sets (p. 16)
  • Problems (p. 18)
  • 2 Univariate data (p. 20)
  • 2.1 Data vectors (p. 22)
  • Structured data (p. 28)
  • Indexing (p. 29)
  • Data types (p. 33)
  • Numeric data types (p. 33)
  • Categorical data types (p. 34)
  • Date and time types (p. 39)
  • Logical data (p. 41)
  • Problems (p. 45)
  • 2.2 Functions (p. 48)
  • Problems (p. 50)
  • 2.3 Numeric summaries (p. 50)
  • Center (p. 51)
  • The sample mean (p. 51)
  • The sample median (p. 55)
  • Measures of position (p. 56)
  • Other measures of center (p. 59)
  • Spread (p. 59)
  • The variance and standard deviation (p. 60)
  • The IQR (p. 65)
  • Shape (p. 66)
  • Viewing the shape of a data set (p. 70)
  • Problems (p. 81)
  • 2.4 Categorical data (p. 85)
  • Problems (p. 87)
  • 3 Bivariate data (p. 88)
  • 3.1 Independent samples (p. 88)
  • Problems (p. 93)
  • 3.2 Data manipulation basics (p. 94)
  • Lists (p. 94)
  • Data frames (p. 96)
  • Model formulas (p. 97)
  • Problems (p. 101)
  • 3.3 Paired data (p. 102)
  • Correlation (p. 105)
  • Trends (p. 115)
  • Transformations (p. 120)
  • Alternative trend lines (p. 123)
  • Problems (p. 128)
  • 3.4 Bivariate categorical data (p. 132)
  • Tables (p. 132)
  • Two-way tables from summarized data (p. 132)
  • Two-way tables from unsummarized data (p. 134)
  • Marginal distributions of two-way tables (p. 135)
  • Conditional distributions of two-way tables (p. 136)
  • The xtabs function (p. 137)
  • Graphical summaries of two-way contingency tables (p. 140)
  • Mosaic plots (p. 141)
  • Measures of association for categorical data (p. 143)
  • Problems (p. 149)
  • 4 Multivariate data (p. 150)
  • 4.1 Data structures in R (p. 150)
  • Problems (p. 154)
  • 4.2 Working with data frames (p. 155)
  • Problems (p. 266)
  • 4.3 Applying a function over a collection (p. 167)
  • Map (p. 168)
  • Filter (p. 177)
  • Reduce (p. 177)
  • Problems (p. 179)
  • 4.4 Using external data (p. 181)
  • Spreadsheet data (p. 181)
  • Web-based data sets (p. 182)
  • 5 Multivariate graphics (p. 189)
  • 5.1 Base graphics (p. 189)
  • Problems (p. 196)
  • 5.2 Lattice graphics (p. 197)
  • Problems (p. 200)
  • 5.3 The ggplot2 package (p. 200)
  • Geoms (p. 201)
  • Grouping (p. 203)
  • Statistical transformations (p. 204)
  • Faceting (p. 207)
  • Problems (p. 210)
  • 6 Populations (p. 211)
  • 6.1 Populations (p. 211)
  • Discrete random variables (p. 213)
  • Using sample to generate random values (p. 214)
  • The mean and standard deviation (p. 215)
  • Continuous random variables (p. 216)
  • The p.d.f. and c.d.f (p. 218)
  • The mean and standard deviation (p. 218)
  • Quantiles (p. 218)
  • Sampling from a population (p. 219)
  • Random samples generated by sample (p. 219)
  • Sampling distributions (p. 220)
  • Problems (p. 221)
  • 6.2 Families of distributions (p. 222)
  • The d, p, q, and r functions (p. 222)
  • Binomial, normal, and some other named distributions (p. 224)
  • Bernoulli random variables (p. 224)
  • Binomial random variables (p. 225)
  • Normal random variables (p. 227)
  • Popular distributions to describe populations (p. 231)
  • Uniform distribution (p. 231)
  • Exponential distribution (p. 232)
  • Lognormal distribution (p. 233)
  • Sampling distributions (p. 233)
  • Problems (p. 234)
  • 6.3 The central limit theorem (p. 236)
  • Normal parent population (p. 237)
  • Nonnormal parent population (p. 238)
  • Problems (p. 240)
  • 7 Statistical inference (p. 242)
  • 7.1 Simulation (p. 244)
  • Repeating a simulation easily (p. 244)
  • Problems (p. 252)
  • 7.2 Significance tests (p. 252)
  • 7.3 Estimation, confidence intervals (p. 255)
  • The basic bootstrap (p. 258)
  • 7.4 Bayesian analysis (p. 259)
  • 8 Confidence intervals (p. 262)
  • 8.1 Confidence intervals for a population proportion, p (p. 264)
  • Problems (p. 269)
  • 8.2 Confidence intervals for the population mean (p. 271)
  • One-sided confidence intervals (p. 274)
  • Problems (p. 276)
  • 8.3 Other confidence intervals (p. 278)
  • Confidence interval for a2 (p. 278)
  • Problems (p. 280)
  • 8.4 Confidence intervals for differences (p. 281)
  • Difference of proportions (p. 282)
  • Difference of means (p. 283)
  • Matched samples (p. 286)
  • Problems (p. 287)
  • 8.5 Confidence intervals for the median (p. 288)
  • Confidence intervals based on the binomial distribution (p. 288)
  • Confidence intervals based on signed-rank statistic (p. 289)
  • Confidence intervals based on the rank-sum statistic (p. 290)
  • Problems (p. 292)
  • 9 Significance tests (p. 294)
  • 9.1 Significance test for a population proportion (p. 299)
  • Using prop, test to compute p-values (p. 301)
  • Problems (p. 302)
  • 9.2 Significance test for the mean (f-tests) (p. 304)
  • Power (p. 307)
  • Problems (p. 309)
  • 9.3 Significance tests and confidence intervals (p. 310)
  • 9.4 Significance tests for the median (p. 312)
  • The sign test (p. 312)
  • The signed-rank test (p. 313)
  • Problems (p. 315)
  • 9.5 Two-sample tests of proportion (p. 316)
  • Problems (p. 319)
  • 9.6 Two-sample tests of center (p. 321)
  • Two-sample tests of center with normal populations (p. 322)
  • Matched samples (p. 325)
  • The Wilcoxon rank-sum test for equality of center (p. 328)
  • Problems (p. 331)
  • 10 Goodness of fit (p. 334)
  • 10.1 The chi-squared goodness-of-fit test (p. 334)
  • The multinomial distribution (p. 334)
  • Pearson's X 2 -statistic (p. 336)
  • Partially specified null hypotheses (p. 339)
  • Problems (p. 341)
  • 10.2 The chi-squared test of independence (p. 344)
  • The chi-squared test of homogeneity (p. 348)
  • Problems (p. 350)
  • 10.3 Goodness-of-fit tests for continuous distributions (p. 352)
  • Kolmogorov-Smirnov test (p. 352)
  • The Shapiro-Wilk test for normality (p. 357)
  • Finding parameter values using fitdistr (p. 359)
  • Problems (p. 362)
  • 11 Linear regression (p. 364)
  • 11.1 The simple linear regression model (p. 364)
  • Estimating the parameters in simple linear regression (p. 365)
  • Using lm to find the estimates (p. 366)
  • Extractor functions for lm (p. 367)
  • Problems (p. 368)
  • 11.2 Statistical inference for simple linear regression (p. 369)
  • Statistical inferences (p. 370)
  • Marginal f-tests (p. 370)
  • The F-test (p. 371)
  • R 2 -the coefficient of determination (p. 373)
  • Using lm to find values for a regression model (p. 374)
  • Confidence intervals (p. 374)
  • Standard error (p. 374)
  • Significance tests (p. 376)
  • Finding a 2 , R 2 (p. 376)
  • F-test for ß 1 = 0 (p. 377)
  • Predicting the response with predict (p. 377)
  • Testing the model assumptions (p. 378)
  • Assessing the linear model for the mean (p. 379)
  • Assessing the residuals (p. 380)
  • Influential points (p. 381)
  • Prediction intervals (p. 382)
  • Confidence intervals for ¿ yx (p. 385)
  • Problems (p. 386)
  • 11.3 Multiple linear regression (p. 390)
  • Types of models (p. 390)
  • Fitting the multiple regression model using lm (p. 392)
  • Using update with model formulas (p. 394)
  • Interpreting the regression parameters (p. 395)
  • Statistical inferences (p. 396)
  • Model selection (p. 397)
  • Partial F-test (p. 398)
  • The Akaike information criterion (p. 400)
  • Problems (p. 402)
  • 12 Analysis of variance (p. 404)
  • 12.1 One-way ANOVA (p. 404)
  • Using R's model formulas to specify ANOVA models (p. 408)
  • Using oneway. test to perform ANOVA (p. 408)
  • Using aov for ANOVA (p. 409)
  • The nonparametric Kruskal-Wallis test (p. 411)
  • Problems (p. 414)
  • 12.2 Using lm for ANOVA (p. 416)
  • Treatment coding for analysis of variance (p. 418)
  • Comparing multiple differences (p. 421)
  • Problems (p. 424)
  • 12.3 ANCOVA (p. 425)
  • Problems (p. 428)
  • 12.4 Two-way ANOVA (p. 429)
  • Interaction plots (p. 430)
  • Fitting a two-way ANOVA (p. 431)
  • Blocking variables (p. 435)
  • Problems (p. 437)
  • 13 Extensions of the linear model (p. 440)
  • 13.1 Logistic regression (p. 440)
  • Generalized linear models (p. 443)
  • Fitting the model using glm (p. 443)
  • 13.2 Nonlinear models (p. 448)
  • Fitting nonlinear models with nls (p. 449)
  • Problems (p. 455)
  • A Programming (p. 458)
  • A.1 Functions (p. 458)
  • Function names (p. 458)
  • Arguments (p. 462)
  • Body (p. 467)
  • Control flow (p. 467)
  • Variable scope (p. 472)
  • Closures (p. 474)
  • A.2 Generic functions (p. 475)
  • S3 (p. 475)
  • S4 (p. 479)
  • Reference classes (p. 479)
  • Bibliography (p. 489)
  • Index (p. 494)