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Introductory statistics with R / Peter Dalgaard

Main Author Dalgaard, Peter Country Estados Unidos. Publication New York : Springer, cop. 2002 Description XI, 267 p. ; 24 cm Series Statistics and computing ISBN 0-387-95475-9 CDU 519.2
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Item type Current location Call number Status Date due Barcode Item holds Course reserves
Monografia Biblioteca Geral da Universidade do Minho
BGUM 519.2 - D Available 314847
Monografia Biblioteca Geral da Universidade do Minho
BGUM 519.2 - D Available 334368

Licenciatura em Estatística Aplicada Laboratórios de Estatística II 2º semestre

Licenciatura em Estatística Aplicada Laboratórios de Estatística III 1º semestre

Monografia Biblioteca Geral da Universidade do Minho
BGUM 519.2 - D Checked out 2022-03-14 352310

Mestrado em Estatística para Ciência de Dados Estatística Computacional 1º semestre

Total holds: 0

Enhanced descriptions from Syndetics:

R is an open-source software package that duplicates the look and functionality of S-PLUS. It can be freely downloaded and it works on multiple computer platforms. This book provides an elementary introduction to R. In each chapter, brief introductory sections are followed by code examples and comments from the computational and statistical viewpoint. A supplementary R package containing the datasets can be downloaded from the web.

Table of contents provided by Syndetics

  • Preface (p. vii)
  • 1 Basics (p. 1)
  • 1.1 First steps (p. 1)
  • 1.1.1 An overgrown calculator (p. 3)
  • 1.1.2 Assignments (p. 3)
  • 1.1.3 Vectorized arithmetic (p. 4)
  • 1.1.4 Standard procedures (p. 6)
  • 1.1.5 Graphics (p. 7)
  • 1.2 R language essentials (p. 10)
  • 1.2.1 Expressions and objects (p. 10)
  • 1.2.2 Functions and arguments (p. 10)
  • 1.2.3 Vectors (p. 12)
  • 1.2.4 Missing values (p. 13)
  • 1.2.5 Functions that create vectors (p. 13)
  • 1.2.6 Matrices and arrays (p. 14)
  • 1.2.7 Factors (p. 16)
  • 1.2.8 Lists (p. 17)
  • 1.2.9 Data frames (p. 18)
  • 1.2.10 Indexing (p. 19)
  • 1.2.11 Conditional selection (p. 20)
  • 1.2.12 Indexing of data frames (p. 21)
  • 1.2.13 subset and transform (p. 22)
  • 1.2.14 Grouped data and data frames (p. 23)
  • 1.2.15 Sorting (p. 24)
  • 1.2.16 Implicit loops (p. 26)
  • 1.3 The graphics subsystem (p. 27)
  • 1.3.1 Plot layout (p. 27)
  • 1.3.2 Building a plot from pieces (p. 29)
  • 1.3.3 Using par (p. 30)
  • 1.3.4 Combining plots (p. 30)
  • 1.4 R programming (p. 32)
  • 1.4.1 Flow control (p. 32)
  • 1.4.2 Classes and generic functions (p. 34)
  • 1.5 Session management (p. 34)
  • 1.5.1 The workspace (p. 34)
  • 1.5.2 Getting help (p. 36)
  • 1.5.3 Packages (p. 37)
  • 1.5.4 Built-in data (p. 37)
  • 1.5.5 attach and detach (p. 38)
  • 1.6 Data entry (p. 39)
  • 1.6.1 Reading from a text file (p. 39)
  • 1.6.2 The data editor (p. 42)
  • 1.6.3 Interfacing to other programs (p. 43)
  • 1.7 Exercises (p. 44)
  • 2 Probability and distributions (p. 45)
  • 2.1 Random sampling (p. 45)
  • 2.2 Probability calculations and combinatorics (p. 46)
  • 2.3 Discrete distributions (p. 47)
  • 2.4 Continuous distributions (p. 48)
  • 2.5 The built-in distributions in R (p. 49)
  • 2.5.1 Densities (p. 49)
  • 2.5.2 Cumulative distribution functions (p. 52)
  • 2.5.3 Quantiles (p. 53)
  • 2.5.4 Random numbers (p. 54)
  • 2.6 Exercises (p. 55)
  • 3 Descriptive statistics and graphics (p. 57)
  • 3.1 Summary statistics for a single group (p. 57)
  • 3.2 Graphical display of distributions (p. 61)
  • 3.2.1 Histograms (p. 61)
  • 3.2.2 Empirical cumulative distribution (p. 63)
  • 3.2.3 Q-Q plots (p. 64)
  • 3.2.4 Boxplots (p. 65)
  • 3.3 Summary statistics by groups (p. 65)
  • 3.4 Graphics for grouped data (p. 67)
  • 3.4.1 Histograms (p. 67)
  • 3.4.2 Parallel boxplots (p. 69)
  • 3.4.3 Stripcharts (p. 70)
  • 3.5 Tables (p. 72)
  • 3.5.1 Generating tables (p. 72)
  • 3.5.2 Marginal tables and relative frequency (p. 74)
  • 3.6 Graphical display of tables (p. 75)
  • 3.6.1 Bar plots (p. 75)
  • 3.6.2 Dotcharts (p. 78)
  • 3.6.3 Pie charts (p. 78)
  • 3.7 Exercises (p. 79)
  • 4 One- and two-sample tests (p. 81)
  • 4.1 One-sample t test (p. 81)
  • 4.2 Wilcoxon signed-rank test (p. 85)
  • 4.3 Two-sample t test (p. 86)
  • 4.4 Comparison of variances (p. 89)
  • 4.5 Two-sample Wilcoxon test (p. 89)
  • 4.6 The paired t test (p. 90)
  • 4.7 The matched-pairs Wilcoxon test (p. 92)
  • 4.8 Exercises (p. 93)
  • 5 Regression and correlation (p. 95)
  • 5.1 Simple linear regression (p. 95)
  • 5.2 Residuals and fitted values (p. 99)
  • 5.3 Prediction and confidence bands (p. 103)
  • 5.4 Correlation (p. 106)
  • 5.4.1 Pearson correlation (p. 106)
  • 5.4.2 Spearman's ¿ (p. 109)
  • 5.4.3 Kendall's ¿ (p. 109)
  • 5.5 Exercises (p. 110)
  • 6 ANOVA and Kruskal-Wallis (p. 111)
  • 6.1 One-way analysis of variance (p. 111)
  • 6.1.1 Pairwise comparisons and multiple testing (p. 115)
  • 6.1.2 Relaxing the variance assumption (p. 117)
  • 6.1.3 Graphical presentation (p. 118)
  • 6.1.4 Bartlett's test (p. 120)
  • 6.2 Kruskal-Wallis test (p. 120)
  • 6.3 Two-way analysis of variance (p. 121)
  • 6.3.1 Graphics for repeated measurements (p. 124)
  • 6.4 The Friedman test (p. 124)
  • 6.5 The ANOVA table in regression analysis (p. 126)
  • 6.6 Exercises (p. 127)
  • 7 Tabular data (p. 129)
  • 7.1 Single proportions (p. 129)
  • 7.2 Two independent proportions (p. 131)
  • 7.3 k proportions, test for trend (p. 133)
  • 7.4 r × c tables (p. 135)
  • 7.5 Exercises (p. 138)
  • 8 Power and the computation of sample size (p. 139)
  • 8.1 The principles of power calculations (p. 139)
  • 8.1.1 The power of one-sample and paired t tests (p. 140)
  • 8.1.2 Power of two-sample t test (p. 142)
  • 8.1.3 Approximate methods (p. 142)
  • 8.1.4 Power of comparisons of proportions (p. 143)
  • 8.2 Two-sample problems (p. 143)
  • 8.3 One-sample problems and paired tests (p. 145)
  • 8.4 Comparison of proportions (p. 146)
  • 8.5 Exercises (p. 146)
  • 9 Multiple regression (p. 149)
  • 9.1 Plotting multivariate data (p. 149)
  • 9.2 Model specification and output (p. 151)
  • 9.3 Model search (p. 154)
  • 9.4 Exercises (p. 157)
  • 10 Linear models (p. 159)
  • 10.1 Polynomial regression (p. 160)
  • 10.2 Regression through the origin (p. 162)
  • 10.3 Design matrices and dummy variables (p. 164)
  • 10.4 Linearity over groups (p. 166)
  • 10.5 Interactions (p. 170)
  • 10.6 Two-way ANOVA with replication (p. 171)
  • 10.7 Analysis of covariance (p. 172)
  • 10.7.1 Graphical description (p. 173)
  • 10.7.2 Comparison of regression lines (p. 177)
  • 10.8 Diagnostics (p. 182)
  • 10.9 Exercises (p. 188)
  • 11 Logistic regression (p. 191)
  • 11.1 Generalized linear models (p. 192)
  • 11.2 Logistic regression on tabular data (p. 193)
  • 11.2.1 The analysis of deviance table (p. 197)
  • 11.2.2 Connection to test for trend (p. 199)
  • 11.3 Logistic regression using raw data (p. 201)
  • 11.4 Prediction (p. 203)
  • 11.5 Model checking (p. 204)
  • 11.6 Exercises (p. 208)
  • 12 Survival analysis (p. 211)
  • 12.1 Essential concepts (p. 211)
  • 12.2 Survival objects (p. 212)
  • 12.3 Kaplan-Meier estimates (p. 213)
  • 12.4 The log-rank test (p. 216)
  • 12.5 The Cox proportional hazards model (p. 218)
  • 12.6 Exercises (p. 220)
  • A Obtaining and installing R (p. 221)
  • B Data sets in the ISwR package (p. 225)
  • C Compendium (p. 247)
  • Index (p. 261)

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