Yosef Cohen, Jeremiah Y. Cohen

Statistics and Data with R

An Applied Approach Through Examples

• Gebundenes Buch

Produktbeschreibung

R, an Open Source software, has become the de facto statistical computing environment. It has an excellent collection of data manipulation and graphics capabilities. It is extensible and comes with a large number of packages that allow statistical analysis at all levels - from simple to advanced - and in numerous fields including Medicine, Genetics, Biology, Environmental Sciences, Geology, Social Sciences and much more. The software is maintained and developed by academicians and professionals and as such, is continuously evolving and up to date. Statistics and Data with R presents an accessible guide to data manipulations, statistical analysis and graphics using R.

Assuming no previous knowledge of statistics or R, the book includes:
_ A comprehensive introduction to the R language.
_ An integrated approach to importing and preparing data for analysis, exploring and analyzing the data, and presenting results.
_ Over 300 examples, including detailed explanationsof the R scripts used throughout.
_ Over 100 moderately large data sets from disciplines ranging from Biology, Ecology and Environmental Science to Medicine, Law, Military and Social Sciences.
_ A parallel discussion of analyses with the normal density, proportions (binomial), counts (Poisson) and bootstrap methods.
_ Two extensive indexes that include references to every R function (and its arguments and packages used in the book and to every introduced concept.

An accompanying Wiki website, http://turtle.gis.umn.edu includes all the scripts and data used in the book. The website also features a solutions manual, providing answers to all of the exercises presented in the book. Visitors are invited to download/upload data and scripts and share comments, suggestions and questions with other visitors. Students, researchers and practitioners will find this to be both a valuable learning resource in statistics and R and an excellent reference book.

Produktdetails

• Verlag: Wiley & Sons
• Artikelnr. des Verlages: 14575805000
• 1. Auflage
• Seitenzahl: 618
• Erscheinungstermin: 3. Dezember 2008
• Englisch
• Abmessung: 249mm x 175mm x 38mm
• Gewicht: 1164g
• ISBN-13: 9780470758052
• ISBN-10: 0470758058
• Artikelnr.: 24928188

Autorenporträt

In the 30 years he has been teaching, Professor Cohen has taught a range of subjects from ecology and computer programming to statistics and data analysis with R. The recipient of numerous research grants, he is also the author and co-author of dozens of articles published in a wide range of journals, and has contributed chapters to a number of edited texts. He has also edited and authored four books.

Inhaltsangabe

Preface. Part I: Data in statistics and R. 1. Basic R. 1.1 Preliminaries.
1.2 Modes. 1.3 Vectors. 1.4 Arithmetic operators and special values. 1.5
Objects. 1.6 Programming. 1.7 Packages. 1.8 Graphics. 1.9 Customizing the
workspace. 1.10 Projects. 1.12 Assignments. 2. Data in statistics and in R.
2.1 Types of data. 2.2 Objects that hold data. 2.3 Data organization. 2.4
Data import, export and connections. 2.5 Data manipulation. 2.6
Manipulating strings. 2.7 Assignments. 3. Presenting data. 3.1 Tables and
the flavors of apply () 3.2 Bar plots. 3.3 Histograms. 3.4 Dot charts. 3.5
Scatter plots. 3.6 Lattice plots. 3.7 Three-dimensional plots and contours.
3.8 Assignments. Part II: Probability, densities and distributions. 4.
Probability and random variables. 4.1 Set theory. 4.2 Trials, events and
experiments. 4.3 Definitions and properties of probability. 4.4 Conditional
probability and independence. 4.5 Algebra with probabilities. 4.6 Random
variables. 4.7 Assignments. 5. Discrete densities and distributions. 5.1
Densities. 5.2 Distribution. 5.3 Properties. 5.4 Expected values. 5.5
Variance and standard deviation. 5.6 The binomial. 5.7 The Poisson. 5.8
Estimating parameters. 5.9 Some useful discrete densities. 5.10
Assignments. 6. Continuous distributions and densities. 6.1 Distributions.
6.2 Densities. 6.3 Properties. 6.4 Expected values. 6.5 Variance and
standard deviation. 6.6 Areas under density curves. 6.7 Inverse
distributions and simulations. 6.8 Some useful continuous densities. 6.9
Assignments. 7. The normal and sampling densities. 7.1 The normal density.
7.2 Applications of the normal. 7.3 Data transformations. 7.4 Random
samples and sampling densities. 7.5 A detour: using R efficiently. 7.6 The
sampling density of the mean. 7.7 The sampling density of proportion. 7.8
The sampling density of intensity. 7.9 The sampling density of variance.
7.10 Bootstrap: arbitrary parameters of arbitrary densities. 7.11
Assignments. Part III: Statistics. 8. Exploratory data analysis. 8.1
Graphical methods. 8.2 Numerical summaries. 8.3 Visual summaries. 8.4
Assignments. 9. Point and interval estimation. 9.1 Point estimation. 9.2
Interval estimation. 9.3 Point and interval estimation for arbitrary
densities. 9.4 Assignments. 10. Single sample hypotheses testing. 10.1 Null
and alternative hypotheses. 10.2 Large sample hypothesis testing. 10.3
Small sample hypotheses testing. 10.4 Arbitrary parameters of arbitrary
densities. 10.5 p-values. 10.6 Assignments. 11. Power and sample size for
single samples. 11.1 Large sample. 11.2 Small samples. 11.3 Power and
sample size for arbitrary densities. 11.4 Assignments. 12. Two samples.
12.1 Large samples. 12.2 Small samples. 12.3 Unknown densities. 12.4
Assignments. 13. Power and sample size for two samples. 13.1 Two means from
normal populations. 13.2 Two proportions. 13.3 Two rates. 13.4 Assignments.
14. Simple linear regression. 14.1 Simple linear models. 14.2 Estimating
regression coefficients. 14.3 The model goodness of fit. 14.4 Hypothesis
testing and confidence intervals. 14.5 Model assumptions. 14.6 Model
diagnostics. 14.7 Power and sample size for the correlation coefficient.
14.8 Assignments. 15. Analysis of variance. 15.1 One-way, fixed-effects
ANOVA. 15.2 Non-parametric one-way ANOVA. 15.3 One-way, random-effects
ANOVA. 15.4 Two-way ANOVA. 15.5 Two-way linear mixed effects models. 15.6
Assignments. 16. Simple logistic regression. 16.1 Simple binomial logistic
regression. 16.2 Fitting and selecting models. 16.3 Assessing goodness of
fit. 16.4 Diagnostics. 16.5 Assignments. 17. Application: the shape of wars
to come. 17.1 A statistical profile of the war in Iraq. 17.2 A statistical
profile of the second Intifada. References. R Index. General Index.