Statistics and Data with R
An Applied Approach Through Examples
(Sprache: Englisch)
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...
Leider schon ausverkauft
versandkostenfrei
Buch (Gebunden)
65.90 €
Produktdetails
Produktinformationen zu „Statistics and Data with R “
Klappentext zu „Statistics and Data with R “
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 explanations of 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.
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 explanations of 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.eduincludes all the scripts and data used in the book. The website also features a solutions manual, providing answers to all of the excercises 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.
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 explanations of 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.eduincludes all the scripts and data used in the book. The website also features a solutions manual, providing answers to all of the excercises 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.
Inhaltsverzeichnis zu „Statistics and Data with R “
PrefacePart 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
... mehr
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
9.1.1 Maximum likelihood estimators
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
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
9.1.1 Maximum likelihood estimators
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
... weniger
Bibliographische Angaben
- Autoren: Yosef Cohen , Jeremiah Y. Cohen
- 2008, 1. Auflage, 618 Seiten, Maße: 16,8 x 24,4 cm, Gebunden, Englisch
- Verlag: Wiley & Sons
- ISBN-10: 0470758058
- ISBN-13: 9780470758052
- Erscheinungsdatum: 13.01.2009
Sprache:
Englisch
Pressezitat
Kommentar zu "Statistics and Data with R"
0 Gebrauchte Artikel zu „Statistics and Data with R“
Zustand | Preis | Porto | Zahlung | Verkäufer | Rating |
---|
Schreiben Sie einen Kommentar zu "Statistics and Data with R".
Kommentar verfassen