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Statistics and Data with R: An Applied Approach Through Examples

Yosef Cohen and Jeremiah Y. Cohen
John Wiley
Publication Date: 
Number of Pages: 
[Reviewed by
Ita Cirovic Donev
, on

Statistics and Data with R is a serious competitor to current books about using R in statistics. It is also a good example of what an introductory applied software book should look like. It is certainly not for an expert or even experienced R user, but it will still fill a gap in many classrooms and be of great help in homework assignments. The table of contents is detailed and quite long. My first thought was that it was too much and that it probably would not be covered in enough detail. Luckily, I was wrong.

The authors really do a great job of balancing the theory, the R code and the presentation. A novice will get an introduction to statistics and data analysis using the software. For a reader already acquainted with R this book will be still than useful, since the presentation does not decrease in substance as we go along. As a result, the book can easily serve as a reference.

As far as the theory goes, results are presented with quite a narrative discussion which serves the purpose of initial understanding of the concepts presented. The most important part, the R code, is presented in a lot of detail, along with appropriate illustrations and figures. Furthermore, there are numerous theoretical and applied examples provided with a step-by-step analysis. Examples are the backbone of the applied chapters, which deal with logistic regression and the like.

This book will most certainly be of great use to upper undergraduate and first-year graduate students or anyone starting to use R for some applied project.

Ita Cirovic Donev holds a Masters degree in statistics from Rice University. Her main research areas are in mathematical finance; more precisely, statistical methods for credit and market risk. Apart from the academic work she does statistical consulting work for financial institutions in the area of risk management.



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.


R Index.

General Index.