Statistics and Data with R : An Applied Approach Through Examples

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.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.

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. COVER ......Page 1 CONTENTS ......Page 6 PREFACE ......Page 14 PART I DATA IN STATISTICS AND R ......Page 18 1 BASIC R ......Page 19 2 DATA IN STATISTICS AND IN R ......Page 60 3 PRESENTING DATA ......Page 90 PART II PROBABILITY, DENSITIES AND DISTRIBUTIONS ......Page 110 4 PROBABILITY AND RANDOM VARIABLES ......Page 111 5 DISCRETE DENSITIES AND DISTRIBUTIONS ......Page 150 6 CONTINUOUS DISTRIBUTIONS AND DENSITIES ......Page 189 7 THE NORMAL AND SAMPLING DENSITIES ......Page 217 PART III STATISTICS ......Page 261 8 EXPLORATORY DATA ANALYSIS ......Page 262 9 POINT AND INTERVAL ESTIMATION ......Page 294 10 SINGLE SAMPLE HYPOTHESES TESTING ......Page 323 11 POWER AND SAMPLE SIZE FOR SINGLE SAMPLES ......Page 350 12 TWO SAMPLES ......Page 377 13 POWER AND SAMPLE SIZE FOR TWO SAMPLES ......Page 408 14 SAMPLE LINEAR REGRESSION ......Page 424 15 ANALYSIS OF VARIANCE ......Page 469 16 SAMPLE LOGISTIC REGRESSION ......Page 516 17 APPLICATION: THE SHAPE OF WARS TO COME ......Page 546 REFERENCES ......Page 568 R INDEX ......Page 574 GENERAL INDEX ......Page 587 Statistics and Data with R; Contents; Preface; Part I Data in statistics and R; 1 Basic R; 2 Data in statistics and in R; 3 Presenting data; Part II Probability, densities and distributions; 4 Probability and random variables; 5 Discrete densities and distributions; 6 Continuous distributions and densities; 7 The normal and sampling densities; Part III Statistics; 8 Exploratory data analysis; 9 Point and interval estimation; 10 Single sample hypotheses testing; 11 Power and sample size for single samples; 12 Two samples; 13 Power and sample size for two samples; 14 Simple linear regression