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Introductory Statistics With R (Statistics And Computing),Used
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R Is An Open Source Implementation Of The S Language. It Works On Multiple Computing Platforms And Can Be Freely Downloaded. R Is Now In Widespread Use For Teaching At Many Levels As Well As For Practical Data Analysis And Methodological Development. This Book Provides An Elementarylevel Introduction To R, Targeting Both Nonstatistician Scientists In Various Fields And Students Of Statistics. The Main Mode Of Presentation Is Via Code Examples With Liberal Commenting Of The Code And The Output, From The Computational As Well As The Statistical Viewpoint. A Supplementary R Package Can Be Downloaded And Contains The Data Sets. The Statistical Methodology Includes Statistical Standard Distributions, One And Twosample Tests With Continuous Data, Regression Analysis, One And Twoway Analysis Of Variance, Regression Analysis, Analysis Of Tabular Data, And Sample Size Calculations. In Addition, The Last Six Chapters Contain Introductions To Multiple Linear Regression Analysis, Linear Models In General, Logistic Regression, Survival Analysis, Poisson Regression, And Nonlinear Regression. In The Second Edition, The Text And Code Have Been Updated To R Version 2.6.2. The Last Two Methodological Chapters Are New, As Is A Chapter On Advanced Data Handling. The Introductory Chapter Has Been Extended And Reorganized As Two Chapters. Exercises Have Been Revised And Answers Are Now Provided In An Appendix.
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- Q: How many pages does this book have? A: This book has two hundred eighty-eight pages. It provides a comprehensive introduction to statistics using R.
- Q: What is the binding type of this book? A: The binding type is paperback. This makes it lightweight and easy to handle for students.
- Q: What are the dimensions of this book? A: The dimensions are six point two six inches in length, zero point five one inches in width, and nine point two five inches in height. It is compact enough for easy storage.
- Q: Is this book suitable for beginners? A: Yes, this book is suitable for beginners. It targets non-statistician scientists and students of statistics, making it accessible to a wide audience.
- Q: How can I use R as described in this book? A: You can use R by following the code examples provided in the book. Each example is accompanied by detailed commentary to facilitate understanding.
- Q: What level of statistical knowledge is required for this book? A: No prior statistical knowledge is required for this book. It provides an elementary-level introduction to statistics.
- Q: How do I keep this book in good condition? A: Store the book in a cool, dry place away from direct sunlight. This will help preserve its binding and pages.
- Q: Is there any advanced content in this book? A: Yes, the book includes advanced topics in the last six chapters. These cover multiple linear regression, logistic regression, and survival analysis.
- Q: Can I find exercises in this book? A: Yes, the book contains exercises at the end of each chapter. Answers are provided in an appendix for reference.
- Q: What statistical methods are covered in this book? A: The book covers standard distributions, regression analysis, and analysis of variance, among other methods. It provides a broad overview of statistical techniques.
- Q: Is this book appropriate for self-study? A: Yes, this book is appropriate for self-study. The clear examples and explanations make it easy to learn independently.
- Q: Does this book come with supplementary materials? A: Yes, it includes a supplementary R package that contains relevant datasets. This enhances the practical learning experience.
- Q: Who is the author of this book? A: The author is Peter Dalgaard. He is well-known for his contributions to the field of statistics and R programming.
- Q: What is the publication date of this book? A: The second edition has been updated to R version two point six point two. It includes revisions and new content.
- Q: Is this book part of a series? A: Yes, this book is part of the Statistics and Computing series published by Springer. It aims to bridge theoretical statistics and practical applications.