Multivariate Statistics for Wildlife and Ecology Research

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Multivariate Statistics for Wildlife and Ecology Research La revue

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With its focus on the practical application of the techniques of multivariate statistics, this book shapes the powerful tools of statistics for the specific needs of ecologists and makes statistics more applicable to their course of study. It gives readers a solid conceptual understanding of the role of multivariate statistics in ecological applications and the relationships among various techniques, while avoiding detailed mathematics and the underlying theory. More importantly, the reader will gain insight into the type of research questions best handled by each technique and the important considerations in applying them. Whether used as a textbook for specialised courses or as a supplement to general statistics texts, the book emphasises those techniques that students of ecology and natural resources most need to understand and employ in their research. While targeted for upper-division and graduate students in wildlife biology, forestry, and ecology, and for professional wildlife scientists and natural resource managers, this book will also be valuable to researchers in any of the biological sciences..


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Applied Multivariate Statistics with R (Statistics for Biology and Health)

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Applied Multivariate Statistics with R (Statistics for Biology and Health) Info

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This book brings the power of multivariate statistics to graduate-level practitioners, making these analytical methods accessible without lengthy mathematical derivations. Using the open source, shareware program R, Professor Zelterman demonstrates the process and outcomes for a wide array of multivariate statistical applications. Chapters cover graphical displays, linear algebra, univariate, bivariate and multivariate normal distributions, factor methods, linear regression, discrimination and classification, clustering, time series models, and additional methods. Zelterman uses practical examples from diverse disciplines to welcome readers from a variety of academic specialties. Those with backgrounds in statistics will learn new methods while they review more familiar topics. Chapters include exercises, real data sets, and R implementations. The data are interesting, real-world topics, particularly from health and biology-related contexts. As an example of the approach, the text examines a sample from the Behavior Risk Factor Surveillance System, discussing both the shortcomings of the data as well as useful analyses. The text avoids theoretical derivations beyond those needed to fully appreciate the methods. Prior experience with R is not necessary.

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Elsevier's Integrated Pharmacology: With STUDENT CONSULT Online Access, 1e

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Elsevier's Integrated Pharmacology: With STUDENT CONSULT Online Access, 1e La revue

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Each title in the new Integrated series focuses on the core knowledge in a specific basic science discipline, while linking that information to related concepts from other disciplines. Case-based questions at the end of each chapter enable you to gauge your mastery of the material, and a color-coded format allows you to quickly find the specific guidance you need. Bonus STUDENT CONSULT access - included with the text - allows you to conveniently access the book's content online · clip content to your handheld device · link to content in other STUDENT CONSULT titles · and more! These concise and user-friendly references provide crucial guidance for the early years of medical training, as well as for exam preparation.

• Includes case-based questions at the end of each chapter
• Features a colour-coded format to facilitate quick reference and promote effective retention
• Offers access to STUDENT CONSULT! At www.studentconsult.com, you'll find the complete text and illustrations of the book online, fully searchable · "Integration Links" to bonus content in other STUDENT CONSULT titles · content clipping for handheld devices · an interactive community center with a wealth of additional resources · and much more!

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Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking, 3rd edition

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Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking, 3rd edition Info

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Thoroughly revised and updated, the third edition of Intuitive Biostatistics: A Nonmathematical Guide to Statistical Thinking retains and refines the core perspectives of the previous editions: a focus on how to interpret statistical results rather than on how to analyze data, minimal use of equations, and a detailed review of assumptions and common mistakes.

With its engaging and conversational tone, this unique book provides a clear introduction to statistics for undergraduate and graduate students in a wide range of fields and also serves as a statistics refresher for working scientists. It is especially useful for those students in health-science related fields who have no background in biostatistics.

CONTENTS

Part A: Introducing Statistics 

 1. Statistics and Probability Are Not Intuitive

 2. The Complexities of Probability

 3. From Sample to Population 

Part B: Confidence Intervals 

 4. Confidence Interval of a Proportion 

 5. Confidence Interval of Survival Data 

 6. Confidence Interval of Counted Data 

Part C: Continuous Variables 

 7. Graphing Continuous Data

 8. Types of Variables 

 9. Quantifying Scatter 

10. The Gaussian Distribution 

11. The Lognormal Distribution and Geometric Mean

12. Confidence Interval of a Mean 

13. The Theory of Confidence Intervals

14. Error Bars 

PART D: P Values and Significance 

15. Introducing P Values 

16. Statistical Significance and Hypothesis Testing

17. Relationship Between Confidence Intervals and Statistical Significance 

18. Interpreting a Result That Is Statistically Significant 

19. Interpreting a Result That Is Not Statistically Significant 

20. Statistical Power

21. Testing for Equivalence or Noninferiority

PART E: Challenges in Statistics 

22. Multiple Comparisons Concepts 

23. The Ubiquity of Multiple Comparison

24. Normality Tests

25. Outliers 

26. Choosing a Sample Size

PART F: Statistical Tests 

27. Comparing Proportions

28. Case-Control Studies

29. Comparing Survival Curves 

30. Comparing Two Means: Unpaired t Test

31. Comparing Two Paired Groups

32. Correlation 

PART G: Fitting Models to Data 

33. Simple Linear Regression

34. Introducing Models 

35. Comparing Models 

36. Nonlinear Regression

37. Multiple Regression 

38. Logistic and Proportional Hazards Regression

PART H The Rest of Statistics 

39. Analysis of Variance 

40. Multiple Comparison Tests After ANOVA 

41. Nonparametric Methods

42. Sensitivity and Specificity and Receiver-Operator Characteristic Curves 

43. Meta-analysis

PART I Putting It All Together 

44. The Key Concepts of Statistics

45. Statistical Traps to Avoid

46. Capstone Example 

47. Review Problems 

48. Answers to Review Problems 

 

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Handbook of Epidemiology

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Handbook of Epidemiology Info

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This 5-volume reference covers the entire field of epidemiology, from statistical methods and study design, to specialized areas such as molecular epidemiology, and applications in clinical medicine and health services research. This updated edition of the Handbook of  Epidemiology adds 20 new chapters on: History of Epidemiological Methods and Concepts, Cluster Randomized Trials, Internet-Based Epidemiology, Misclassification, Sensitivity Analysis and Bias Analysis,  Emergency and Disaster Health Surveillance, Statistical Inference, Data Management in Epidemiology, Bayesian Methods in Epidemiology, Generalized Estimating Equations, Directed Acyclic Graphs, Life Course Epidemiology, Physical Activity Epidemiology, Radiation Epidemiology, Epidemiology of  Obesity, Epidemiology of Respiratory Allergies and Asthma, Epidemiology of  Dental Diseases, Epidemiology of Digestive Diseases, Epidemiology of Psychiatric Disorders, Epidemiology of  Diabetes.

All other chapters are extensively revised from  the 1st edition. This is a reference for epidemiological researchers and graduate students in public health.

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Langman's Medical Embryology

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Langman's Medical Embryology Description

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Offering a strong clinical focus and exceptional full color artwork and images, Langman's Medical Embryology, 13e helps medical, nursing, and health professions students develop a basic understanding of embryology and its clinical relevance. Concise chapter summaries, captivating clinical correlates boxes, clinical problems, and a clear, concise writing style make the subject matter accessible to students and relevant to instructors.

The new edition features over 100 new and updated illustrations, additional clinical images and photos of early embryologic development, an expanded chapter on the cardiovascular system, and the latest information on molecular biology and genetics..


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Statistics at Square One

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Statistics at Square One Info

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The new edition of this international bestseller continues to throw light on the world of statistics for health care professionals and medical students.

Revised throughout, the 11th edition features new material in the areas of

• relative risk, absolute risk and   numbers needed to treat
• diagnostic tests, sensitivity, specificity, ROC curves
• free statistical software

The popular self-testing exercises at the end of every chapter are strengthened by the addition of new sections on reading and reporting statistics and formula appreciation.

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Biostatistics for the Health Sciences

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Biostatistics for the Health Sciences Description

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This book provides a solid foundation in introductory biostatistics with up-to-date methods, lucid explanations, and a modern approach. Explains commonly used biostatistical methods, such as odds and risk ratios, and Fisher's exact test, in a clear and thorough manner. Introduces equivalence testing in a variety of research settings. Presents nonparametric methods in a modern light, couched in the broader context of permutation-based methods. Provides real-world data with case studies consisting of synopses of published research. Provides step-by-step solutions to exercises, along with pertinent equations used in obtaining the solution and page numbers of relevant discussions. For health science students and professionals who need to increase their understanding of biostatistics..


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Handbook of Functional MRI Data Analysis

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Handbook of Functional MRI Data Analysis Info

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Functional magnetic resonance imaging (fMRI) has become the most popular method for imaging brain function. Handbook for Functional MRI Data Analysis provides a comprehensive and practical introduction to the methods used for fMRI data analysis. Using minimal jargon, this book explains the concepts behind processing fMRI data, focusing on the techniques that are most commonly used in the field. This book provides background about the methods employed by common data analysis packages including FSL, SPM, and AFNI. Some of the newest cutting-edge techniques, including pattern classification analysis, connectivity modeling, and resting state network analysis, are also discussed. Readers of this book, whether newcomers to the field or experienced researchers, will obtain a deep and effective knowledge of how to employ fMRI analysis to ask scientific questions and become more sophisticated users of fMRI analysis software..


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Persuader (Jack Reacher)

An elite ex-military cop who left the service years ago, he’s moved from place to place…without family…without possessions…without commitments.

And without fear. Which is good, because trouble—big, violent, complicated trouble—finds Reacher wherever he goes. And when trouble finds him, Reacher does not quit, not once…not ever.

But some unfinished business has now found Reacher. And Reacher is a man who hates unfinished business.

Ten years ago, a key investigation went sour and someone got away with murder. Now a chance encounter brings it all back. Now Reacher sees his one last shot. Some would call it vengeance. Some would call it redemption. Reacher would call it…justice.

An Introduction to Experimental Design and Statistics for Biology

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An Introduction to Experimental Design and Statistics for Biology Description

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This illustrated textbook for biologists provides a refreshingly clear and authoritative introduction to the key ideas of sampling, experimental design, and statistical analysis. The author presents statistical concepts through common sense, non-mathematical explanations and diagrams. These are followed by the relevant formulae and illustrated by worked examples. The examples are drawn from all areas of biology, from biochemistry to ecology and from cell to animal biology. The book provides everything required in an introductory statistics course for biology undergraduates, and it is also useful for more specialized undergraduate courses in ecology, botany, and zoology.

This illustrated textbook for biologists provides a refreshingly clear and authoritative introduction to the key ideas of sampling, experimental design, and statistical analysis. The author presents statistical concepts through common sense, non-mathematical explanations and diagrams. These are followed by the relevant formulae and illustrated by worked examples. The examples are drawn from all areas of biology, from biochemistry to ecology and from cell to animal biology. The book provides everything required in an introductory statistics course for biology undergraduates, and it is also useful for more specialized undergraduate courses in ecology, botany, and zoology.

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Logistic Regression: A Self-Learning Text (Statistics for Biology and Health)

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Logistic Regression: A Self-Learning Text (Statistics for Biology and Health) Info

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This is the second edition of this text on logistic regression methods. As in the first edition, each chapter contains a presentation of its topic in "lecture-book" format together with objectives, an outline, key formulae, practice exercises, and a test. The "lecture- book" has a sequence of illustrations and formulae in the left column of each page and a script (i.e., text) in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that highlight the main points, formulae, or examples being presented.

This second edition includes five new chapters and an appendix. The new chapters are: Chapter 9. Polytomous Logistic Regression; Chapter 10. Ordinal Logistic Regression; Chapter 11. Logistic Regression for Correlated Data: GEE; Chapter 12. GEE Examples; Chapter 13. Other Approaches for Analysis of Correlated Data.

Chapters 9 and 10 extend logistic regression to response variables that have more than two categories. Chapters 11-13 extend logistic regression to generalized estimating equations (GEE) and other methods for analyzing correlated response data. The appendix "Computer Programs for Logistic Regression" provides descriptions and examples of computer programs for carrying out the variety of logistic regression procedures described in the main text. The software packages considered are SAS® Version 8.0, SPSS® Version 10.0 and STATA Version 7.0.

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How Life Begins: The Science of Life in the Womb--Newly Revised.

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How Life Begins: The Science of Life in the Womb--Newly Revised. Info

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From the moment of fertilization until the day of birth, the extraordinary life of a fetus is chronicled in this awe-inspiring volume drawn from the latest in pre-natal research. This newly revised and updated digital edition offers a month-by-month guide to what a developing baby is seeing, hearing, dreaming about, and how it is accomplishing the miracle of preparing to be human. Readers will discover:

How a fetus's eye movements work to "boot up" the computer that is its brain
How the hands and feet commit programmed suicide to create fingers and
What role genetics plays in determining an infant's sexual identity and personality
How the baby becomes the choreographer of the mother's labor, and the director of his or her own birth

Interviews with leading researchers combine with truly remarkable photography to make How Life Begins an unforgettable journey marking the passage from single cell to bright-eyed baby..


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Emma's Secret: A Novel (Finding Emma Series)

I was very excited when I read the short description of Emma's Secret. I thought it would be about finding and helping this Emma character get back her life. However, it was hardly about Emma at all. Throughout the entire book we still only know very little about Emma, except for the fact that everyone was affected by Emma's kidnapping and new look at life. We didn't get to really see her progress or anything.

The point of view in the story changed almost every other page. Starting out with Dottie's journal entries, it almost immediately changes into the main character Megan, Emma's mom. Then it continues to change to Jack, Emma's 'kidnapper', and then Megan's husband Peter. The point of view could change up to three times in one chapter, just jumping all over the place. Dottie's journal could have been easily left out, as it seemed to never fit in. Author Steena Holmes seemed to give everyone and their mother a journal to write in, as if the entire town was into it.

Jack's story line was the one I enjoyed the most because it was the only one that seemed to have a point. He showed you his life and what he was doing and thinking. You felt so bad for him because he had lost everything he cared for and was left alone. He helped fill in little details about Emma's life away from the home, but never seemed to provide any unneeded information or wasted words, which everyone else did. Megan's story line was the most boring and frustrating of them all. Her entire character worried over stupid things and had trust issues. Her best friend Laurie, was keeping this 'big huge' secret from her, that made her upset. Her husband, Peter, also was keeping some big secret involving his hot and sexy secretary that I found to be very clichéd. Even if the book is called Emma's Secret, Emma is the only one who doesn't have one! I struggled my way through the book only to be disappointed by these 'secrets.' Once finding out that these disappointing secrets were the highlight of the entire book, I honestly didn't want to continue reading. These secrets were so pointless and had absolutely nothing to do with the plot! The entire book should have concentrated on helping Emma.

This story was jumpy, boring, and overall terrible. Whatever plot the author was trying to establish failed, and there were so many terrible clichéd moments that it made me wonder if Steena Holmes even tried to be creative with the story. I am glad that I won't have to read the book again

The Moonlit Garden

Corina Bomann's The Moonlit Garden instantly intrigued me when I read the book description so I knew I had to read it. I absolutely love reading historical fiction and I studied music for a long time growing up so I couldn't wait to start reading this book! As soon as I started it, I had a hard time putting it down and I tore through it within less than two days. I was also interested to see that The Moonlit Garden had originally been written in German and then translated into English by Alison Layland. In my experience, not all translated novels read as smoothly and beautifully as this one and, at just over 400 pages, The Moonlit Garden is an exquisitely-crafted novel that will appeal to historical fiction fans alike.

Lilly Kaiser is in her late thirties and owns an antique shop in Berlin, Germany. She is working in her shop on a seemingly normal day when a mysterious elderly gentleman walks in to show her a violin, claiming it belongs to her. Before she can ask him any more questions, he disappears into thin air and Lilly is left to figure out the mystery behind the beautiful antique violin with the symbol of a rose on the back and a piece of sheet music tucked into the violin's case. Suddenly, Lilly's life has been altered and she has been given a new purpose of tracing down the history of the violin and why would have received it. Before she knows it, she is on a whirlwind journey over multiple continents to discover what happened so long ago to the violin's original owner.

Beautifully written and flawlessly translated, The Moonlit Garden is a wonderful historical fiction novel that kept me thoroughly engaged over the past two days. I stayed up way too late and hardly did anything else other than read this book and I loved every single minute of it! I loved this novel so much that it will stay with me for a long time and I would definitely be interested in reading another of Corina Bomann's novels. I highly recommend The Moonlit Garden for anyone who enjoys historical fiction!

Illustrated Dental Embryology, Histology, and Anatomy, 2e (Illustrated Colour Text)

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Illustrated Dental Embryology, Histology, and Anatomy, 2e (Illustrated Colour Text) La revue

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Offers concise and comprehensive coverage of orofacial embryology, oral histology, and dental anatomy while also providing a brief introduction to dental structures. The clearly written text highlights both basic science and clinical applications to give the reader well-rounded coverage. Features of the book include: clinical applications for diverse topics that range from dental implants to periodontal considerations, an outstanding illustration program, microscopic photographs, and clinical photographs that clarify essential concepts, a review of anatomical nomenclature, pronunciations, a glossary, chapter overviews, and learning objectives.

• Each chapter features microscopic photographs, as well as clinical photographs to clarify essential concepts.
• Logical organization makes it easier for students to move through the text and to be successful at comprehending the information.
• Tables are used throughout the book to present and compare similar information and help students recognize similarities and differences among related topics.
• Boxed information throughout highlights the clinical considerations of different topics.
• An overview at the beginning of each chapter lets the students easily see what key information can be found in each chapter.
• Standard student objectives are outlined at the beginning of each chapter to clearly identify the information the student is expected to absorb from studying the chapter.
• A glossary at the back of the book contains the terms listed in the key terms of the chapters with definitions.

• Now in full-color.
• Incorporates case studies put forth by the National Dental Hygiene Board Examination.
• Sixteen chapters include new or updated information, including new information on stem cells in dentistry (chapter 7) and updated information on tooth color, staining, and bleaching (chapter 12).
• An expanded workbook emphasizes problem-based learning along with a curriculum development and delivery system that recognizes the need for students to develop problem-solving skills and acquire necessary knowledge and skills.

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Designing Clinical Research

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Designing Clinical Research Description

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Designing Clinical Research sets the standard for providing a practical guide to planning, tabulating, formulating, and implementing clinical research, with an easy-to-read, uncomplicated presentation. This edition incorporates current research methodology—including molecular and genetic clinical research—and offers an updated syllabus for conducting a clinical research workshop.

Emphasis is on common sense as the main ingredient of good science. The book explains how to choose well-focused research questions and details the steps through all the elements of study design, data collection, quality assurance, and basic grant-writing. All chapters have been thoroughly revised, updated, and made more user-friendly.

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Essentials of Human Embryology, 1e

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Essentials of Human Embryology, 1e La revue

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This is the condensed version of Human Embryology 2nd Edition by William J. Larsen. This consice textbook provides detailed coverage of the concepts and principles that underlie human development. The book provides a view of exciting applications that are currently in use or are on the horizon..


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Modeling Count Data

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Modeling Count Data Info

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This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of modeling count data, including a thorough presentation of the Poisson model. It then works up to an analysis of the problem of overdispersion and of the negative binomial model, and finally to the many variations that can be made to the base count models. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in health, ecology, econometrics, transportation, and other fields..


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Patten's Foundations of Embryology

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Patten's Foundations of Embryology Description

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Designed for junior, senior and graduate courses in embryology, developmental biology and medical embryology, this book presents the story of vertebrate embryonic development. A chapter on fertilization, featuring a comparison of classical invertebrate systems, includes new data on mammalian fertilization. This edition indicates where contemporary research has clarified understanding of embryological processes..


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Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating (Statistics for Biology and Health)

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Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating (Statistics for Biology and Health) La revue

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"This book aims to provide insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but these innovations are insufficiently applied in medical research. Old-fashioned, data hungry methods are often used in data sets of limited size, validation of predictions is not done or only in a simplistic way, and updating of already available models is not considered. A sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. Clinical Prediction Models presents a practical checklist with seven steps that need to be considered for development of a valid prediction model. These include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and clinical usefulness; internal validation; and presentation format. The steps are illustrated with many small case studies and R computer code, with data sets made available in the public domain [http://www.clinicalpredictionmodels.org/]. The book further focuses on generalizability of prediction models, including patterns of invalidity that may be encountered in new settings, approaches to modifying and extending a model, and comparisons of centers after case-mix adjustment by a prediction model. The text is primarily intended for epidemiologists and applied biostatisticians. It can be used as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. It is beneficial if readers are familiar with common statistical models in medicine: linear regression, logistic regression, and Cox regression. The book is practical in nature. But it also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. In this era of evidence-based medicine, randomized clinical trials are the basis for assessment of treatment efficacy. Prediction models are key to individualizing diagnostic and treatment decision-making. ""This book aims to provide insight and practical illustrations on how modern statistical concepts and regression methods can be applied in medical prediction problems, including diagnostic and prognostic outcomes. Many advances have been made in statistical approaches towards outcome prediction, but these innovations are insufficiently applied in medical research. Old-fashioned, data hungry methods are often used in data sets of limited size, validation of predictions is not done or only in a simplistic way, and updating of already available models is not considered. A sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. Clinical Prediction Models presents a practical checklist with seven steps that need to be considered for development of a valid prediction model. These include preliminary considerations such as dealing with missing values; coding of predictors; selection of main effects and interactions for a multivariable model; estimation of model parameters with shrinkage methods and incorporation of external data; evaluation of performance and clinical usefulness; internal validation; and presentation format. The steps are illustrated with many small case studies and R computer code, with data sets made available in the public domain [http://www.clinicalpredictionmodels.org/]. The book further focuses on generalizability of prediction models, including patterns of invalidity that may be encountered in new settings, approaches to modifying and extending a model, and comparisons of centers after case-mix adjustment by a prediction model. The text is primarily intended for epidemiologists and applied biostatisticians. It can be used as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. It is beneficial if readers are familiar with common statistical models in medicine: linear regression, logistic regression, and Cox regression. The book is practical in nature. But it also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. In this era of evidence-based medicine, randomized clinical trials are the basis for assessment of treatment efficacy. Prediction models are key to individualizing diagnostic and treatment decision-making. ".


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Modeling Count Data

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Modeling Count Data Info

.

This entry-level text offers clear and concise guidelines on how to select, construct, interpret and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of linear regression and works up to an analysis of the Poisson and negative binomial models, and to the problem of overdispersion. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in public health, ecology, econometrics, transportation, and other related fields..


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Primer to Analysis of Genomic Data Using R (Use R!)

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Primer to Analysis of Genomic Data Using R (Use R!) La revue

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Through this book, researchers and students will learn to use R for analysis of large-scale genomic data and how to create routines to automate analytical steps. The philosophy behind the book is to start with real world raw datasets and perform all the analytical steps needed to reach final results. Though theory plays an important role, this is a practical book for graduate and undergraduate courses in bioinformatics and genomic analysis or for use in lab sessions. How to handle and manage high-throughput genomic data, create automated workflows and speed up analyses in R is also taught. A wide range of R packages useful for working with genomic data are illustrated with practical examples.

The key topics covered are association studies, genomic prediction, estimation of population genetic parameters and diversity, gene expression analysis, functional annotation of results using publically available databases and how to work efficiently in R with large genomic datasets. Important principles are demonstrated and illustrated through engaging examples which invite the reader to work with the provided datasets. Some methods that are discussed in this volume include: signatures of selection, population parameters (LD, FST, FIS, etc); use of a genomic relationship matrix for population diversity studies; use of SNP data for parentage testing; snpBLUP and gBLUP for genomic prediction. Step-by-step, all the R code required for a genome-wide association study is shown: starting from raw SNP data, how to build databases to handle and manage the data, quality control and filtering measures, association testing and evaluation of results, through to identification and functional annotation of candidate genes. Similarly, gene expression analyses are shown using microarray and RNAseq data.

At a time when genomic data is decidedly big, the skills from this book are critical. In recent years R has become the de facto< tool for analysis of gene expression data, in addition to its prominent role in analysis of genomic data. Benefits to using R include the integrated development environment for analysis, flexibility and control of the analytic workflow. Included topics are core components of advanced undergraduate and graduate classes in bioinformatics, genomics and statistical genetics. This book is also designed to be used by students in computer science and statistics who want to learn the practical aspects of genomic analysis without delving into algorithmic details. The datasets used throughout the book may be downloaded from the publisher’s website.

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The Essence of Multivariate Thinking: Basic Themes and Methods (Multivariate Applications Series)

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The Essence of Multivariate Thinking: Basic Themes and Methods (Multivariate Applications Series) Description

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By focusing on underlying themes, this book helps readers better understand the connections between multivariate methods. For each method the author highlights: the similarities and differences between the methods, when they are used and the questions they address, the key assumptions and equations, and how to interpret the results. The concepts take center stage while formulas are kept to a minimum. Examples using the same data set give readers continuity so they can more easily apply the concepts. Each method is also accompanied by a worked out example, SPSS and SAS input, and an example of how to write up the results. EQS code is used for the book’s SEM applications.

This extensively revised edition features:

• New SEM chapters including an introduction (ch.10), path analysis (ch.11), confirmatory factor analysis (ch.12), and latent variable modeling (ch.13) the last three with an EQS application.
• A new chapter on multilevel modeling (ch. 8) that is now used more frequently in the social sciences.
• More emphasis on significance tests, effect sizes, and confidence intervals to encourage readers to adopt a thorough approach to assessing the magnitude of their findings.
• A new data set that explores the work environment.
• More discussion about the basic assumptions and equations for each method for a more accessible approach.
• New examples that help clarify the distinctions between methods.
• A new website at https://sites.google.com/site/multivariatesecondedition/ that features the datasets for all of the examples in the book for use in both SPSS and SAS and in EQS for the SEM chapters.

The first two chapters review the core themes that run through most multivariate methods. The author shows how understanding multivariate methods is much more achievable when we notice the themes that underlie these statistical techniques. This multiple level approach also provides greater reliability and validity in our research. After providing insight into the core themes, the author illustrates them as they apply to the most popular multivariate methods used in the social, and behavioral sciences. First, two intermediate methods are explored – multiple regression and analysis of covariance. Next the multivariate grouping variable methods of multivariate analysis of variance, discriminant function analysis, and logistic regression are explored. Next the themes are applied to multivariate modeling methods including multilevel modeling, path analysis, confirmatory factor analysis, and latent variable models that include exploratory structural methods of principal component and factor analysis. The book concludes with a summary of the common themes and how they pertain to each method discussed in this book.

Intended for advanced undergraduate and/or graduate courses in multivariate statistics taught in psychology, education, human development, business, nursing, and other social and life sciences, researchers also appreciate this book‘s applied approach. Knowledge of basic statistics, research methods, basic algebra, and finite mathematics is recommended.

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Colour Atlas of Life Before Birth: Normal Fetal Development

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Colour Atlas of Life Before Birth: Normal Fetal Development La revue

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Detailed, magnificently illustrated photographic color illustrations of complete embryos and fetuses, close-up external and dissection views, cross sections, casts, and echograms, with particular attention in detail to organ and organ-system development. A valuable supplement to reference texts on gross anatomy..


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Your Baby's First Year Week By Week

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Your Baby's First Year Week By Week Description

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Finally, the long-awaited new book from the authors of the million-copy bestseller Your Pregnancy Week by Week.Learn about your baby's weekly development and how to car for baby's body and mind. In an easy-to-read format, each week includes How Big Is Your Baby? Basic Baby Care Milestones (Social, Emotional, Physical, Intellectual) Toys and Play This upbeat book emphasizes well-baby care and how you can encourage your baby's social, emotional, physical and intellectual development. Charts and illustrations appear throughout the book. You will also find easy-to-read two-page spreads that highlight issues of special interest.Topics Covered Baby gear-carriers, highchairs, swings, cribs Baby massage Baby's personal style Bonding Common medical problems Diapering Dressing baby First foods How to talk to baby Playing with baby to develop cognitive, social and motor skills Sleeping habits

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Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data (Cambridge Medicine (Hardcover))

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Statistical Modeling for Biomedical Researchers: A Simple Introduction to the Analysis of Complex Data (Cambridge Medicine (Hardcover)) Description

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For biomedical researchers, the new edition of this standard text guides readers in the selection and use of advanced statistical methods and the presentation of results to clinical colleagues. It assumes no knowledge of mathematics beyond high school level and is accessible to anyone with an introductory background in statistics. The Stata statistical software package is used to perform the analyses, in this edition employing the intuitive version 10.
Topics covered include linear, logistic and Poisson regression, survival analysis, fixed-effects analysis of variance, and repeated-measure analysis of variance. Restricted cubic splines are used to model non-linear relationships. Each method is introduced in its simplest form and then extended to cover more complex situations. An appendix will help the reader select the most appropriate statistical methods for their data. The text makes extensive use of real data sets available online through Vanderbilt University..


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