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The Practice of Statistics in the Life Sciences: Free Resources and Essential Skills
Are you a life sciences student struggling to grasp the crucial role of statistics in your field? Or perhaps a researcher seeking freely available resources to enhance your data analysis skills? This comprehensive guide dives deep into the world of statistics within the life sciences, exploring readily accessible resources and highlighting the essential statistical techniques you need to master. We'll navigate the sometimes daunting landscape of statistical analysis, revealing how to leverage free tools and online materials to bolster your understanding and advance your research.
Why Statistics is Crucial in the Life Sciences
The life sciences, encompassing fields like biology, medicine, ecology, and agriculture, are inherently data-rich. From gene expression analysis to clinical trial results and ecological population studies, data forms the bedrock of scientific discovery. However, raw data alone tells only part of the story. To extract meaningful insights, to identify patterns, and to draw valid conclusions, a solid grasp of statistical methods is absolutely essential. Without statistical analysis, research findings can be misrepresented, leading to flawed conclusions and potentially hindering advancements in these vital fields.
Free Online Resources for Learning Statistics in the Life Sciences
Fortunately, a wealth of free resources is available to help you develop your statistical expertise. These resources cater to different learning styles and levels of statistical knowledge, from introductory courses to advanced techniques.
#### 1. Open Educational Resources (OER):
Many universities and organizations offer free online courses and textbooks focusing on statistics for the life sciences. These OER often include video lectures, practice problems, and downloadable materials, offering a flexible and accessible learning experience. A quick search on platforms like OpenStax, MIT OpenCourseware, and Coursera (for some free courses) will reveal a treasure trove of options. Look specifically for courses mentioning "biostatistics," "statistical methods in biology," or "data analysis for life sciences."
#### 2. Online Statistical Software:
Several powerful statistical software packages offer free versions or free trials, allowing you to gain practical experience analyzing real-world datasets. R, a popular open-source programming language, provides a vast library of statistical packages and is particularly well-suited for life science applications. Other options include Gnumeric (a free spreadsheet program with basic statistical functions) and smaller, more specialized packages depending on your specific needs. Remember to explore online tutorials and documentation for these software packages, as they can be complex to master initially.
#### 3. YouTube Channels and Blogs:
Numerous YouTube channels and blogs offer free tutorials and explanations of statistical concepts and techniques. These resources can provide supplementary explanations or offer different perspectives on complex statistical ideas. Search for keywords like "biostatistics tutorial," "R for biologists," or "statistical analysis in ecology" to find relevant content. However, always critically evaluate the source's credibility and ensure the information aligns with established statistical principles.
Essential Statistical Techniques for Life Scientists
While the specific statistical methods used will vary depending on the research question and data type, some techniques are fundamental for life scientists.
#### 1. Descriptive Statistics: This foundational area involves summarizing and presenting data using measures such as mean, median, standard deviation, and variance. Understanding these measures is crucial for interpreting data and identifying potential outliers or biases.
#### 2. Hypothesis Testing: This involves using statistical tests to determine whether observed differences or relationships in data are statistically significant or likely due to chance. Common tests include t-tests, ANOVA (Analysis of Variance), and chi-square tests.
#### 3. Regression Analysis: This is used to model the relationship between variables, allowing researchers to predict outcomes based on the values of other variables. Linear regression is a common technique, but other forms, such as logistic regression (for binary outcomes), are also frequently employed.
#### 4. Analysis of Variance (ANOVA): This powerful technique allows researchers to compare the means of three or more groups, determining whether there are significant differences between them.
#### 5. Non-parametric Methods: These are used when assumptions of normality or equal variance are violated in the data. Examples include the Mann-Whitney U test and the Kruskal-Wallis test.
Mastering Statistics: A Continuous Process
Becoming proficient in statistics is a journey, not a destination. Consistent practice, careful study of the underlying principles, and applying your knowledge to real datasets are essential for developing your expertise. Don't be afraid to seek help from colleagues, mentors, or online communities when you encounter difficulties. The free resources available are invaluable tools, but active learning and consistent effort are key to unlocking their potential.
Conclusion
The practice of statistics in the life sciences is no longer optional; it's indispensable. By leveraging the free resources and mastering the essential statistical techniques outlined above, you can significantly enhance your research capabilities and contribute meaningfully to the advancements within your field. The availability of free online tools and resources makes this knowledge accessible to everyone, regardless of background or budget. Embrace these opportunities, and unlock the power of data to drive your scientific endeavors.
FAQs
1. Where can I find free datasets for practicing statistical analysis? Many government agencies and research institutions offer publicly available datasets. Websites like Kaggle and UCI Machine Learning Repository are also excellent sources of datasets suitable for practicing statistical techniques.
2. What is the difference between parametric and non-parametric tests? Parametric tests assume the data follows a specific distribution (often normal), while non-parametric tests make fewer assumptions about the data distribution. The choice of test depends on the nature of your data.
3. How can I improve my interpretation of statistical results? Practice is key! Work through numerous examples, focus on understanding the underlying concepts, and critically evaluate the meaning of p-values and confidence intervals within the context of your research question.
4. Are there any free statistical consulting services available? Some universities and research organizations may offer limited free statistical consulting to students or researchers. Check with your institution or search for relevant online forums and communities.
5. What are some good books on biostatistics available for free online? While complete textbooks are rarely entirely free, searching for "biostatistics open textbook" or exploring OER platforms may yield chapters or sections of relevant textbooks available for free download or online access. Remember to check licenses before using any material for academic purposes.
| the practice of statistics in the life sciences free: The Practice of Statistics in the Life Sciences Brigitte Baldi, David S. Moore, 2013-12-27 This remarkably engaging textbook is the perfect learning resource for undergraduate and postgraduate biology students studying statistics and data analysis. Part of the best-selling Moore family of statistics books, it covers essential statistical topics with examples and exercises drawn from across the field of life sciences, including disciplines such as nursing, public health, and allied health. Based on David Moore’s classic The Basic Practice of Statistics, this textbook applies the bestseller’s signature emphasis on statistical thinking, real data and what statisticians actually do, to the world of life sciences, helping engage students and underlining how statistics can directly apply to the projects they’re working on. The new edition includes new and updated exercises, examples, and samples of real data, as well as an expanded range of media tools for students and instructors. This textbook is also available on LaunchPad. |
| the practice of statistics in the life sciences free: Practice of Statistics in the Life Sciences Brigitte Baldi, David S. Moore, 2018-03-08 This remarkably engaging textbook is the perfect learning resource for undergraduate and postgraduate biology students studying statistics and data analysis. Part of the best-selling Moore family of statistics books, it covers essential statistical topics with examples and exercises drawn from across the field of life sciences, including disciplines such as nursing, public health, and allied health. Based on David Moore’s classic The Basic Practice of Statistics, this textbook applies the bestseller’s signature emphasis on statistical thinking to the world of life sciences, helping engage students and underlining how statistics can directly apply to the projects they’re working on. This textbook will be available on SaplingPlus, a highly-intelligent online teaching and learning tool which will be available for statistics in Autumn 2018. |
| the practice of statistics in the life sciences free: Data Analysis for the Life Sciences with R Rafael A. Irizarry, Michael I. Love, 2016-10-04 This book covers several of the statistical concepts and data analytic skills needed to succeed in data-driven life science research. The authors proceed from relatively basic concepts related to computed p-values to advanced topics related to analyzing highthroughput data. They include the R code that performs this analysis and connect the lines of code to the statistical and mathematical concepts explained. |
| the practice of statistics in the life sciences free: All of Statistics Larry Wasserman, 2013-12-11 Taken literally, the title All of Statistics is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data. |
| the practice of statistics in the life sciences free: Modern Statistics for the Life Sciences Alan Grafen, Rosie Hails, 2002-03-21 Model formulae represent a powerful methodology for describing, discussing, understanding, and performing that large part of statistical tests known as linear statistics. The book aims to put this methodology firmly within the grasp of undergraduates. |
| the practice of statistics in the life sciences free: Introductory Statistics 2e Barbara Illowsky, Susan Dean, 2023-12-13 Introductory Statistics 2e provides an engaging, practical, and thorough overview of the core concepts and skills taught in most one-semester statistics courses. The text focuses on diverse applications from a variety of fields and societal contexts, including business, healthcare, sciences, sociology, political science, computing, and several others. The material supports students with conceptual narratives, detailed step-by-step examples, and a wealth of illustrations, as well as collaborative exercises, technology integration problems, and statistics labs. The text assumes some knowledge of intermediate algebra, and includes thousands of problems and exercises that offer instructors and students ample opportunity to explore and reinforce useful statistical skills. This is an adaptation of Introductory Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License. |
| the practice of statistics in the life sciences free: Biometry Robert R. Sokal, F. James Rohlf, 1995 Offers students with little background in statistical analysis an introduction to a variety of statistical concepts and methods. In addition to the incorporation of computer calculation, this new edition expands on a number of important topics, including the revised Kolmogrov-Smirnov test. |
| the practice of statistics in the life sciences free: Data Journeys in the Sciences Sabina Leonelli, Niccolò Tempini, 2020-06-29 This groundbreaking, open access volume analyses and compares data practices across several fields through the analysis of specific cases of data journeys. It brings together leading scholars in the philosophy, history and social studies of science to achieve two goals: tracking the travel of data across different spaces, times and domains of research practice; and documenting how such journeys affect the use of data as evidence and the knowledge being produced. The volume captures the opportunities, challenges and concerns involved in making data move from the sites in which they are originally produced to sites where they can be integrated with other data, analysed and re-used for a variety of purposes. The in-depth study of data journeys provides the necessary ground to examine disciplinary, geographical and historical differences and similarities in data management, processing and interpretation, thus identifying the key conditions of possibility for the widespread data sharing associated with Big and Open Data. The chapters are ordered in sections that broadly correspond to different stages of the journeys of data, from their generation to the legitimisation of their use for specific purposes. Additionally, the preface to the volume provides a variety of alternative “roadmaps” aimed to serve the different interests and entry points of readers; and the introduction provides a substantive overview of what data journeys can teach about the methods and epistemology of research. |
| the practice of statistics in the life sciences free: Simultaneous Statistical Inference Thorsten Dickhaus, 2014-01-23 This monograph will provide an in-depth mathematical treatment of modern multiple test procedures controlling the false discovery rate (FDR) and related error measures, particularly addressing applications to fields such as genetics, proteomics, neuroscience and general biology. The book will also include a detailed description how to implement these methods in practice. Moreover new developments focusing on non-standard assumptions are also included, especially multiple tests for discrete data. The book primarily addresses researchers and practitioners but will also be beneficial for graduate students. |
| the practice of statistics in the life sciences free: Learning Statistics with R Daniel Navarro, 2013-01-13 Learning Statistics with R covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com |
| the practice of statistics in the life sciences free: Introductory Business Statistics 2e Alexander Holmes, Barbara Illowsky, Susan Dean, 2023-12-13 Introductory Business Statistics 2e aligns with the topics and objectives of the typical one-semester statistics course for business, economics, and related majors. The text provides detailed and supportive explanations and extensive step-by-step walkthroughs. The author places a significant emphasis on the development and practical application of formulas so that students have a deeper understanding of their interpretation and application of data. Problems and exercises are largely centered on business topics, though other applications are provided in order to increase relevance and showcase the critical role of statistics in a number of fields and real-world contexts. The second edition retains the organization of the original text. Based on extensive feedback from adopters and students, the revision focused on improving currency and relevance, particularly in examples and problems. This is an adaptation of Introductory Business Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License. |
| the practice of statistics in the life sciences free: Statistics for the Life Sciences Myra L. Samuels, Jeffrey A. Witmer, Andrew Schaffner, 2012 Statistics for the Life Sciences, Fourth Edition, is the perfect book for introductory statistics classes, covering the key concepts of statistics as applied to the life sciences, while incorporating the tools and themes of modern data analysis. This text uses an abundance of real data in the exercises and examples to minimize computation, so that students can focus on the statistical concepts and issues, not the mathematics. Basic algebra is assumed as a prerequisite. ¿ This latest edition is also available as an enhanced Pearson eText. This exciting new version features an embedded versio. |
| the practice of statistics in the life sciences free: OpenIntro Statistics David Diez, Christopher Barr, Mine Çetinkaya-Rundel, 2015-07-02 The OpenIntro project was founded in 2009 to improve the quality and availability of education by producing exceptional books and teaching tools that are free to use and easy to modify. We feature real data whenever possible, and files for the entire textbook are freely available at openintro.org. Visit our website, openintro.org. We provide free videos, statistical software labs, lecture slides, course management tools, and many other helpful resources. |
| the practice of statistics in the life sciences free: Statistical Issues in Drug Development Stephen S. Senn, 2008-02-28 Drug development is the process of finding and producingtherapeutically useful pharmaceuticals, turning them into safe andeffective medicine, and producing reliable information regardingthe appropriate dosage and dosing intervals. With regulatoryauthorities demanding increasingly higher standards in suchdevelopments, statistics has become an intrinsic and criticalelement in the design and conduct of drug development programmes. Statistical Issues in Drug Development presents anessential and thought provoking guide to the statistical issues andcontroversies involved in drug development. This highly readable second edition has been updated toinclude: Comprehensive coverage of the design and interpretation ofclinical trials. Expanded sections on missing data, equivalence, meta-analysisand dose finding. An examination of both Bayesian and frequentist methods. A new chapter on pharmacogenomics and expanded coverage ofpharmaco-epidemiology and pharmaco-economics. Coverage of the ICH guidelines, in particular ICH E9,Statistical Principles for Clinical Trials. It is hoped that the book will stimulate dialogue betweenstatisticians and life scientists working within the pharmaceuticalindustry. The accessible and wide-ranging coverage make itessential reading for both statisticians and non-statisticiansworking in the pharmaceutical industry, regulatory bodies andmedical research institutes. There is also much to benefitundergraduate and postgraduate students whose courses include amedical statistics component. |
| the practice of statistics in the life sciences free: Statistical Power Analysis for the Behavioral Sciences Jacob Cohen, 2013-05-13 Statistical Power Analysis is a nontechnical guide to power analysis in research planning that provides users of applied statistics with the tools they need for more effective analysis. The Second Edition includes: * a chapter covering power analysis in set correlation and multivariate methods; * a chapter considering effect size, psychometric reliability, and the efficacy of qualifying dependent variables and; * expanded power and sample size tables for multiple regression/correlation. |
| the practice of statistics in the life sciences free: Understanding The New Statistics Geoff Cumming, 2013-06-19 This is the first book to introduce the new statistics - effect sizes, confidence intervals, and meta-analysis - in an accessible way. It is chock full of practical examples and tips on how to analyze and report research results using these techniques. The book is invaluable to readers interested in meeting the new APA Publication Manual guidelines by adopting the new statistics - which are more informative than null hypothesis significance testing, and becoming widely used in many disciplines. Accompanying the book is the Exploratory Software for Confidence Intervals (ESCI) package, free software that runs under Excel and is accessible at www.thenewstatistics.com. The book’s exercises use ESCI's simulations, which are highly visual and interactive, to engage users and encourage exploration. Working with the simulations strengthens understanding of key statistical ideas. There are also many examples, and detailed guidance to show readers how to analyze their own data using the new statistics, and practical strategies for interpreting the results. A particular strength of the book is its explanation of meta-analysis, using simple diagrams and examples. Understanding meta-analysis is increasingly important, even at undergraduate levels, because medicine, psychology and many other disciplines now use meta-analysis to assemble the evidence needed for evidence-based practice. The book’s pedagogical program, built on cognitive science principles, reinforces learning: Boxes provide evidence-based advice on the most effective statistical techniques. Numerous examples reinforce learning, and show that many disciplines are using the new statistics. Graphs are tied in with ESCI to make important concepts vividly clear and memorable. Opening overviews and end of chapter take-home messages summarize key points. Exercises encourage exploration, deep understanding, and practical applications. This highly accessible book is intended as the core text for any course that emphasizes the new statistics, or as a supplementary text for graduate and/or advanced undergraduate courses in statistics and research methods in departments of psychology, education, human development , nursing, and natural, social, and life sciences. Researchers and practitioners interested in understanding the new statistics, and future published research, will also appreciate this book. A basic familiarity with introductory statistics is assumed. |
| the practice of statistics in the life sciences free: How to Lie with Statistics Darrell Huff, 2023-03-30 'A great introduction to a crucial topic' Bill Gates 'Perhaps the most popular book on statistics ever published ... It's a marvel ... gave me a peek behind the curtain of statistical manipulation, showing me how the swindling was done so that I would not be fooled again' Tim Harford In 1954, Darrell Huff decided enough was enough. Fed up with politicians, advertisers and journalists using statistics to sensationalise, inflate, confuse, oversimplify and - on occasion - downright lie, he decided to shed light on their ill-informed and sneaky ways. How to Lie with Statistics is the result - the definitive and hilarious primer in the ways statistics are used to deceive. With over one and half million copies sold around the world, it has delighted generations of readers with its cheeky takes on the ins and outs of samples, averages, errors, graphs and indexes. And in the modern world of big data and misinformation, Huff remains the perfect guide through the maze of facts and figures that are designed to make us believe anything. 'A hilarious exploration of mathematical mendacity.... Every time you pick it up, what happens? Bang goes another illusion!' The New York Times 'A pleasantly subversive little book guaranteed to undermine your faith in the almighty statistic' Atlantic |
| the practice of statistics in the life sciences free: Dual Use Research of Concern in the Life Sciences National Academies of Sciences, Engineering, and Medicine, Policy and Global Affairs, Committee on Science, Technology, and Law, Committee on Dual Use Research of Concern: Options for Future Management, 2017-10-01 The potential misuse of advances in life sciences research is raising concerns about national security threats. Dual Use Research of Concern in the Life Sciences: Current Issues and Controversies examines the U.S. strategy for reducing biosecurity risks in life sciences research and considers mechanisms that would allow researchers to manage the dissemination of the results of research while mitigating the potential for harm to national security. |
| the practice of statistics in the life sciences free: A Framework for K-12 Science Education National Research Council, Division of Behavioral and Social Sciences and Education, Board on Science Education, Committee on a Conceptual Framework for New K-12 Science Education Standards, 2012-02-28 Science, engineering, and technology permeate nearly every facet of modern life and hold the key to solving many of humanity's most pressing current and future challenges. The United States' position in the global economy is declining, in part because U.S. workers lack fundamental knowledge in these fields. To address the critical issues of U.S. competitiveness and to better prepare the workforce, A Framework for K-12 Science Education proposes a new approach to K-12 science education that will capture students' interest and provide them with the necessary foundational knowledge in the field. A Framework for K-12 Science Education outlines a broad set of expectations for students in science and engineering in grades K-12. These expectations will inform the development of new standards for K-12 science education and, subsequently, revisions to curriculum, instruction, assessment, and professional development for educators. This book identifies three dimensions that convey the core ideas and practices around which science and engineering education in these grades should be built. These three dimensions are: crosscutting concepts that unify the study of science through their common application across science and engineering; scientific and engineering practices; and disciplinary core ideas in the physical sciences, life sciences, and earth and space sciences and for engineering, technology, and the applications of science. The overarching goal is for all high school graduates to have sufficient knowledge of science and engineering to engage in public discussions on science-related issues, be careful consumers of scientific and technical information, and enter the careers of their choice. A Framework for K-12 Science Education is the first step in a process that can inform state-level decisions and achieve a research-grounded basis for improving science instruction and learning across the country. The book will guide standards developers, teachers, curriculum designers, assessment developers, state and district science administrators, and educators who teach science in informal environments. |
| the practice of statistics in the life sciences free: The Book of R Tilman M. Davies, 2016-07-16 The Book of R is a comprehensive, beginner-friendly guide to R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis. You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package. Dozens of hands-on exercises (with downloadable solutions) take you from theory to practice, as you learn: –The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops –Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R –How to access R’s thousands of functions, libraries, and data sets –How to draw valid and useful conclusions from your data –How to create publication-quality graphics of your results Combining detailed explanations with real-world examples and exercises, this book will provide you with a solid understanding of both statistics and the depth of R’s functionality. Make The Book of R your doorway into the growing world of data analysis. |
| the practice of statistics in the life sciences free: Introduction to Data Science Rafael A. Irizarry, 2019-11-20 Introduction to Data Science: Data Analysis and Prediction Algorithms with R introduces concepts and skills that can help you tackle real-world data analysis challenges. It covers concepts from probability, statistical inference, linear regression, and machine learning. It also helps you develop skills such as R programming, data wrangling, data visualization, predictive algorithm building, file organization with UNIX/Linux shell, version control with Git and GitHub, and reproducible document preparation. This book is a textbook for a first course in data science. No previous knowledge of R is necessary, although some experience with programming may be helpful. The book is divided into six parts: R, data visualization, statistics with R, data wrangling, machine learning, and productivity tools. Each part has several chapters meant to be presented as one lecture. The author uses motivating case studies that realistically mimic a data scientist’s experience. He starts by asking specific questions and answers these through data analysis so concepts are learned as a means to answering the questions. Examples of the case studies included are: US murder rates by state, self-reported student heights, trends in world health and economics, the impact of vaccines on infectious disease rates, the financial crisis of 2007-2008, election forecasting, building a baseball team, image processing of hand-written digits, and movie recommendation systems. The statistical concepts used to answer the case study questions are only briefly introduced, so complementing with a probability and statistics textbook is highly recommended for in-depth understanding of these concepts. If you read and understand the chapters and complete the exercises, you will be prepared to learn the more advanced concepts and skills needed to become an expert. |
| the practice of statistics in the life sciences free: Statistics for Health Care Professionals Ian Scott, Debbie Mazhindu, 2005-01-13 Statistics for Health Care Professionals is an accessible guide to understanding statistics within health care practice. Focusing on quantitative approaches to investigating problems, the book introduces the basic rules and principles of statistics. Challenging the notion that statistics are often incomprehensible and complex to use, the authors begin by presenting a `how to' section explaining how specific statistical tests can be performed. They also help readers to understand the language of statistics, which is often a stumbling block for those coming to the subject for the first time. The reader is taught how to calculate statistics by hand as well as being introduced to computer packages to make life easier, and then how to analyse these results. As the results of health care research are so integral to decision-making and developing new practice within the profession, the book encourages the reader to think critically about data analysis and research design, and how these can impact upon evidence based practice. This critical stance is also crucial in the assessment of the many reports and documents issued within the health industry. Statistics for Health Care Professionals includes practical examples of statistical techniques throughout, and the exercises within and at the end of each chapter help readers to learn and to develop proficiency. There is also a glossary at the end of the book for quick and easy referencing. This book is essential reading for those coming to statistics for the first time within a health care setting. |
| the practice of statistics in the life sciences free: The Practice of Statistics Daren S. Starnes, Dan Yates, David S. Moore, 2010-12-17 View a Panopto recording of textbook author Daren Starnes detailing ten reasons the new fourth edition of The Practice of Statistics is the right choice for the AP* Statistics course. Watch instructor video reviews here. Available for your Fall 2010 Course! Request Sample Chapter 3 here. The most thorough and exciting revision to date, The Practice of Statistics 4e is a text that fits all AP* Statistics classrooms. Authors Starnes, Yates and Moore drew upon the guidance of some of the most notable names in AP* and their students to create a text that fits today’s classroom. The new edition comes complete with new pedagogical changes, including built-in AP* testing, four-step examples, section summaries, “Check Your Understanding” boxes and more. The Practice of Statistics long stands as the only high school statistics textbook that directly reflects the College Board course description for AP* Statistics. Combining the data analysis approach with the power of technology, innovative pedagogy, and a number of new features, the fourth edition will provide you and your students with the most effective text for learning statistics and succeeding on the AP* Exam. |
| the practice of statistics in the life sciences free: Statistics for Data Scientists Maurits Kaptein, Edwin van den Heuvel, 2022-02-02 This book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles. Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for data science. |
| the practice of statistics in the life sciences free: Clinical Prediction Models Ewout W. Steyerberg, 2019-07-22 The second edition of this volume provides 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 a sensible strategy is needed for model development, validation, and updating, such that prediction models can better support medical practice. There is an increasing need for personalized evidence-based medicine that uses an individualized approach to medical decision-making. In this Big Data era, there is expanded access to large volumes of routinely collected data and an increased number of applications for prediction models, such as targeted early detection of disease and individualized approaches to diagnostic testing and treatment. Clinical Prediction Models presents a practical checklist that needs to be considered for development of a valid prediction model. Steps 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 usefulness; internal validation; and presentation formatting. The text also addresses common issues that make prediction models suboptimal, such as small sample sizes, exaggerated claims, and poor generalizability. The text is primarily intended for clinical epidemiologists and biostatisticians. Including many case studies and publicly available R code and data sets, the book is also appropriate as a textbook for a graduate course on predictive modeling in diagnosis and prognosis. While practical in nature, the book also provides a philosophical perspective on data analysis in medicine that goes beyond predictive modeling. Updates to this new and expanded edition include: • A discussion of Big Data and its implications for the design of prediction models • Machine learning issues • More simulations with missing ‘y’ values • Extended discussion on between-cohort heterogeneity • Description of ShinyApp • Updated LASSO illustration • New case studies |
| the practice of statistics in the life sciences free: Biomedical Statistics Shakti Kumar Yadav, Sompal Singh, Ruchika Gupta, 2019-11-23 This book is written in a very easy-to-follow format, and explains the key concepts of biomedical statistics in a lucid yet straightforward manner. It explains how mathematical and statistical tools can be used to find answers to common research questions. In addition, the main text is supplemented by a wealth of solved exercises and illustrative examples to aid in comprehension. Given its content, the book offers an invaluable quick reference guide for graduating students and can be very helpful in their examination process. At the same time, it represents a handy guide for medical and paramedical teachers, post-graduate medical students, research personnel, biomedical scientists and epidemiologists. |
| the practice of statistics in the life sciences free: Modern Statistics for Modern Biology SUSAN. HUBER HOLMES (WOLFGANG.), Wolfgang Huber, 2018 |
| the practice of statistics in the life sciences free: Statistical Design and Analysis of Biological Experiments Hans-Michael Kaltenbach, 2021-04-15 This richly illustrated book provides an overview of the design and analysis of experiments with a focus on non-clinical experiments in the life sciences, including animal research. It covers the most common aspects of experimental design such as handling multiple treatment factors and improving precision. In addition, it addresses experiments with large numbers of treatment factors and response surface methods for optimizing experimental conditions or biotechnological yields. The book emphasizes the estimation of effect sizes and the principled use of statistical arguments in the broader scientific context. It gradually transitions from classical analysis of variance to modern linear mixed models, and provides detailed information on power analysis and sample size determination, including ‘portable power’ formulas for making quick approximate calculations. In turn, detailed discussions of several real-life examples illustrate the complexities and aberrations that can arise in practice. Chiefly intended for students, teachers and researchers in the fields of experimental biology and biomedicine, the book is largely self-contained and starts with the necessary background on basic statistical concepts. The underlying ideas and necessary mathematics are gradually introduced in increasingly complex variants of a single example. Hasse diagrams serve as a powerful method for visualizing and comparing experimental designs and deriving appropriate models for their analysis. Manual calculations are provided for early examples, allowing the reader to follow the analyses in detail. More complex calculations rely on the statistical software R, but are easily transferable to other software. Though there are few prerequisites for effectively using the book, previous exposure to basic statistical ideas and the software R would be advisable. |
| the practice of statistics in the life sciences free: The Practice of Statistics for Business and Economics David S. Moore, 2011-01-01 This text immerses students in the course immediately, involving them in practical, statistics-supported business decision making from the outset. Using real data to provide a context for tackling modern business problems, it introduces a range of core ideas early - including data production and interpretation. |
| the practice of statistics in the life sciences free: Introductory Statistics for the Life and Biomedical Sciences Julie Vu, David Harrington, 2020-03 Introduction to Statistics for the Life and Biomedical Sciences has been written to be used in conjunction with a set of self-paced learning labs. These labs guide students through learning how to apply statistical ideas and concepts discussed in the text with the R computing language.The text discusses the important ideas used to support an interpretation (such as the notion of a confidence interval), rather than the process of generating such material from data (such as computing a confidence interval for a particular subset of individuals in a study). This allows students whose main focus is understanding statistical concepts to not be distracted by the details of a particular software package. In our experience, however, we have found that many students enter a research setting after only a single course in statistics. These students benefit from a practical introduction to data analysis that incorporates the use of a statistical computing language.In a classroom setting, we have found it beneficial for students to start working through the labs after having been exposed to the corresponding material in the text, either from self-reading or through an instructor presenting the main ideas. The labs are organized by chapter, and each lab corresponds to a particular section or set of sections in the text.There are traditional exercises at the end of each chapter that do not require the use of computing. In the current posting, Chapters 1 - 5 have end-of-chapter exercises. More complicated methods, such as multiple regression, do not lend themselves to hand calculation and computing is necessary for gaining practical experience with these methods. The lab exercises for these later chapters become an increasingly important part of mastering the material.An essential component of the learning labs are the Lab Notes accompanying each chapter. The lab notes are a detailed reference guide to the R functions that appear in the labs, written to be accessible to a first-time user of a computing language. They provide more explanation than available in the R help documentation, with examples specific to what is demonstrated in the labs. |
| the practice of statistics in the life sciences free: Statistics for High-Dimensional Data Peter Bühlmann, Sara van de Geer, 2011-06-08 Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science. |
| the practice of statistics in the life sciences free: Introduction to the Practice of Statistics David S. Moore, George P. McCabe, Bruce A. Craig, 2009 The new Sixth Edition brings the acclaimed IPS approach to a new generation, with a number of enhancements in the text and with breakthrough media tools for instructors and students. It demonstrates how statistical techniques are used to solve real-world problems, combining real data and applications with innovative pedagogy, both in the text and via electronic media. New Format Options Introduction to the Practice of Statistics, Sixth Edition is available as: • A core book containing the first 13 chapters in hardcover (1-4292-1622-0) or paperback (1-4292-1621-2). Companion chapters 14-17 are available on the book's CD and web site. • Extended Version (hardcover; includes chapters 1-15): 1-4292-1623-9 |
| the practice of statistics in the life sciences free: Using R for Introductory Statistics John Verzani, 2018-10-03 The second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package=UsingR)), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing. |
| the practice of statistics in the life sciences free: Modeling Life Alan Garfinkel, Jane Shevtsov, Yina Guo, 2017-09-06 This book develops the mathematical tools essential for students in the life sciences to describe interacting systems and predict their behavior. From predator-prey populations in an ecosystem, to hormone regulation within the body, the natural world abounds in dynamical systems that affect us profoundly. Complex feedback relations and counter-intuitive responses are common in nature; this book develops the quantitative skills needed to explore these interactions. Differential equations are the natural mathematical tool for quantifying change, and are the driving force throughout this book. The use of Euler’s method makes nonlinear examples tractable and accessible to a broad spectrum of early-stage undergraduates, thus providing a practical alternative to the procedural approach of a traditional Calculus curriculum. Tools are developed within numerous, relevant examples, with an emphasis on the construction, evaluation, and interpretation of mathematical models throughout. Encountering these concepts in context, students learn not only quantitative techniques, but how to bridge between biological and mathematical ways of thinking. Examples range broadly, exploring the dynamics of neurons and the immune system, through to population dynamics and the Google PageRank algorithm. Each scenario relies only on an interest in the natural world; no biological expertise is assumed of student or instructor. Building on a single prerequisite of Precalculus, the book suits a two-quarter sequence for first or second year undergraduates, and meets the mathematical requirements of medical school entry. The later material provides opportunities for more advanced students in both mathematics and life sciences to revisit theoretical knowledge in a rich, real-world framework. In all cases, the focus is clear: how does the math help us understand the science? |
| the practice of statistics in the life sciences free: Probability and Statistics Michael J. Evans, Jeffrey S. Rosenthal, 2010-03-01 Unlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor to the course, incorporating the computer and offering an integrated approach to inference that includes the frequency approach and the Bayesian inference. From the start the book integrates simulations into its theoretical coverage, and emphasizes the use of computer-powered computation throughout. Math and science majors with just one year of calculus can use this text and experience a refreshing blend of applications and theory that goes beyond merely mastering the technicalities. The new edition includes a number of features designed to make the material more accessible and level-appropriate to the students taking this course today. |
| the practice of statistics in the life sciences free: Introduction to the Practice of Statistics David S. Moore, George P. McCabe, 1999 CD-ROM includes: Electronic Encyclopedia of Statistical Examples and Exercises, an interactive quiz for each chapter, video clips and some special electronic statistical tools. |
| the practice of statistics in the life sciences free: Biostatistics Wayne W. Daniel, Chad L. Cross, 2018-11-13 The ability to analyze and interpret enormous amounts of data has become a prerequisite for success in allied healthcare and the health sciences. Now in its 11th edition, Biostatistics: A Foundation for Analysis in the Health Sciences continues to offer in-depth guidance toward biostatistical concepts, techniques, and practical applications in the modern healthcare setting. Comprehensive in scope yet detailed in coverage, this text helps students understand—and appropriately use—probability distributions, sampling distributions, estimation, hypothesis testing, variance analysis, regression, correlation analysis, and other statistical tools fundamental to the science and practice of medicine. Clearly-defined pedagogical tools help students stay up-to-date on new material, and an emphasis on statistical software allows faster, more accurate calculation while putting the focus on the underlying concepts rather than the math. Students develop highly relevant skills in inferential and differential statistical techniques, equipping them with the ability to organize, summarize, and interpret large bodies of data. Suitable for both graduate and advanced undergraduate coursework, this text retains the rigor required for use as a professional reference. |
| the practice of statistics in the life sciences free: Practical Statistics for Data Scientists Peter Bruce, Andrew Bruce, 2017-05-10 Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data |
| the practice of statistics in the life sciences free: The SAGE Dictionary of Statistics Duncan Cramer, Dennis Howitt, 2004-06-09 The SAGE Dictionary of Statistics provides students and researchers with an accessible and definitive resource to use when studying statistics in the social sciences, reading research reports and undertaking data analysis. |
| the practice of statistics in the life sciences free: Statistics for Criminal Justice and Criminology in Practice and Research Jack Fitzgerald, Jerry Fitzgerald, 2013-01-17 Statistics for Criminal Justice and Criminology in Practice and Research—by Jack Fitzgerald and Jerry Fitzgerald—is an engaging and comprehensive introduction to the study of basic statistics for students pursuing careers as practitioners or researchers in both Criminal Justice and Criminology programs. This student-friendly text shows how to calculate a variety of descriptive and inferential statistics, recognize which statistics are appropriate for particular data analysis situations, and perform hypothesis tests using inferential statistics. But it is much more than a cook book. It encourages readers to think critically about the strengths and limitations of the statistics they are calculating, as well as how they may be misapplied and misleading. Examples of statistics and statistical analyses are drawn from the worlds of the practitioner as well as the policymaker and researcher. Students will also gain a clear understanding of major ethical issues in conducting statistical analyses and reporting results, as well as insight into the realities of the life of researchers and practitioners as they use statistics and statistical analyses in their day-to-day activities. |
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