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Marketing and regulatory pressures are driving laboratories to adopt statistically valid quality control or quality assurance systems. For the laboratory professional who’s unfamiliar with the statistical tools used in laboratory quality control, Basic Statistics for Laboratories offers guidance to employing basic statistical controls or reports required by regulatory or accrediting organizations, as well as statistical methods which may otherwise be useful in the lab. The book explains, in basic terms, how to set up, maintain, and interpret control charts and other commonly used laboratory statistical tools, and explains their value to the user. Every technique is delivered in its simplest, most basic form. There is step-by-step guidance to method development, validation, comparison of test methods, and quality control for even small samples, without the use of mathematics beyond the high school level. Tests for the significance of differences, presented in "cookbook" format solutions, make it easy for lab professionals to plug in their own data and use tables. You’ll also find exclusive coverage of the problems of asbestos counting laboratories. In addition, the volume presents simple solutions to other problems involving data handling and interpretation, such as the treatment of outliers and how to deal with single or rarely encountered samples. For analysts, test engineers, and laboratory technicians in medical, pathological, environmental, industrial hygiene, and forensic laboratories, Basic Statistics for Laboratories is a timely and essential reference.
This manual asks students to gather their own data, work with it in Minitab "RM", and report on it in writing. Each of the 17 assigned experiments combines several important statistical concepts. The manual also features: -- Laboratory experiments that emphasize data analysis, design of experiments, and sampling -- Experiments that interrelate classical concepts and techniques -- Writing assignments that focus student attention on interpretation of output -- Experiments that challenge students to go beyond simple answers by reflecting on experimental results, drawing conclusions, and revising and extending the experiments -- Several experiments that are designed as group projects
This manual asks students to gather their own data, work with it in Minitab, and report on it in writing. Each of the 17 assigned experiments combines several important statistical concepts.
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
Work at the biology bench requires an ever-increasing knowledge of mathematical methods and formulae. This is a compilation of the most common mathematical concepts and methods in molecular biology, with clear, straightforward guidance on their application to research investigations.
Laboratory Manual of Biomathematics is a companion to the textbook An Invitation to Biomathematics. This laboratory manual expertly aids students who wish to gain a deeper understanding of solving biological issues with computer programs. It provides hands-on exploration of model development, model validation, and model refinement, enabling students to truly experience advancements made in biology by mathematical models. Each of the projects offered can be used as individual module in traditional biology or mathematics courses such as calculus, ordinary differential equations, elementary probability, statistics, and genetics. Biological topics include: Ecology, Toxicology, Microbiology, Epidemiology, Genetics, Biostatistics, Physiology, Cell Biology, and Molecular Biology . Mathematical topics include Discrete and continuous dynamical systems, difference equations, differential equations, probability distributions, statistics, data transformation, risk function, statistics, approximate entropy, periodic components, and pulse-detection algorithms. It includes more than 120 exercises derived from ongoing research studies. This text is designed for courses in mathematical biology, undergraduate biology majors, as well as general mathematics. The reader is not expected to have any extensive background in either math or biology. Can be used as a computer lab component of a course in biomathematics or as homework projects for independent student work Biological topics include: Ecology, Toxicology, Microbiology, Epidemiology, Genetics, Biostatistics, Physiology, Cell Biology, and Molecular Biology Mathematical topics include: Discrete and continuous dynamical systems, difference equations, differential equations, probability distributions, statistics, data transformation, risk function, statistics, approximate entropy, periodic components, and pulse-detection algorithms Includes more than 120 exercises derived from ongoing research studies
Packed with exercises, checklists, and how-to sections, this robust lab manual gives students hands-on guidance and practice for analyzing their own psychological research. The lab manual’s four sections include activities that correspond directly with the chapters of Dawn M. McBride’s The Process of Statistical Analysis in Psychology; activities related to data analysis projects (including data sets) that students can manipulate and analyze; activities designed to help students choose the correct test for different types of data; and exercises designed to help students write up results from analyses in APA style. INSTRUCTORS: Bundle the Lab Manual for Statistical Analysis with The Process of Statistical Analysis in Psychology for only $5 more! Bundle ISBN: 978-1-5443-0974-3
This open access textbook provides the background needed to correctly use, interpret and understand statistics and statistical data in diverse settings. Part I makes key concepts in statistics readily clear. Parts I and II give an overview of the most common tests (t-test, ANOVA, correlations) and work out their statistical principles. Part III provides insight into meta-statistics (statistics of statistics) and demonstrates why experiments often do not replicate. Finally, the textbook shows how complex statistics can be avoided by using clever experimental design. Both non-scientists and students in Biology, Biomedicine and Engineering will benefit from the book by learning the statistical basis of scientific claims and by discovering ways to evaluate the quality of scientific reports in academic journals and news outlets.
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.