"Provides an excellent introduction to the methodology and implementation of experimentation in the natural, engineering and medical sciences. [...] This book focuses on general research skills, such as adopting a scientific mindset, learning how to plan meaningful experiments and understanding the fundamentals of collecting and interpreting data. It is directed to anyone engaged in experiments." Particularly suitable for undergraduates or other novice researchers learning about designing their own experiments.
"The tools to choose the graphs and statistics that are suitable for your data, and to understand what the statistical results actually mean." A very short introduction to statistics and why you need to use them. It also includes some guidance on gathering data, focused for the social sciences, but the principles are broadly applicable.
"Studiously avoids the recipe-book style and keeps algebraic details of specific statistical methods to the minimum extent necessary to understand the underlying concepts." Recommended if you are considering designing an experiment but want a broad overview before choosing an approach, or if you have an approach but want to understand how it fits into the broader statistical and scientific thought process. The text is organized around case studies for different methods. It will likely be most applicable for undergraduates or graduate students learning about experimental design and who are comfortable with seeing equations scattered throughout the text.
The authors "motivate learning with interesting biological and medical examples; they emphasize intuitive understanding; and they focus on real data. The book covers basic topics in introductory statistics, including graphs, confidence intervals, hypothesis testing, comparison of means, regression, and designing experiments." Available on reserves.
"This handbook explains how to establish the framework for an experimental project, how to set up all of the components of an experimental system, design experiments within that system, determine and use the correct set of controls, and formulate models to test the veracity and resiliency of the data." A philosophical guide to experimental design, rather than a statistically focused approach.
"For students or researchers in biology who need to design experiments, sampling programs, or analyze resulting data. [...] The chapters include such topics as linear and logistic regression, simple and complex ANOVA models, log-linear models, and multivariate techniques. The main analyses are illustrated with many examples from published papers and an extensive reference list to both the statistical and biological literature is also included."
"The first part of the book gives a largely non-mathematical introduction to data exploration, univariate methods (including GAM and mixed modelling techniques), multivariate analysis, time series analysis (e.g. common trends) and spatial statistics. The second part provides 17 case studies, mainly written together with biologists who attended courses given by the first authors. [...] The case studies can be used as a template for your own data analysis; just try to find a case study that matches your own ecological questions and data structure, and use this as starting point for you own analysis."
"The Ecological Detective makes liberal use of computer programming for the generation of hypotheses, exploration of data, and the comparison of different models. The authors' attitude is one of exploration, both statistical and graphical."
"The handbook identifies both the advantages of using meta-analysis for research synthesis and the potential pitfalls and limitations of meta-analysis (including when it should not be used). Different approaches to carrying out a meta-analysis are described, and include moment and least-square, maximum likelihood, and Bayesian approaches, all illustrated using worked examples based on real biological datasets."
"Real data sets from postgraduate ecological studies or research projects are used throughout. The first part of the book is a largely non-mathematical introduction to linear mixed effects modelling, GLM and GAM, zero inflated models, GEE, GLMM and GAMM. The second part provides ten case studies that range from koalas to deep sea research."
"This book provides an overview of the theory and application of linear and nonlinear mixed-effects models in the analysis of grouped data, such as longitudinal data, repeated measures, and multilevel data." It focuses in the NLME library for S and S-plus, but can be used in R, and the principles applied to other mixed-effect model packages such as lme4. The first few chapters are useful as an introduction to the topic and the remainder of the book will be of more interest to those with advanced modeling needs or interest in the mathematics of the modeling.
This open and free introductory statistics textbook covers topics typical for a college-level non-math majors statistics course. Topics include distributions, probability, research design, estimation, hypothesis testing, power and effect size, comparison of means, regression, analysis of variance (ANOVA), transformations, chi square, and non-parametric (distribution-free) tests). It is available as a pdf, online, or as an epub. An Instructor's Manual and PowerPoint slides are also available upon request from the project leader at Rice University.
A free and open introductory statistics textbook for non-math majors. "They have sought to present only the core concepts and use a wide-ranging set of exercises for each concept to drive comprehension. [...] a smaller and less intimidating textbook that trades some extended and unnecessary topics for a better-focused presentation of the central material." It covers descriptive statistics, probability, distributions, discrete and continuous random variables, estimation, hypothesis testing, comparison of means, correlation and regression, chi square, and F-tests.
"We hope readers will take away three ideas from this book in addition to forming a foundation of statistical thinking and methods. (1) Statistics is an applied field with a wide range of practical applications. (2) You don't have to be a math guru to learn from interesting, real data. (3) Data are messy, and statistical tools are imperfect. However, when you understand the strengths and weaknesses of these tools, you can use them to learn interesting things about the world." This free and open introductory statistics textbook for non-math majors discusses data and data collection, foundations for inference with randomization and simulations (then leading into standard parametric statistics), inference with categorical and numerical data, and linear, multiple logistic regression. An introduction to probability is included as an appendix.
"An invaluable reference tool that guides readers through drafting the results of quantitative experiments." This book gives social science examples but should be of use to learners and researchers in many scientific fields in learning how to report the results of statistical tests.
"The philosophy of model-based data analysis and strategy for the analysis of empirical data. The book introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that best represents the inference supported by the data. [...] The text has been written for biologists and statisticians using models for making inferences from empirical data."
"Provides advanced undergraduate as well as graduate students with a timely and comprehensive introduction to today's most commonly encountered statistical and multivariate techniques, while assuming only a limited knowledge of higher-level mathematics." Covers SPSS and SAS code; also available on Reserves at Bizzell Memorial Library.
"Reference tool covering statistics, probability theory, biostatistics, quality control, and economics with emphasis in applications of statistical methods in sociology, engineering, computer science, biomedicine, psychology, survey methodology, and other client disciplines." A good source for topics less often covered in the general textbooks.
"More than 500 entries include definitions, history, mathematical details, limitations, examples, references, and further readings. All entries include cross-references as well as the key citations. The back matter includes a timeline of statistical inventions." Another good resource for topics not included in the general texts listed previously.
This book is written for S but can be used for R with minimal modifications. Available as an ebook or physical copy. "A guide to using S environments to perform statistical analyses providing both an introduction to the use of S and a course in modern statistical methods. The emphasis is on presenting practical problems and full analyses of real data sets."
"Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos."
From the introduction: "R makes it easy to work with and learn from data. It also happens to be a complete programmming language, but if you’re reading this guide then that might not be of interest to you. That’s OK — the goal here is not to teach you how to program in R. The goal is to teach you just enough R to be confident to explore your data. In this guide, we use R in the same way we use any other statistics software: To check and visualise data, run statistical analyses, and share our results with others. To do that it’s worth learning the absolute basics of the R language and key recent extensions to it. "
"Although many of the techniques are relevant to molecular bioinformatics, the motivation for the text is much broader, focusing on topics and techniques that are applicable to a range of scientific endeavors."
Call Number: QA76.73.P98 V365 2016 (Youngblood Energy Library)
Publication Date: 2016
"For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all--IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools. Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. "
"Python Data Analytics will help you tackle the world of data acquisition and analysis using the power of the Python language. At the heart of this book lies the coverage of pandas, an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language. "