Fundamental Statistical Principles for the Neurobiologist

Fundamental Statistical Principles for Neurobiologists introduces readers to basic experimental design and statistical thinking in a comprehensive, relevant manner. This book is an introductory statistics book that covers fundamental principles written by a neuroscientist who understands the plight of the neuroscience graduate student and the senior investigator. It summarizes the fundamental concepts associated with statistical analysis that are useful for the neuroscientist, and provides understanding of a particular test in language that is more understandable to this specific audience, with the overall purpose of explaining which statistical technique should be used in which situation. Different types of data are discussed such as how to formulate a research hypothesis, the primary types of statistical errors and statistical power, followed by how to actually graph data and what kinds of mistakes to avoid. Chapters discuss variance, standard deviation, standard error, mean, confidence intervals, correlation, regression, parametric vs. nonparametric statistical tests, ANOVA, and post hoc analyses. Finally, there is a discussion on how to deal with data points that appear to be "outliers" and what to do when there is missing data, an issue that has not sufficiently been covered in literature. An introductory guide to statistics aimed specifically at the neuroscience audience Contains numerous examples with actual data that is used in the analysis Gives the investigators a starting pointing for evaluating data in easy-to-understand language Explains in detail many different statistical tests commonly used by neuroscientists

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  • Author : Stephen W. Scheff
  • Publisher : Academic Press
  • Pages : 234 pages
  • ISBN : 0128050519
  • Rating : 4/5 from 21 reviews
CLICK HERE TO GET THIS BOOKFundamental Statistical Principles for the Neurobiologist

Fundamental Statistical Principles for the Neurobiologist

Fundamental Statistical Principles for the Neurobiologist
  • Author : Stephen W. Scheff
  • Publisher : Academic Press
  • Release : 11 February 2016
GET THIS BOOKFundamental Statistical Principles for the Neurobiologist

Fundamental Statistical Principles for Neurobiologists introduces readers to basic experimental design and statistical thinking in a comprehensive, relevant manner. This book is an introductory statistics book that covers fundamental principles written by a neuroscientist who understands the plight of the neuroscience graduate student and the senior investigator. It summarizes the fundamental concepts associated with statistical analysis that are useful for the neuroscientist, and provides understanding of a particular test in language that is more understandable to this specific audience, with

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GET THIS BOOKSignal Processing and Machine Learning for Biomedical Big Data

Within the healthcare domain, big data is defined as any ``high volume, high diversity biological, clinical, environmental, and lifestyle information collected from single individuals to large cohorts, in relation to their health and wellness status, at one or several time points.'' Such data is crucial because within it lies vast amounts of invaluable information that could potentially change a patient's life, opening doors to alternate therapies, drugs, and diagnostic tools. Signal Processing and Machine Learning for Biomedical Big Data

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  • Publisher : John Wiley & Sons
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  • Release : 22 September 2010
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  • Publisher : Oxford : EOLSS Publishers/UNESCO
  • Release : 07 March 2021
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This three volume set presents a multidisciplinary examination of the global life support systems on which we depend by providing a selection of articles on sustainable development issues written by international experts. Volume 1 focuses on the earth and atmospheric sciences, mathematical, biological and medical sciences, social sciences and humanities, physical sciences, engineering and technology resources. Volume 2 covers chemical sciences, energy science and water engineering, as well as the main issues related to environmental sciences and ecological resources. Volume 3 offers a

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