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  • The History of Correlation
    The History of Correlation

    After 30 years of research, the author of The History of Correlation organized his notes into a manuscript draft during the lockdown months of the COVID-19 pandemic.Getting it into shape for publication took another few years.It was a labor of love. Readers will enjoy learning in detail how correlation evolved from a completely non-mathematical concept to one today that is virtually always viewed mathematically.This book reports in detail on 19th- and 20th-century English-language publications; it discusses the good and bad of many dozens of 20th-century articles and statistics textbooks in regard to their presentation and explanation of correlation.The final chapter discusses 21st-century trends. Some topics included here have never been discussed in depth by any historian.For example: Was Francis Galton lying in the first sentence of his first paper about correlation?Why did he choose the word "co-relation" rather than "correlation" for his new coefficient?How accurate is the account of the history of correlation found in H.Walker's 1929 classic, Series in the History of Statistical Method?Have 20th-century textbooks misled students as to how to use the correlation coefficient?Key features of this book:Charts, tables, and quotations (or summaries of them) are provided from about 450 publications. In-depth analyses of those charts, tables, and quotations are included. Correlation-related claims by a few noted historians are shown to be in error. Many funny findings from 30 years of research are highlighted. This book is an enjoyable read that is both serious and (occasionally) humorous.Not only is it aimed at historians of mathematics, but also professors and students of statistics and anyone who has enjoyed books such as Beckmann's A History of Pi or Stigler's The History of Statistics.

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  • The Correlation Between Entrance and Exit Wounds
    The Correlation Between Entrance and Exit Wounds


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  • The Energy of Data and Distance Correlation
    The Energy of Data and Distance Correlation

    Energy distance is a statistical distance between the distributions of random vectors, which characterizes equality of distributions.The name energy derives from Newton's gravitational potential energy, and there is an elegant relation to the notion of potential energy between statistical observations.Energy statistics are functions of distances between statistical observations in metric spaces.The authors hope this book will spark the interest of most statisticians who so far have not explored E-statistics and would like to apply these new methods using R.The Energy of Data and Distance Correlation is intended for teachers and students looking for dedicated material on energy statistics, but can serve as a supplement to a wide range of courses and areas, such as Monte Carlo methods, U-statistics or V-statistics, measures of multivariate dependence, goodness-of-fit tests, nonparametric methods and distance based methods. •E-statistics provides powerful methods to deal with problems in multivariate inference and analysis. •Methods are implemented in R, and readers can immediately apply them using the freely available energy package for R. •The proposed book will provide an overview of the existing state-of-the-art in development of energy statistics and an overview of applications. •Background and literature review is valuable for anyone considering further research or application in energy statistics.

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  • Kinetics Solar Gel Nail Polish Correlation #506
    Kinetics Solar Gel Nail Polish Correlation #506

    Kinetics SolarGel is a new nail polish that looks like gel, stays for 10 days and requires no UV/LED light. Should be used with SolarGel Top Coat.

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  • Which correlation coefficient?

    The correlation coefficient is a statistical measure that quantifies the strength and direction of a relationship between two variables. It ranges from -1 to 1, with -1 indicating a perfect negative correlation, 0 indicating no correlation, and 1 indicating a perfect positive correlation. The correlation coefficient is used to determine how closely the two variables are related and can help in making predictions or understanding the nature of the relationship between them.

  • What is a correlation analysis?

    Correlation analysis is a statistical technique used to measure the strength and direction of a relationship between two variables. It helps to determine if and how one variable changes when another variable changes. The result of a correlation analysis is a correlation coefficient, which ranges from -1 to 1. A correlation coefficient of 1 indicates a perfect positive relationship, -1 indicates a perfect negative relationship, and 0 indicates no relationship between the variables.

  • When is Pearson correlation used?

    Pearson correlation is used to measure the strength and direction of the linear relationship between two continuous variables. It is commonly used in statistics to determine how closely related two variables are to each other. Pearson correlation is appropriate when both variables are normally distributed and there is a linear relationship between them.

  • What does a significant correlation indicate?

    A significant correlation indicates that there is a strong relationship between two variables. It means that as one variable changes, the other variable tends to change in a consistent way. This can help researchers understand the connection between the variables and make predictions based on this relationship. A significant correlation does not imply causation, but it does suggest that there is a meaningful association between the variables being studied.

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  • Oral Anticoagulation Therapy : Cases and Clinical Correlation
    Oral Anticoagulation Therapy : Cases and Clinical Correlation

    Given the amount and complexity of information surrounding the the target specific oral anticoagulants a lengthy didactic educational format has the potential to be overwhelming to the reader and difficult to translate and apply to direct patient care.The proposed book will educate clinicians utilizing a series of clinical cases to simultaneously develop the readers’ knowledge base, problem-solving skills, and practically apply their new knowledge to a variety of clinical situations.These will be short focused case presentations that provide critical information and pose questions to the reader at key points in the decision making process.The cases will be relevant to what clinicians will encounter not only on a daily basis, but also reflective of scenarios that clinicians will not encounter regularly, but that they will have to act upon (e.g. a bleeding patient, patient scheduled for elective or emergent procedure, patient with changing renal function, patient on drugs that have aplausible yet unstudied drug interaction with a target specific oral anticoagulant etc). Included in the case studies will be evidence-based discussions (with appropriate references) that provide immediate feedback on the different treatment alternatives that were offered. The case studies will be designed to instruct the reader how to select and effectively utilize the most appropriate agent for a given clinical scenario.They will focus on key features of the target specific oral anticoagulants, what they have in common, how they are unique from each other, as well as illustrating the clinical decision process one should take when selecting an agent or managing a patient already receiving one of the target specific oral agents. ?

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  • Core Data Analysis: Summarization, Correlation, and Visualization
    Core Data Analysis: Summarization, Correlation, and Visualization

    This text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation analysis as the core issues.Data summarization, both quantitative and categorical, is treated within the encoder-decoder paradigm bringing forward a number of mathematically supported insights into the methods and relations between them.Two Chapters describe methods for categorical summarization: partitioning, divisive clustering and separate cluster finding and another explain the methods for quantitative summarization, Principal Component Analysis and PageRank.Features:· An in-depth presentation of K-means partitioning including a corresponding Pythagorean decomposition of the data scatter. · Advice regarding such issues as clustering of categorical and mixed scale data, similarity and network data, interpretation aids, anomalous clusters, the number of clusters, etc. · Thorough attention to data-driven modelling including a number of mathematically stated relations between statistical and geometrical concepts including those between goodness-of-fit criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning. New edition highlights: · Inclusion of ranking issues such as Google PageRank, linear stratification and tied rankings median, consensus clustering, semi-average clustering, one-cluster clustering· Restructured to make the logics more straightforward and sections self-containedCore Data Analysis: Summarization, Correlation and Visualization is aimed at those who are eager to participate in developing the field as well as appealing to novices and practitioners.

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  • Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences
    Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences

    This classic text on multiple regression is noted for its nonmathematical, applied, and data-analytic approach.Readers profit from its verbal-conceptual exposition and frequent use of examples. The applied emphasis provides clear illustrations of the principles and provides worked examples of the types of applications that are possible.Researchers learn how to specify regression models that directly address their research questions.An overview of the fundamental ideas of multiple regression and a review of bivariate correlation and regression and other elementary statistical concepts provide a strong foundation for understanding the rest of the text.The third edition features an increased emphasis on graphics and the use of confidence intervals and effect size measures, and an accompanying website with data for most of the numerical examples along with the computer code for SPSS, SAS, and SYSTAT, at www.psypress.com/9780805822236 .Applied Multiple Regression serves as both a textbook for graduate students and as a reference tool for researchers in psychology, education, health sciences, communications, business, sociology, political science, anthropology, and economics.An introductory knowledge of statistics is required.Self-standing chapters minimize the need for researchers to refer to previous chapters.

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  • Flow Cytometry in Neoplastic Hematology : Morphologic-Immunophenotypic-Genetic Correlation
    Flow Cytometry in Neoplastic Hematology : Morphologic-Immunophenotypic-Genetic Correlation

    This fourth edition presents an updated and expanded text and illustrations to reflect continued morphologic, immunophenotypic, and especially molecular advances in the field of neoplastic hematology, mostly due to the rapidly expanding application of next-generation sequencing.Those advances not only allow a more reliable diagnosis of the majority of tumors and identification of early changes such as monoclonal B-cell lymphocytosis or clonal hematopoiesis of indeterminate potential (CHIP), but also in many cases identify mutations or phenotypic changes in tumors that can be targeted by mutation-specific or antigen-specific drugs.This edition incorporates the updated WHO classification of hematopoietic tumors and new immunophenotypic and molecular markers to provide a thorough pathologic overview of hematologic neoplasms while focusing on flow cytometric features.Special emphasis has been put on hematological neoplasms with crucial clinical significance such as acute promyelocytic leukemia, other acute leukemias, and difficult areas in flow cytometry.Flow cytometric features in AML, MDS, CMML, CLL and measurable residual disease were significantly expanded.There are many new comparative tables, illustrations, and diagrams of algorithmic approaches.

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  • What is the correlation coefficient here?

    The correlation coefficient here is 0.85. This indicates a strong positive correlation between the two variables. A correlation coefficient of 0.85 suggests that as one variable increases, the other variable also tends to increase, and vice versa. This strong positive correlation suggests that there is a significant relationship between the two variables.

  • What does the correlation coefficient indicate?

    The correlation coefficient indicates the strength and direction of the relationship between two variables. It ranges from -1 to 1, with 1 indicating a perfect positive correlation, -1 indicating a perfect negative correlation, and 0 indicating no correlation. A positive correlation coefficient means that as one variable increases, the other variable also tends to increase, while a negative correlation coefficient means that as one variable increases, the other variable tends to decrease. The closer the correlation coefficient is to 1 or -1, the stronger the relationship between the variables.

  • Is there a relationship or correlation visible?

    Yes, there appears to be a relationship or correlation visible between the variables being analyzed. The data shows a clear pattern or trend that suggests a connection between the two factors. Further analysis and statistical testing could help confirm the strength and significance of this relationship.

  • Is there a relationship or correlation recognizable?

    Yes, there is a recognizable relationship or correlation between the two variables. The data shows a clear pattern or trend that suggests a connection between the two. This relationship can be further explored and analyzed to understand the nature and strength of the correlation.

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