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Author: Robert George Douglas Steel
Publisher: McGraw-Hill Science, Engineering & Mathematics
ISBN:
Category : Mathematics
Languages : en
Pages : 696
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Book Description
This textbook provides a thorough treatment of major statistical methods and techniques for both staticticians and non-statisticians requiring a foundation in applied statistics. There is an emphasis throughout on inference from data, the principle of fitting models by least squares, and careful interpretation of results. The authors employ SAS to produce PC-based statistical graphics and perform some analyses where appropriate. This edition includes updated real-world data sets.
Author: Robert George Douglas Steel
Publisher: McGraw-Hill Science, Engineering & Mathematics
ISBN:
Category : Mathematics
Languages : en
Pages : 696
Get Book Here
Book Description
This textbook provides a thorough treatment of major statistical methods and techniques for both staticticians and non-statisticians requiring a foundation in applied statistics. There is an emphasis throughout on inference from data, the principle of fitting models by least squares, and careful interpretation of results. The authors employ SAS to produce PC-based statistical graphics and perform some analyses where appropriate. This edition includes updated real-world data sets.
Author: Robert G. D. Steel
Publisher:
ISBN: 9780071147491
Category : Biomathematics
Languages : en
Pages : 0
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Book Description
Author: Robert George Douglas Steel
Publisher: McGraw-Hill Companies
ISBN:
Category : Mathematics
Languages : en
Pages : 504
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Book Description
Statistics defined. Some history of statistics. Statistics and the scientific method. studying statistic; Probability. Sampling from a normal distribution. Comparisons involving two sample means. Principles of experimental design. Analysis of variance I: the one-way classification. Analysis of variance II: multiway classifications. Linear regression. Linear correlation. Analysis of variance III: Factorial experiments. Analysis of variance IV: split-plot designs and analysis. Analysis of variance V: unequal subsclass numbers. Multiple and partial regression and correlation. Analysis of covariance. Nonlinear regression. Some uses of chi-square. Enumeration data I: one-way classifications. Enumeration data II: contingency tables. Some discrete distributions. Nonparametric statistics. Sampling finite populations.
Author: Alvan R. Feinstein
Publisher: CRC Press
ISBN: 1420035681
Category : Mathematics
Languages : en
Pages : 713
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Book Description
The get-it-over-with-quickly approach to statistics has been encouraged - and often necessitated - by the short time allotted to it in most curriculums. If included at all, statistics is presented briefly, as a task to be endured mainly because pertinent questions may appear in subsequent examinations for licensure or other certifications. However,
Author: Rudolf J. Freund
Publisher: Elsevier
ISBN: 0080498221
Category : Mathematics
Languages : en
Pages : 694
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Book Description
This broad text provides a complete overview of most standard statistical methods, including multiple regression, analysis of variance, experimental design, and sampling techniques. Assuming a background of only two years of high school algebra, this book teaches intelligent data analysis and covers the principles of good data collection. * Provides a complete discussion of analysis of data including estimation, diagnostics, and remedial actions * Examples contain graphical illustration for ease of interpretation * Intended for use with almost any statistical software * Examples are worked to a logical conclusion, including interpretation of results * A complete Instructor's Manual is available to adopters
Author: Roberto Rivera
Publisher: John Wiley & Sons
ISBN: 1119486416
Category : Mathematics
Languages : en
Pages : 688
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Book Description
Introduces readers to the principles of managerial statistics and data science, with an emphasis on statistical literacy of business students Through a statistical perspective, this book introduces readers to the topic of data science, including Big Data, data analytics, and data wrangling. Chapters include multiple examples showing the application of the theoretical aspects presented. It features practice problems designed to ensure that readers understand the concepts and can apply them using real data. Over 100 open data sets used for examples and problems come from regions throughout the world, allowing the instructor to adapt the application to local data with which students can identify. Applications with these data sets include: Assessing if searches during a police stop in San Diego are dependent on driver’s race Visualizing the association between fat percentage and moisture percentage in Canadian cheese Modeling taxi fares in Chicago using data from millions of rides Analyzing mean sales per unit of legal marijuana products in Washington state Topics covered in Principles of Managerial Statistics and Data Science include:data visualization; descriptive measures; probability; probability distributions; mathematical expectation; confidence intervals; and hypothesis testing. Analysis of variance; simple linear regression; and multiple linear regression are also included. In addition, the book offers contingency tables, Chi-square tests, non-parametric methods, and time series methods. The textbook: Includes academic material usually covered in introductory Statistics courses, but with a data science twist, and less emphasis in the theory Relies on Minitab to present how to perform tasks with a computer Presents and motivates use of data that comes from open portals Focuses on developing an intuition on how the procedures work Exposes readers to the potential in Big Data and current failures of its use Supplementary material includes: a companion website that houses PowerPoint slides; an Instructor's Manual with tips, a syllabus model, and project ideas; R code to reproduce examples and case studies; and information about the open portal data Features an appendix with solutions to some practice problems Principles of Managerial Statistics and Data Science is a textbook for undergraduate and graduate students taking managerial Statistics courses, and a reference book for working business professionals.
Author: Stephen W. Scheff
Publisher: Academic Press
ISBN: 0128050519
Category : Science
Languages : en
Pages : 236
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Book Description
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
Author: Robert R. Sokal
Publisher: Macmillan
ISBN: 9780716724117
Category : Mathematics
Languages : en
Pages : 918
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Book Description
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.
Author: D.R. Cox
Publisher: Routledge
ISBN: 1351465791
Category : Mathematics
Languages : en
Pages : 202
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Book Description
This book should be of interest to senior undergraduate and postgraduate students of applied statistics.
Author: David A. Blackwell
Publisher: Courier Corporation
ISBN: 0486150895
Category : Mathematics
Languages : en
Pages : 388
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Book Description
Evaluating statistical procedures through decision and game theory, as first proposed by Neyman and Pearson and extended by Wald, is the goal of this problem-oriented text in mathematical statistics. First-year graduate students in statistics and other students with a background in statistical theory and advanced calculus will find a rigorous, thorough presentation of statistical decision theory treated as a special case of game theory. The work of Borel, von Neumann, and Morgenstern in game theory, of prime importance to decision theory, is covered in its relevant aspects: reduction of games to normal forms, the minimax theorem, and the utility theorem. With this introduction, Blackwell and Professor Girshick look at: Values and Optimal Strategies in Games; General Structure of Statistical Games; Utility and Principles of Choice; Classes of Optimal Strategies; Fixed Sample-Size Games with Finite Ω and with Finite A; Sufficient Statistics and the Invariance Principle; Sequential Games; Bayes and Minimax Sequential Procedures; Estimation; and Comparison of Experiments. A few topics not directly applicable to statistics, such as perfect information theory, are also discussed. Prerequisites for full understanding of the procedures in this book include knowledge of elementary analysis, and some familiarity with matrices, determinants, and linear dependence. For purposes of formal development, only discrete distributions are used, though continuous distributions are employed as illustrations. The number and variety of problems presented will be welcomed by all students, computer experts, and others using statistics and game theory. This comprehensive and sophisticated introduction remains one of the strongest and most useful approaches to a field which today touches areas as diverse as gambling and particle physics.
