10433nam 2200577 450 99646654750331620220627155216.03-030-62404-810.1007/978-3-030-62404-0(CKB)4100000011781644(DE-He213)978-3-030-62404-0(MiAaPQ)EBC6511448(Au-PeEL)EBL6511448(OCoLC)1244620971(PPN)254725260(EXLCZ)99410000001178164420211008d2021 uy 0engurnn#008mamaatxtrdacontentcrdamediacrrdacarrierUsing R for biostatistics /Thomas W. MacFarland, Jan M. Yates1st ed. 2021.Cham, Switzerland :Springer,[2021]©20211 online resource (XXXV, 902 p. 300 illus., 265 illus. in color.)3-030-62403-X 1 Introduction: Biostatistics and R -- 1.1 Purpose of this Text -- 1.2 Development of Biostatistics -- 1.3 Development of R -- 1.4 How R is Used in this Text -- 1.5 Import Data into R -- 1.6 Addendum1: Efficient Programming with R, Project Workflow, and Good Programming Practices (gpp) -- 1.7 Addendum2: Preview of Descriptive Statistics and Graphics Using R -- 1.8 Addendum3: R and Beautiful Graphics -- 1.9 Addendum4: Research Designs Used in Biostatistics -- 1.10 Prepare to Exit, Save, and Later Retrieve this R Session -- 1.11 External Data and/or Data Resources Used in this Lesson -- 2 Data Exploration, Descriptive Statistics, and Measures of Central Tendency -- 2.1 Background -- 2.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 2.3 Organize the Data and Display the Code Book -- 2.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 2.5 Descriptive Statistics for Initial Analysis of the Data -- 2.6 Quality Assurance, Data Distribution, and Tests for Normality -- 2.7 Statistical Test(s) -- 2.8 Summary -- 2.9 Addendum1: Specialized External Packages and Functions -- 2.10 Addendum2: Parametric v Nonparametric -- 2.11 Addendum3: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns -- 2.12 Prepare to Exit, Save, and Later Retrieve this R Session -- 2.13 External Data and/or Data Resources Used in this Lesson -- 3 Student's t-Test for Independent Samples -- 3.1 Background -- 3.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 3.3 Organize the Data and Display the Code Book -- 3.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 3.5 Descriptive Statistics for Initial Analysis of the Data -- 3.6 Quality Assurance, Data Distribution, and Tests for Normality -- 3.7 Statistical Test(s) -- 3.8 Summary of Outcomes -- 3.9 Addendum1: t-Statistic v z-Statistic -- 3.10 Addendum2: Parametric v Nonparametric -- 3.11 Addendum3: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns -- 3.12 Prepare to Exit, Save, and Later Retrieve This R Session -- 3.13 External Data and/or Data Resources Used in this Lesson -- 4 Student's t-Test for Matched Pairs -- 4.1 Background -- 4.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 4.3 Organize the Data and Display the Code Book -- 4.4 Conduct a Visual Data Check Using Graphics(e.g., Figures) -- 4.5 Descriptive Statistics for Initial Analysis of the Data -- 4.6 Quality Assurance, Data Distribution, and Tests for Normality -- 4.7 Statistical Test(s) -- 4.8 Summary of Outcomes -- 4.9 Addendum1: R-Based Tools for Unstacked (e.g. Wide) Data -- 4.10 Addendum2: Stacked Data and Student's t-Test for Matched Pairs -- 4.11 Addendum 3: The Impact of N on Student's t-Test -- 4.12 Addendum 4: Parametric v Nonparametric -- 4.13 Addendum5: Additional Practice Datasets for Data with Normal Distribution Patterns and Data That Do Not Exhibit Normal Distribution Patterns -- 4.14 Prepare to Exit, Save, and Later Retrieve This R Session -- 4.15 External Data and/or Data Resources Used in this Lesson -- 5 Oneway Analysis of Variance (ANOVA) -- 5.1 Background -- 5.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 5.3 Organize the Data and Display the Code Book -- 5.4 Conduct a Visual Data Check Using Graphics(e.g., Figures) -- 5.5 Descriptive Statistics for Initial Analysis of the Data -- 5.6 Quality Assurance, Data Distribution, and Tests for Normality -- 5.7 Statistical Test(s) -- 5.8 Summary of Outcomes -- 5.9 Addendum1: Other Packages for Display of Oneway ANOVA -- 5.10 Addendum2: Parametric v Nonparametric -- 5.11 Addendum3: Additional Practice Data Sets -- 5.12 Prepare to Exit, Save, and Later Retrieve This R Session -- 5.13 External Data and/or Data Resources Used in this Lesson -- 6 Twoway Analysis of Variance (ANOVA) -- 6.1 Background -- 6.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 6.3 Organize the Data and Display the Code Book -- 6.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 6.5 Descriptive Statistics for Initial Analysis of the Data -- 6.6 Quality Assurance, Data Distribution, and Tests for Normality -- 6.7 Statistical Test(s) -- 6.8 Summary of Outcomes -- 6.9 Addendum 1: Other Packages for Display of Twoway ANOVA -- 6.10 Addendum 2: Parametric v Nonparametric -- 6.11 Addendum 3: Additional Practice Data Sets -- 6.12 Prepare to Exit, Save, and Later Retrieve This R Session -- 6.13 External Data and/or Data Resources Used in this Lesson -- 7 Correlation, Association, Regression, Likelihood, and Prediction -- 7.1 Background -- 7.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 7.3 Organize the Data and Display the Code Book -- 7.4 Quality Assurance, Data Distribution, and Tests for Normality -- 7.5 Statistical Test(s) -- 7.6 Summary of Outcomes -- 7.7 Addendum 1: Multiple Regression -- 7.8 Addendum 2: Likelihood and Odds Ratio -- 7.9 Addendum 3:Parametric v Nonparametric -- 7.10 Addendum 4: Additional Practice Data Sets -- 7.11 Prepare to Exit, Save, and Later Retrieve This R Session -- 7.12 External Data and/or Data Resources Used in this Lesson -- 8 Working with Large and Complex Datasets -- 8.1 Background -- 8.2 Import Data in Comma-Separated Values (.csv) File Format and/or Self Generate the Data Using R-Based Functions -- 8.3 Organize the Data and Display the Code Book -- 8.4 Conduct a Visual Data Check Using Graphics (e.g., Figures) -- 8.5 Descriptive Statistics for Initial Analysis of the Data -- 8.6 Quality Assurance, Data Distribution, and Tests for Normality -- 8.7 Statistical Test(s) -- 8.8 Summary of Outcomes -- 8.9 Addendum1: Additional Graphics, to Show Relationships Between and Among Data -- 8.10 Addendum2: Graphics Using the lattice Package -- 8.11 Addendum3: Graphics Using the ggplot2 Package -- 8.12 Addendum 4: Beyond an Introduction to R - Use the tidyverse to Create Subsets of Original Datasets -- 8.13 Prepare to Exit, Save, and Later Retrieve This R Session -- 8.14 External Data and/or Data Resources Used in this Lesson -- 9 Future Actions and Next Steps -- 9.1 Use of This Text -- 9.2 R and Beautiful Reporting with R Markdown -- 9.3 Future Use of R for Biostatistics -- 9.4 Big Data and Bio Informatics -- 9.5 External Resources -- 9.6 Contact the Authors. .This book introduces the open source R software language that can be implemented in biostatistics for data organization, statistical analysis, and graphical presentation. In the years since the authors’ 2014 work Introduction to Data Analysis and Graphical Presentation in Biostatistics with R, the R user community has grown exponentially and the R language has increased in maturity and functionality. This updated volume expands upon skill-sets useful for students and practitioners in the biological sciences by describing how to work with data in an efficient manner, how to engage in meaningful statistical analyses from multiple perspectives, and how to generate high-quality graphics for professional publication of their research. A common theme for research in the diverse biological sciences is that decision-making depends on the empirical use of data. Beginning with a focus on data from a parametric perspective, the authors address topics such as Student t-Tests for independent samples and matched pairs; oneway and twoway analyses of variance; and correlation and linear regression. The authors also demonstrate the importance of a nonparametric perspective for quality assurance through chapters on the Mann-Whitney U Test, Wilcoxon Matched-Pairs Signed-Ranks test, Kruskal-Wallis H-Test for Oneway Analysis of Variance, and the Friedman Twoway Analysis of Variance. To address the element of data presentation, the book also provides an extensive review of the many graphical functions available with R. There are now perhaps more than 15,000 external packages available to the R community. The authors place special emphasis on graphics using the lattice package and the ggplot2 package, as well as less common, but equally useful, figures such as bean plots, strip charts, and violin plots. A robust package of supplementary material, as well as an introduction of the development of both R and the discipline of biostatistics, makes this ideal for novice learners as well as more experienced practitioners.BiometryData processingR (Computer program language)BiometriathubProcessament de dadesthubR (Llenguatge de programació)thubLlibres electrònicsthubBiometryData processing.R (Computer program language).BiometriaProcessament de dadesR (Llenguatge de programació)570.15195MacFarland Thomas W.721661Yates Jan M.MiAaPQMiAaPQMiAaPQBOOK996466547503316Using R for biostatistics1896712UNISA03992nam 2200541 450 991081180370332120200520144314.01-119-42068-71-119-42070-91-119-42071-7(CKB)3710000001633622(DLC) 2017030269(Au-PeEL)EBL4914179(CaPaEBR)ebr11412425(CaONFJC)MIL1021881(OCoLC)992437550(MiAaPQ)EBC4914179(EXLCZ)99371000000163362220170811h20172017 uy 0engur|||||||||||txtrdacontentnrdamediancrdacarrierReal-time embedded systems /Jiacun WangHoboken, New Jersey :Wiley,2017.©20171 online resourceQuantitative Software Engineering Series1-118-11617-8 Includes bibliographical references at the end of each chapters and index."Offering comprehensive coverage of the convergence of real-time embedded systems scheduling, resource access control, software design and development, and high-level system modeling, analysis and verification; Following an introductory overview, Dr. Wang delves into the specifics of hardware components, including processors, memory, I/O devices and architectures, communication structures, peripherals, and characteristics of real-time operating systems. Later chapters are dedicated to real-time task scheduling algorithms and resource access control policies, as well as priority-inversion control and deadlock avoidance. Concurrent system programming and POSIX programming for real-time systems are covered, as are finite state machines and Time Petri nets. Of special interest to software engineers will be the chapter devoted to model checking, in which the author discusses temporal logic and the NuSMV model checking tool, as well as a chapter treating real-time software design with UML. The final portion of the book explores practical issues of software reliability, aging, rejuvenation, security, safety, and power management. In addition, the book: Explains real-time embedded software modeling and design with finite state machines, Petri nets, and UML, and real-time constraints verification with the model checking tool, NuSMV Features real-world examples in finite state machines, model checking, real-time system design with UML, and more Covers embedded computer programing, designing for reliability, and designing for safety Explains how to make engineering trade-offs of power use and performance Investigates practical issues concerning software reliability, aging, rejuvenation, security, and power management Real-Time Embedded Systems is a valuable resource for those responsible for real-time and embedded software design, development, and management. It is also an excellent textbook for graduate courses in computer engineering, computer science, information technology, and software engineering on embedded and real-time software systems, and for undergraduate computer and software engineering courses"--Provided by publisher."This book offers comprehensive coverage of the convergence of real-time embedded systems scheduling, resource access control, software design and development, and high-level system modeling, analysis and verification"--Provided by publisher.Quantitative software engineering series.Embedded computer systemsReal-time data processingEmbedded computer systems.Real-time data processing.006.2/2TEC008070bisacshWang Jiacun1963-1372748MiAaPQMiAaPQMiAaPQBOOK9910811803703321Real-time embedded systems4010917UNINA