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Using Formative Assessment to Differentiate Mathematics Instruction, Grades 4–10: Seven Practices to Maximize Learning
by Leslie E. LaudSeven easy steps to differentiating math instruction for busy teachers Staff development expert Leslie Laud provides a clear roadmap for using formative assessment to differentiate mathematics instruction for students in Grades 4–10. She presents a comprehensive framework of seven research-based practices that show teachers how to: Get started and establish norms Implement formative assessment Create tiered lessons Manage a multitasking classroom effectively Tested, reviewed, and enhanced by experienced math teachers, the book includes practical examples, reproducibles, and student activities that are easy for busy teachers to implement immediately.
Using Formative Assessment to Drive Mathematics Instruction in Grades 3-5
by Jennifer Taylor-Cox Christine OberdorfProvide targeted mathematics instruction for every child. These books combine formative assessment with practical activities to differentiate the elementary classroom. The formative assessments include student work samples at varying levels. The authors... Illustrate the distinction between a "traditional" assessment and an "enhanced" assessment. Describe specific differentiated activities so each student may consistently receive instruction geared to specific need. Provide teachers with "Questions to Assess" to determine what each child understands about the math concept. Show how to move students to higher-level mathematics thinking and to apply math concepts. Include extension activities to offer challenging work for children who have achieved skill mastery level. Each activity states a goal, the materials needed, a description of the activity, as well as specific questions to ask students. The assessments and activities are aligned with the Common Core State Standards for Mathematics and the expectations described by the National Council of Teachers of Mathematics.This resource will help teachers, principals, and curriculum directors identify students' levels of understanding about mathematics and provide concrete resources for remediation, instruction, and enrichment. These books are also an excellent resource for use during workshops and in-class observations.
Using Fundamental Analysis and an Ensemble of Classifier Models Along with a Risk-Off Filter to Select Outperforming Companies (Synthesis Lectures on Technology Management & Entrepreneurship)
by Rui Neves Manuel MouraThis book develops a quantitative stock market investment methodology using financial indicators that beats the benchmark of S&P500 index. To achieve this goal, an ensemble of machine learning models is meticulously constructed, incorporating four distinct algorithms: support vector machine, k-nearest neighbors, random forest, and logistic regression. These models all make use of financial ratios extracted from company financial statements for the purposes of predictive forecasting. The ensemble classifier is subject to a strict testing of precision which compares it to the performance of its constituent models separately. Rolling window and cross-validation tests are used in this evaluation in order to provide a comprehensive assessment framework. A risk-off filter is developed to limit risk during uncertain market periods, and consequently to improve the Sharpe ratio of the model. The risk adjusted performance of the final model, supported by the risk-off filter, achieves a Sharpe ratio of 1.63 which surpasses both the model’s performance without the filter that delivers Sharpe ratio of 1.41 and the one from the S&P500 index of 0.80. The substantial increase in risk-adjusted returns is accomplished by reducing the model’s volatility from an annual standard of deviation of 15.75% to 11.22%, which represents an almost 30% decrease in volatility.
Using Game Theory to Improve Safety within Chemical Industrial Parks
by Genserik Reniers Yulia PavlovaThough the game-theoretic approach has been vastly studied and utilized in relation to economics of industrial organizations, it has hardly been used to tackle safety management in multi-plant chemical industrial settings. Using Game Theory for Improving Safety within Chemical Industrial Parks presents an in-depth discussion of game-theoretic modeling which may be applied to improve cross-company prevention and -safety management in a chemical industrial park. By systematically analyzing game-theoretic models and approaches in relation to managing safety in chemical industrial parks, Using Game Theory for Improving Safety within Chemical Industrial Parks explores the ways game theory can predict the outcome of complex strategic investment decision making processes involving several adjacent chemical plants. A number of game-theoretic decision models are discussed to provide strategic tools for decision-making situations. Offering clear and straightforward explanations of methodologies, Using Game Theory for Improving Safety within Chemical Industrial Parks provides managers and management teams with approaches to asses situations and to improve strategic safety- and prevention arrangements.
Using IBM® SPSS® Statistics: An Interactive Hands-On Approach
by James O. AldrichUsing IBM® SPSS® Statistics: An Interactive Hands-On Approach, Third Edition gives readers an accessible and comprehensive guide to walking through SPSS®, providing them with step-by-step knowledge for effectively analyzing their data. From entering data to working with existing databases, and working with the help menu through performing factor analysis, Using IBM® SPSS® Statistics covers every aspect of SPSS® from introductory through intermediate statistics. The book is divided into parts that focus on mastering SPSS® basics, dealing with univariate statistics and graphing, inferential statistics, relational statistics, and more. Written using IBM® SPSS® version 25 and 24, and compatible with the earlier releases, this book is one of the most comprehensive SPSS® guides available.
Using IBM® SPSS® Statistics: An Interactive Hands-On Approach
by James O. AldrichUsing IBM® SPSS® Statistics: An Interactive Hands-On Approach, Third Edition gives readers an accessible and comprehensive guide to walking through SPSS®, providing them with step-by-step knowledge for effectively analyzing their data. From entering data to working with existing databases, and working with the help menu through performing factor analysis, Using IBM® SPSS® Statistics covers every aspect of SPSS® from introductory through intermediate statistics. The book is divided into parts that focus on mastering SPSS® basics, dealing with univariate statistics and graphing, inferential statistics, relational statistics, and more. Written using IBM® SPSS® version 25 and 24, and compatible with the earlier releases, this book is one of the most comprehensive SPSS® guides available.
Using IBM® SPSS® Statistics for Research Methods and Social Science Statistics
by Dr William E. WagnerReliable and student-friendly, Using IBM® SPSS® Statistics for Research Methods and Social Science Statistics by William E. Wagner, III is known for its effectiveness in helping readers learn to use SPSS software for simple data management. Now reflecting SPSS Version 23.0, the Sixth Edition includes updated examples, screenshots, and tables based on current GSS (General Social Survey) data. This manual is an excellent companion to any undergraduate social statistics and research methods text and is ideal as a stand-alone guide for those learning to use SPSS software for the first time.
Using IBM® SPSS® Statistics for Research Methods and Social Science Statistics
by Dr William E. WagnerReliable and student-friendly, Using IBM® SPSS® Statistics for Research Methods and Social Science Statistics by William E. Wagner, III is known for its effectiveness in helping readers learn to use SPSS software for simple data management. Now reflecting SPSS Version 23.0, the Sixth Edition includes updated examples, screenshots, and tables based on current GSS (General Social Survey) data. This manual is an excellent companion to any undergraduate social statistics and research methods text and is ideal as a stand-alone guide for those learning to use SPSS software for the first time.
Using IBM® SPSS® Statistics for Research Methods and Social Science Statistics
by Dr. William E. WagnerUsing IBM SPSS for Social Statistics and Research Methods supports the use of SPSS for social statistics and research methods classes and is an excellent companion to any undergraduate statistics or research methods textbook. The book covers a wide range of data analysis topics to help students working on papers, research projects, and proposals. Using examples, tables, and actual SPSS screen captures, along with current data sets from the General Social Survey, it guides users through several different kinds of SPSS files including data files, output files, and syntax files.
Using IBM® SPSS® Statistics for Research Methods and Social Science Statistics
by Dr. William E. WagnerUsing IBM SPSS for Social Statistics and Research Methods supports the use of SPSS for social statistics and research methods classes and is an excellent companion to any undergraduate statistics or research methods textbook. The book covers a wide range of data analysis topics to help students working on papers, research projects, and proposals. Using examples, tables, and actual SPSS screen captures, along with current data sets from the General Social Survey, it guides users through several different kinds of SPSS files including data files, output files, and syntax files.
Using Information Technology in Mathematics Education
by James Tooke Norma HendersonComputers have changed the ways that mathematics are taught and learned. Is your institution taking advantage of what today's technology offers?With contributions from researchers and practitioners alike, Using Information Technology in Mathematics Education explores the impact of the computer on the curriculum, the teaching and learning of mathematics, and the professional development of teachers, both pre-service and in-service.As editor James Tooke states: “The connection between mathematics and the computer is obvious. Elementary notions of mathematics gave rise to the computer; advanced notions gave it a more powerful state. As the computer advanced, it expanded mathematics, allowing the creation of further branches of the field; for instance, fractal geometry had no reality until the advent of high-speed computers.”In its look at the relationship between mathematics, the computer, and mathematics education, Using Information Technology in Mathematics Education: addresses the computer as a vehicle for teaching calculus at Texas A&M includes reports from several programs that have utilized the computer when teaching mathematics at lower levels of content than calculus such as intermediate algebra and geometry examines the computer's role in student learning probability discusses the use of computers in the professional development of teachers explores ways to use computers to reduce mathematics anxietyUsing Information Technology in Mathematics Education examines the history and impact of computers in mathematics and mathematics education--from the early, crude computer-assisted instruction efforts through LOGO software for elementary schools, through MAPLE for the university, to the Web-based calculus courses now being offered by outstanding universities. Use it to facilitate learning and teacher growth in your institution!
Using & Interpreting Statistics: A Practical Text for the Behavioral, Social, and Health Sciences,2nd Edition
by Eric W. CortyEric Corty’s engaging, easy-to-understand textbook focuses on the needs of behavioral science students encountering statistical practices for the first time. An award-winning master teacher, Corty speaks to students in their language, with an approachable voice that conveys the basics of statistics step-by-step. Examples come from the behavioral and social sciences, as well as from recognizable aspects of everyday life to help students see the relevance of statistics.
Using Mathematica for Quantum Mechanics: A Student’s Manual
by Roman SchmiedThis book revisits many of the problems encountered in introductory quantum mechanics, focusing on computer implementations for finding and visualizing analytical and numerical solutions. It subsequently uses these implementations as building blocks to solve more complex problems, such as coherent laser-driven dynamics in the Rubidium hyperfine structure or the Rashba interaction of an electron moving in 2D. The simulations are highlighted using the programming language Mathematica. No prior knowledge of Mathematica is needed; alternatives, such as Matlab, Python, or Maple, can also be used.
Using Mathematics to Understand Biological Complexity: From Cells to Populations (Association for Women in Mathematics Series #22)
by Rebecca Segal Blerta Shtylla Suzanne SindiThis volume tackles a variety of biological and medical questions using mathematical models to understand complex system dynamics. Working in collaborative teams of six, each with a senior research mentor, researchers developed new mathematical models to address questions in a range of application areas. Topics include retinal degeneration, biopolymer dynamics, the topological structure of DNA, ensemble analysis, multidrug-resistant organisms, tumor growth modeling, and geospatial modeling of malaria. The work is the result of newly formed collaborative groups begun during the Collaborative Workshop for Woman in Mathematical Biology hosted by the Institute of Pure and Applied Mathematics at UCLA in June 2019. Previous workshops in this series have occurred at IMA, NIMBioS, and MBI.
Using Mobile Technologies in the Teaching and Learning of Mathematics (Mathematics Education in the Digital Era #12)
by Nigel Calder Kevin Larkin Nathalie SinclairMobile technologies influence the way that we interact with the world, the way that we live. We use them for communication, entertainment, information and research. In education settings, there has been substantial investment in mobile devices, often without a concomitant investment in developing pedagogy and practices. With mobile technologies evolving rapidly, and the number of educational apps growing, there is a need for research into how they facilitate mathematics learning. Such research is of particular importance regarding how such devices may be used to open up new ways of envisaging mathematics and mathematics education, and to help develop conceptual rather than procedural or declarative knowledge. This volume draws upon international research and reports on a range of research projects that have incorporated mobile technologies for mathematics education. It presents research on the use of mobile technologies, such as iPads, iPods, iPhones, Androids, and Tablets, across a diverse range of cultures, year levels and contexts. It examines the ways in which mobile technologies, including apps, might influence students’ engagement, cognition, collaboration and attitudes, through the reshaping of the learning experience. In addition, the book presents appropriate ways to integrate mobile technologies into teaching and learning programmes. It is a significant reference book for those involved with teaching mathematics or using mobile technologies in education, while also offering insights and examples that are applicable to the use of digital technologies in education generally.
Using Multivariate Statistics Sixth Edition
by Barbara G. Tabachnick Linda S. FidellThe book 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.
Using Predictive Analytics to Improve Healthcare Outcomes
by John W. NelsonUsing Predictive Analytics to Improve Healthcare Outcomes Discover a comprehensive overview, from established leaders in the field, of how to use predictive analytics and other analytic methods for healthcare quality improvement.Using Predictive Analytics to Improve Healthcare Outcomes delivers a 16-step process to use predictive analytics to improve operations in the complex industry of healthcare. The book includes numerous case studies that make use of predictive analytics and other mathematical methodologies to save money and improve patient outcomes. The book is organized as a “how-to” manual, showing how to use existing theory and tools to achieve desired positive outcomes.You will learn how your organization can use predictive analytics to identify the most impactful operational interventions before changing operations. This includes: A thorough introduction to data, caring theory, Relationship-Based Care®, the Caring Behaviors Assurance System©, and healthcare operations, including how to build a measurement model and improve organizational outcomes. An exploration of analytics in action, including comprehensive case studies on patient falls, palliative care, infection reduction, reducing rates of readmission for heart failure, and more—all resulting in action plans allowing clinicians to make changes that have been proven in advance to result in positive outcomes.Discussions of how to refine quality improvement initiatives, including the use of “comfort” as a construct to illustrate the importance of solid theory and good measurement in adequate pain management.An examination of international organizations using analytics to improve operations within cultural context.Using Predictive Analytics to Improve Healthcare Outcomes is perfect for executives, researchers, and quality improvement staff at healthcare organizations, as well as educators teaching mathematics, data science, or quality improvement. Employ this valuable resource that walks you through the steps of managing and optimizing outcomes in your clinical care operations.
Using R and RStudio for Data Management, Statistical Analysis, and Graphics
by Nicholas J. Horton Ken KleinmanIncorporating the latest R packages as well as new case studies and applications, Using R and RStudio for Data Management, Statistical Analysis, and Graphics, Second Edition covers the aspects of R most often used by statistical analysts. New users of R will find the book's simple approach easy to understand while more sophisticated users will appreciate the invaluable source of task-oriented information.New to the Second Edition: The use of RStudio, which increases the productivity of R users and helps users avoid error-prone cut-and-paste workflows New chapter of case studies illustrating examples of useful data management tasks, reading complex files, making and annotating maps, "scraping" data from the web, mining text files, and generating dynamic graphics New chapter on special topics that describes key features, such as processing by group, and explores important areas of statistics, including Bayesian methods, propensity scores, and bootstrapping New chapter on simulation that includes examples of data generated from complex models and distributions A detailed discussion of the philosophy and use of the knitr and markdown packages for R New packages that extend the functionality of R and facilitate sophisticated analyses Reorganized and enhanced chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots Easily Find Your Desired TaskConveniently organized by short, clear descriptive entries, this edition continues to show users how to easily perform an analytical task in R. Users can quickly find and implement the material they need through the extensive indexing, cross-referencing, and worked examples in the text. Datasets and code are available for download on a supplementary website.
Using R for Bayesian Spatial and Spatio-Temporal Health Modeling (Chapman & Hall/CRC The R Series)
by Andrew B. LawsonProgressively more and more attention has been paid to how location affects health outcomes. The area of disease mapping focusses on these problems, and the Bayesian paradigm has a major role to play in the understanding of the complex interplay of context and individual predisposition in such studies of disease. Using R for Bayesian Spatial and Spatio-Temporal Health Modeling provides a major resource for those interested in applying Bayesian methodology in small area health data studies. Features: Review of R graphics relevant to spatial health data Overview of Bayesian methods and Bayesian hierarchical modeling as applied to spatial data Bayesian Computation and goodness-of-fit Review of basic Bayesian disease mapping models Spatio-temporal modeling with MCMC and INLA Special topics include multivariate models, survival analysis, missing data, measurement error, variable selection, individual event modeling, and infectious disease modeling Software for fitting models based on BRugs, Nimble, CARBayes and INLA Provides code relevant to fitting all examples throughout the book at a supplementary website The book fills a void in the literature and available software, providing a crucial link for students and professionals alike to engage in the analysis of spatial and spatio-temporal health data from a Bayesian perspective using R. The book emphasizes the use of MCMC via Nimble, BRugs, and CARBAyes, but also includes INLA for comparative purposes. In addition, a wide range of packages useful in the analysis of geo-referenced spatial data are employed and code is provided. It will likely become a key reference for researchers and students from biostatistics, epidemiology, public health, and environmental science.
Using R for Biostatistics
by Thomas W. MacFarland Jan M. YatesThis 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.
Using R for Digital Soil Mapping
by Brendan P. Malone Budiman Minasny Alex B. McbratneyThis book describes and provides many detailed examples of implementing Digital Soil Mapping (DSM) using R. The work adheres to Digital Soil Mapping theory, and presents a strong focus on how to apply it. DSM exercises are also included and cover procedures for handling and manipulating soil and spatial data in R. The book also introduces the basic concepts and practices for building spatial soil prediction functions, and then ultimately producing digital soil maps.
Using R for Introductory Statistics (Chapman & Hall/CRC The R Series)
by John VerzaniThe second edition of a bestselling textbook, Using R for Introductory Statistics guides students through the basics of R, helping them overcome the sometimes steep learning curve. The author does this by breaking the material down into small, task-oriented steps. The second edition maintains the features that made the first edition so popular, while updating data, examples, and changes to R in line with the current version. See What’s New in the Second Edition: Increased emphasis on more idiomatic R provides a grounding in the functionality of base R. Discussions of the use of RStudio helps new R users avoid as many pitfalls as possible. Use of knitr package makes code easier to read and therefore easier to reason about. Additional information on computer-intensive approaches motivates the traditional approach. Updated examples and data make the information current and topical. The book has an accompanying package, UsingR, available from CRAN, R’s repository of user-contributed packages. The package contains the data sets mentioned in the text (data(package="UsingR")), answers to selected problems (answers()), a few demonstrations (demo()), the errata (errata()), and sample code from the text. The topics of this text line up closely with traditional teaching progression; however, the book also highlights computer-intensive approaches to motivate the more traditional approach. The authors emphasize realistic data and examples and rely on visualization techniques to gather insight. They introduce statistics and R seamlessly, giving students the tools they need to use R and the information they need to navigate the sometimes complex world of statistical computing.
Using R for Numerical Analysis in Science and Engineering (Chapman & Hall/CRC The R Series)
by Victor A. BloomfieldInstead of presenting the standard theoretical treatments that underlie the various numerical methods used by scientists and engineers, Using R for Numerical Analysis in Science and Engineering shows how to use R and its add-on packages to obtain numerical solutions to the complex mathematical problems commonly faced by scientists and engineers. This practical guide to the capabilities of R demonstrates Monte Carlo, stochastic, deterministic, and other numerical methods through an abundance of worked examples and code, covering the solution of systems of linear algebraic equations and nonlinear equations as well as ordinary differential equations and partial differential equations. It not only shows how to use R’s powerful graphic tools to construct the types of plots most useful in scientific and engineering work, but also: Explains how to statistically analyze and fit data to linear and nonlinear models Explores numerical differentiation, integration, and optimization Describes how to find eigenvalues and eigenfunctions Discusses interpolation and curve fitting Considers the analysis of time series Using R for Numerical Analysis in Science and Engineering provides a solid introduction to the most useful numerical methods for scientific and engineering data analysis using R.
Using R for Statistics
by Sarah StowellUsing R for Statistics will get you the answers to most of the problems you are likely to encounter when using a variety of statistics. This book is a problem-solution primer for using R to set up your data, pose your problems and get answers using a wide array of statistical tests. The book walks you through R basics and how to use R to accomplish a wide variety statistical operations. You'll be able to navigate the R system, enter and import data, manipulate datasets, calculate summary statistics, create statistical plots and customize their appearance, perform hypothesis tests such as the t-tests and analyses of variance, and build regression models. Examples are built around actual datasets to simulate real-world solutions, and programming basics are explained to assist those who do not have a development background. After reading and using this guide, you'll be comfortable using and applying R to your specific statistical analyses or hypothesis tests. No prior knowledge of R or of programming is assumed, though you should have some experience with statistics. What you'll learn How to apply statistical concepts using R and some R programming How to work with data files, prepare and manipulate data, and combine and restructure datasets How to summarize continuous and categorical variables What is a probability distribution How to create and customize plots How to do hypothesis testing How to build and use regression and linear models Who this book is for No prior knowledge of R or of programming is assumed, making this book ideal if you are more accustomed to using point-and-click style statistical packages. You should have some prior experience with statistics, however. Table of Contents 1. R Fundamentals 2. Working with Data Files 3. Preparing and Manipulating Data 4. Combining and Restructuring Data Sets 5. Continuous Variables 6. Tabular Data 7. Probability Distribution 8. Creating Plots 9. Customizing Plots 10. Hypothesis Tests 11. Regression and Linear Models 12. Appendix A: Basic Programming with R 13. Appendix B: Add-on Packages 14: Appendix C: Data Sets
Using R for Trade Policy Analysis: R Codes for the UNCTAD and WTO Practical Guide (SpringerBriefs in Economics)
by Massimiliano PortoThis book explains the best practices of the UNCTAD & WTO for trade analysis to the R users community. It shows how to replicate the UNCTAD & WTO's Stata codes in the Practical Guide to Trade Policy Analysis by using R. Applications and exercises are chosen from the Practical Guide to Trade Policy Analysis and explain how to implement the codes in R. This books targets readers with a basic knowledge of R. It is particularly suitable for Stata users.