John Haysom and Michael Bowen provide middle and high school science teachers with more than 100 student activities to help the students develop their understanding of scientific concepts. The powerful Predict, Observe, Explain (POE) strategy, field-tested by hundreds of teachers, is designed to foster student inquiry and challenge existing conceptions that students bring to the classroom.

One of the more characteristic features of contemporary sociology is an increasing interest in theories. More and more theories are being developed in various areas of social investigation; we observe also an increasing number of verificational studies aimed primarily toward the verification of various theories. The essays presented in this volume deal with theories too, but they approach this problem from a methodological perspective. There fore it seems worthwhile in the preface to this volume to make a kind of general declaration about the author's aims and his approach to the subject of his interest, and about his view of the role of methodological reflection in the development of sciences. First let me say what methodology cannot do. It cannot be a substitute for the formulation of substantive theories, nor can it substitute for the empirical studies which confirm or reject such theories. Therefore its impact upon the development of any science, including the social sciences, is only indirect, by its undertaking the analysis of research tools and rules of scientific procedures. It can also propose certain standards for scientific procedures, but the application of these standards is the domain of substan tive researchers, and it is the substantive researchers who ultimately develop any science. Nevertheless the potential impact-of methodological reflection, even if only indirect, should not be underestimated.

The book is a summary of a time series forecasting competition that was held a number of years ago. It aims to provide a snapshot of the range of new techniques that are used to study time series, both as a reference for experts and as a guide for novices.

Based upon ten case studies, Prediction explores how science-based predictions guide policy making and what this means in terms of global warming, biogenetically modifying organisms and polluting the environment with chemicals.

Think of deep learning as an art of cooking. One way to cook is to follow a recipe. But when we learn how the food, the spices, and the fire behave, we make our creation. And an understanding of the "how" transcends the creation. Likewise, an understanding of the "how" transcends deep learning. In this spirit, this book presents the deep learning constructs, their fundamentals, and how they behave. Baseline models are developed alongside, and concepts to improve them are exemplified.

A number of approaches are being defined for statistics and machine learning. These approaches are used for the identification of the process of the system and the models created from the system’s perceived data, assisting scientists in the generation or refinement of current models. Machine learning is being studied extensively in science, particularly in bioinformatics, economics, social sciences, ecology, and climate science, but learning from data individually needs to be researched more for complex scenarios. Advanced knowledge representation approaches that can capture structural and process properties are necessary to provide meaningful knowledge to machine learning algorithms. It has a significant impact on comprehending difficult scientific problems. Prediction and Analysis for Knowledge Representation and Machine Learning demonstrates various knowledge representation and machine learning methodologies and architectures that will be active in the research field. The approaches are reviewed with real-life examples from a wide range of research topics. An understanding of a number of techniques and algorithms that are implemented in knowledge representation in machine learning is available through the book’s website. Features: Examines the representational adequacy of needed knowledge representation Manipulates inferential adequacy for knowledge representation in order to produce new knowledge derived from the original information Improves inferential and acquisition efficiency by applying automatic methods to acquire new knowledge Covers the major challenges, concerns, and breakthroughs in knowledge representation and machine learning using the most up-to-date technology Describes the ideas of knowledge representation and related technologies, as well as their applications, in order to help humankind become better and smarter This book serves as a reference book for researchers and practitioners who are working in the field of information technology and computer science in knowledge representation and machine learning for both basic and advanced concepts. Nowadays, it has become essential to develop adaptive, robust, scalable, and reliable applications and also design solutions for day-to-day problems. The edited book will be helpful for industry people and will also help beginners as well as high-level users for learning the latest things, which include both basic and advanced concepts.

Elections are random events. From individuals deciding whether to vote, to people deciding for whom to vote, to election authorities deciding what to count, the outcomes of competitive democratic elections are rarely known until election day...or beyond. Understanding Elections through Statistics: Polling, Prediction, and Testing explores this random phenomenon from two points of view: predicting the election outcome using opinion polls and testing the election outcome using government-reported data. Written for those with only a brief introduction to statistics, this book takes you on a statistical journey from how polls are taken to how they can—and should—be used to estimate current popular opinion. Once an understanding of the election process is built, we turn toward testing elections for evidence of unfairness. While holding elections has become the de facto proof of government legitimacy, those electoral processes may hide a dirty little secret of the government illicitly ensuring a favorable election outcome. This book includes these features designed to make your statistical journey more enjoyable: Vignettes of elections, including maps, to provide concrete bases for the material In-chapter cues to help one avoid the heavy math—or to focus on it End-of-chapter problems designed to review and extend that which was covered in the chapter Many opportunities to turn the power of the R statistical environment to the enclosed election data files, as well as to those you find interesting From these features, it is clear the audience for this book is quite diverse. This text provides mathematics for those interested in mathematics, but also offers detours for those who just want a good read and a deeper understanding of elections. Author Ole J. Forsberg holds PhDs in both political science and statistics. He currently teaches mathematics and statistics in the Department of Mathematics at Knox College in Galesburg, IL.

In the book "Mental Illnesses - Understanding, Prediction and Control" attention is devoted to the many background factors that are present in understanding public attitudes, immigration, stigma, and competencies surrounding mental illness. Various etiological and pathogenic factors, starting with adhesion molecules at one level and ending with abuse and maltreatment in childhood and youth at another level that are related to mental illness, include personality disorders that sit between mental health and illness. If we really understand the nature of mental illness then we should be able to not only predict but perhaps even to control it irrespective of the type of mental illness in question but also the degree of severity of the illness in order to allow us to predict their long-term outcome and begin to reduce its influence and costs to society. How can we integrate theory, research evidence, and specific ways to deal with mental illness? An attempt will be made in the last conclusive chapter of this volume.

Prediction and Change of Health Behavior honors the work of Martin Fishbein by illustrating the breadth and depth of the reasoned action approach. Focused on attitudes and their effects on health-related behavior, the book demonstrates the profound impact of Fishbein and Ajzen's theories of reasoned action on attitude research and on the solu