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Seven-week test-retest correlations were obtained for need scale scores and individual need profiles for a sample of pre-flight students. The coefficients for need scales were in the 50's and 60's (2 of 15 were below .50). Mean scores on 7 of the need scales changed significantly over the seven-week intertest interval. Of the 74 individual profile correlations, 64 were significant; the median value was .75. There was some evidence that low profile stability was related to a low consistency score on one or both administrations. The test-retest data were compared with other reliability studies of the Personal Preference Schedule. (Author).
First published in 1984. Routledge is an imprint of Taylor & Francis, an informa company.
From the beloved author of Because of Mr. Terupt comes the sequel to The Perfect Score, about a lovable group of students at Lake View Middle School and the rewards and challenges of seventh grade. These students are in for a year of secrets, discoveries, and kid power! GAVIN finally joins the football team—a dream come true!—but Coach Holmes refuses to play him for reasons that also threaten to tear Gavin's family apart. When RANDI attends an elite gymnastics camp, she uncovers a startling family connection. SCOTT starts researching an article for the school newspaper and stumbles right into a hornet's nest of lies. With his loser older brother, Brian, out of the house, TREVOR's life is loads better—until he realizes that only he can save Brian from getting into deep trouble. NATALIE's top goals: (1) find out why Mrs. Woods and Mrs. Magenta no longer speak to each other—a mission shared by all the kids—and (2) teach a certain someone an important life skill without anyone knowing. It's tough keeping secrets. And tougher still to deal with the fallout when secrets spill out.
This publication provides lessons that use history, civics, government and economics activities to bring to life the institutions students read and hear about everyday.
This two volume set of LNAI 11061 and LNAI 11062 constitutes the refereed proceedings of the 11th International Conference on Knowledge Science, Engineering and Management, KSEM 2018, held in Changchun, China, in August 2018. The 62 revised full papers and 26 short papers presented were carefully reviewed and selected from 262 submissions. The papers of the first volume are organized in the following topical sections: text mining and document analysis; image and video data analysis; data processing and data mining; recommendation algorithms and systems; probabilistic models and applications; knowledge engineering applications; and knowledge graph and knowledge management. The papers of the second volume are organized in the following topical sections: constraints and satisfiability; formal reasoning and ontologies; deep learning; network knowledge representation and learning; and social knowledge analysis and management.
The Tax and Legal Playbook: Strategies that Save You Time and Money CPA and A ttorney Mark J. Kohler answers the leading tax and legal questions facing small business owners, across all stages of business, and delivers a comprehensive playbook of clear-cut truths, thought-provoking advice, and underutilized solutions to save you time, money, and heartache. You can read this book straight through or as a reference guide—reading the chapters as needed to answer the questions as they arise. You’ll come away wiser and better equipped to make the best decisions for your business, your family, and yourself. Covers: • The ins and outs behind fundamental business decisions • Underutilized tax and legal strategies that can save you thousands • Picking the right legal entity for your business • Common legal scams and deceptions to avoid • Protecting your assets and planning for retirement • Issues are illustrated with entertaining, real-world examples
From award-winning higher education journalist and New York Times bestselling author Jeffrey Selingo comes a revealing look from inside the admissions office—one that identifies surprising strategies that will aid in the college search. Getting into a top-ranked college has never seemed more impossible, with acceptance rates at some elite universities dipping into the single digits. In Who Gets In and Why, journalist and higher education expert Jeffrey Selingo dispels entrenched notions of how to compete and win at the admissions game, and reveals that teenagers and parents have much to gain by broadening their notion of what qualifies as a “good college.” Hint: it’s not all about the sticker on the car window. Selingo, who was embedded in three different admissions offices—a selective private university, a leading liberal arts college, and a flagship public campus—closely observed gatekeepers as they made their often agonizing and sometimes life-changing decisions. He also followed select students and their parents, and he traveled around the country meeting with high school counselors, marketers, behind-the-scenes consultants, and college rankers. While many have long believed that admissions is merit-based, rewarding the best students, Who Gets In and Why presents a more complicated truth, showing that “who gets in” is frequently more about the college’s agenda than the applicant. In a world where thousands of equally qualified students vie for a fixed number of spots at elite institutions, admissions officers often make split-second decisions based on a variety of factors—like diversity, money, and, ultimately, whether a student will enroll if accepted. One of the most insightful books ever about “getting in” and what higher education has become, Who Gets In and Why not only provides an unusually intimate look at how admissions decisions get made, but guides prospective students on how to honestly assess their strengths and match with the schools that will best serve their interests.
Companion Workbook to the book Prosper Up! & Use to the Reader It is the authors intent that the workbook be downloaded as an e-book at no additional cost and be used in conjunction with the book, Prosper Up! This workbook offers a wide variety of various personal exercises, quizzes, tests, examinations, worksheets, checklists, evaluations, and calculators that are designed to be specific for each chapter of the book in order to make the learning more meaningful and individual. After the personal information is completed and entered, it can be referred to by the reader in the future and serve as an essential prosperity journal of exactly what efforts have been achieved, which ones still need to be worked on, and the ones that are needed to be improved to be able to measure your progress in order to achieve a higher level of prosperity success. Of particular use to the reader is Chapter 7 called Revealing Your Prosperity Score Number which involves a unique questionnaire which results in a measurement known as a ProsperiScore. This one-of-a-kind scorecard was created by the author over a length period of time in a style similar to a FICO credit scoring. When completed, the results will give the reader a realistic glimpse of where they are now in relation to where they desire to be in proportion to their long-term goals and objectives. In addition, it will also give the reader an accurate reading of where they measure up compared to others in similar situations, age groups, income brackets, and net worth. Along with the book, it can be used as a learning and study tool after each chapter is completed and allow the reader to personalize their individual progress. The results offer a method for each reader to share their results with others including accountants, financial planners, estate attorneys, family members, and trusted friends. It is a remarkable tool that will offer assistance in marketing and selling the book Prosper Up! As well as provide accumulated factual data that will assist the purchaser of the book a highly useful way to gain key insight into what they are doing right on their prosperity journey and what they need to continue to work on in the future.
Explore the infinite possibilities offered by Artificial Intelligence and Neural Networks KEY FEATURES ● Covers numerous concepts, techniques, best practices and troubleshooting tips by community experts. ● Includes practical demonstration of robust deep learning prediction models with exciting use-cases. ● Covers the use of the most powerful research toolkit such as Python, PyTorch, and Neural Network Intelligence. DESCRIPTION This book is amid at teaching the readers how to apply the deep learning techniques to the time series forecasting challenges and how to build prediction models using PyTorch. The readers will learn the fundamentals of PyTorch in the early stages of the book. Next, the time series forecasting is covered in greater depth after the programme has been developed. You will try to use machine learning to identify the patterns that can help us forecast the future results. It covers methodologies such as Recurrent Neural Network, Encoder-decoder model, and Temporal Convolutional Network, all of which are state-of-the-art neural network architectures. Furthermore, for good measure, we have also introduced the neural architecture search, which automates searching for an ideal neural network design for a certain task. Finally by the end of the book, readers would be able to solve complex real-world prediction issues by applying the models and strategies learnt throughout the course of the book. This book also offers another great way of mastering deep learning and its various techniques. WHAT YOU WILL LEARN ● Work with the Encoder-Decoder concept and Temporal Convolutional Network mechanics. ● Learn the basics of neural architecture search with Neural Network Intelligence. ● Combine standard statistical analysis methods with deep learning approaches. ● Automate the search for optimal predictive architecture. ● Design your custom neural network architecture for specific tasks. ● Apply predictive models to real-world problems of forecasting stock quotes, weather, and natural processes. WHO THIS BOOK IS FOR This book is written for engineers, data scientists, and stock traders who want to build time series forecasting programs using deep learning. Possessing some familiarity of Python is sufficient, while a basic understanding of machine learning is desirable but not needed. TABLE OF CONTENTS 1. Time Series Problems and Challenges 2. Deep Learning with PyTorch 3. Time Series as Deep Learning Problem 4. Recurrent Neural Networks 5. Advanced Forecasting Models 6. PyTorch Model Tuning with Neural Network Intelligence 7. Applying Deep Learning to Real-world Forecasting Problems 8. PyTorch Forecasting Package 9. What is Next?