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Predict It!

Author: Dona Herweck Rice

Publisher: Teacher Created Materials

Published: 2015-05-20

Total Pages: 34

ISBN-13: 1480750964

This high-interest informational text will help students gain science content knowledge while building their literacy skills and nonfiction reading comprehension. This appropriately leveled nonfiction science reader features hands-on, simple science experiments. Third grade students will learn all about scientific predictions through this engaging text that is aligned to the Next Generation Science Standards and supports STEM education.

Predict It!

Author: Azza Sharkawy

Publisher: Science Sleuths

Published: 2014-10-31

Total Pages: 0

ISBN-13: 9780778707738

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Scientists look for patterns to help them make predictions. This motivating title explores different patterns in the natural world, such as day and night and the changing seasons. Using their new knowledge, readers will act like scientists by identifying weather patterns and making predictions.

Predict It! Guided Reading 6-Pack

Author:

Publisher: Teacher Created Materials

Published: 2016-12-15

Total Pages: 34

ISBN-13: 1425831230

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Are you good at predicting? Have you ever made a scientific prediction? This dynamic science book will help students learn all about scientific predictions and the steps that scientists take to make good predictions. With a hands-on "Think Like a Scientist" lab activity that is aligned to the Next Generation Science Standards, this is a perfect tool to develop students' scientific practices and support STEM Education. Including a glossary and index, the helpful text features in this easy-to-read informational text support the development of content-area literacy while vibrant images keep readers engaged from cover to cover. This 6-Pack includes six copies of this Level P title and a lesson plan that specifically supports Guided Reading instruction.

Predicting the Next President

Author: Allan J. Lichtman

Publisher: Rowman & Littlefield

Published: 2024-07-01

Total Pages: 246

ISBN-13:

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In the days after Donald Trump’s unexpected victory on election night 2016, The New York Times, CNN, and other leading media outlets reached out to one of the few pundits who had correctly predicted the outcome, Allan J. Lichtman. While many election forecasters base their findings exclusively on public opinion polls, Lichtman looks at the underlying fundamentals that have driven every presidential election since 1860. Using his 13 historical factors or “keys” (four political, seven performance, and two personality), Lichtman had been predicting Trump’s win since September 2016. In the updated 2024 edition, he applies the keys to every presidential election since 1860 and shows readers the current state of the 2024 race. In doing so, he dispels much of the mystery behind electoral politics and challenges many traditional assumptions. An indispensable resource for political junkies!

Predictive Analytics

Author: Eric Siegel

Publisher: John Wiley & Sons

Published: 2016-01-13

Total Pages: 368

ISBN-13: 1119145686

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"Mesmerizing & fascinating..." —The Seattle Post-Intelligencer "The Freakonomics of big data." —Stein Kretsinger, founding executive of Advertising.com Award-winning | Used by over 30 universities | Translated into 9 languages An introduction for everyone. In this rich, fascinating — surprisingly accessible — introduction, leading expert Eric Siegel reveals how predictive analytics (aka machine learning) works, and how it affects everyone every day. Rather than a “how to” for hands-on techies, the book serves lay readers and experts alike by covering new case studies and the latest state-of-the-art techniques. Prediction is booming. It reinvents industries and runs the world. Companies, governments, law enforcement, hospitals, and universities are seizing upon the power. These institutions predict whether you're going to click, buy, lie, or die. Why? For good reason: predicting human behavior combats risk, boosts sales, fortifies healthcare, streamlines manufacturing, conquers spam, optimizes social networks, toughens crime fighting, and wins elections. How? Prediction is powered by the world's most potent, flourishing unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn. Predictive analytics (aka machine learning) unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future drives millions of decisions more effectively, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate. In this lucid, captivating introduction — now in its Revised and Updated edition — former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: What type of mortgage risk Chase Bank predicted before the recession. Predicting which people will drop out of school, cancel a subscription, or get divorced before they even know it themselves. Why early retirement predicts a shorter life expectancy and vegetarians miss fewer flights. Five reasons why organizations predict death — including one health insurance company. How U.S. Bank and Obama for America calculated the way to most strongly persuade each individual. Why the NSA wants all your data: machine learning supercomputers to fight terrorism. How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. How judges and parole boards rely on crime-predicting computers to decide how long convicts remain in prison. 182 examples from Airbnb, the BBC, Citibank, ConEd, Facebook, Ford, Google, the IRS, LinkedIn, Match.com, MTV, Netflix, PayPal, Pfizer, Spotify, Uber, UPS, Wikipedia, and more. How does predictive analytics work? This jam-packed book satisfies by demystifying the intriguing science under the hood. For future hands-on practitioners pursuing a career in the field, it sets a strong foundation, delivers the prerequisite knowledge, and whets your appetite for more. A truly omnipresent science, predictive analytics constantly affects our daily lives. Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.

Modeling, Evaluating, and Predicting IT Human Resources Performance

Author: Konstantina Richter

Publisher: CRC Press

Published: 2015-03-06

Total Pages: 272

ISBN-13: 1482299933

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Numerous methods exist to model and analyze the different roles, responsibilities, and process levels of information technology (IT) personnel. However, most methods neglect to account for the rigorous application and evaluation of human errors and their associated risks. This book fills that need. Modeling, Evaluating, and Predicting IT Human Resources Performance explains why it is essential to account for the human factor when determining the various risks in the software engineering process. The book presents an IT human resources evaluation approach that is rooted in existing research and describes how to enhance existing approaches through strict use of software measurement and statistical principles and criteria. Discussing IT human factors from a risk assessment point of view, the book identifies, analyzes, and evaluates the basics of IT human performance. It details the IT human factors required to achieve desired levels of human performance prediction. It also provides a rigorous investigation of existing human factors evaluation methods, including IT expertise and Big Five, in combination with powerful statistical methods, such as failure mode and effect analysis (FMEA) and design of experiment (DoE). Supplies an overview of existing methods of human risk evaluation Provides a detailed analysis of IT role-based human factors using the well-known Big Five method for software engineering Models the human factor as a risk factor in the software engineering process Summarizes emerging trends and future directions In addition to applying well-known human factors methods to software engineering, the book presents three models for analyzing psychological characteristics. It supplies profound analysis of human resources within the various software processes, including development, maintenance, and application under consideration of the Capability Maturity Model Integration (CMMI) process level five.

Will He Go?

Author: Lawrence Douglas

Publisher: Twelve

Published: 2020-05-19

Total Pages: 120

ISBN-13: 1538751879

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In advance of the 2020 election, legal scholar Lawrence Douglas prepares readers for a less-than-peaceful transition of power. It doesn't require a strong imagination to get a sense of the mayhem Trump will unleash if he loses a closely contested election. It is no less disturbing to imagine Trump still insisting that he is the rightful leader of the nation. With millions of diehard supporters firmly believing that their revered president has been toppled by malignant forces of the Deep State, Trump could remain a force of constitutional chaos for years to come. WILL TRUMP GO? addresses such questions as: How might Trump engineer his refusal to acknowledge electoral defeat? What legal and extra-legal paths could he pursue in mobilizing a challenge to the electoral outcome? What legal, political, institutional, and popular mechanisms can be used to stop him? What would be the fallout of a failure to remove him from office? What would be the fallout of a successful effort to unseat him? Can our democracy snap back from Trump? Trump himself has essentially told the nation he will never accept electoral defeat. A book that prepares us for Trump's refusal to concede, then, is hardly speculative; it is a necessary precaution against a coming crisis.

Forecasting: principles and practice

Author: Rob J Hyndman

Publisher: OTexts

Published: 2018-05-08

Total Pages: 380

ISBN-13: 0987507117

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Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.

The Elements of Statistical Learning

Author: Trevor Hastie

Publisher: Springer Science & Business Media

Published: 2013-11-11

Total Pages: 545

ISBN-13: 0387216065

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During the past decade there has been an explosion in computation and information technology. With it have come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book. This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorization, and spectral clustering. There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie co-developed much of the statistical modeling software and environment in R/S-PLUS and invented principal curves and surfaces. Tibshirani proposed the lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, projection pursuit and gradient boosting.