Na Klar! 1 - Resource and Assessment File PDF Download

Author: Geoff Brammall
Publisher: Nelson Thornes
ISBN: 0748778454
Category : German language
Languages : en
Pages : 148

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Book Description
Na klar! is the German course for the Key Stage 3 National Framework. Na klar! is a three-stage course covering all requirements up to Key Stage 4. At Key Stage 4 the course is particularly appropriate for the AQA specifications.

Author: Michael Spencer
Publisher: Nelson Thornes
ISBN: 074877839X
Category : German language
Languages : de
Pages : 150

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Book Description
Na klar! is the German course for the Key Stage 3 National Framework. Na klar! is a three-stage course covering all requirements up to Key Stage 4. At Key Stage 4 the course is particularly appropriate for the AQA specifications.

Author: Colin Christie
Publisher: Nelson Thornes
ISBN: 9780748778300
Category : German language
Languages : de
Pages : 214

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Book Description
Na klar! is a three-stage course covering all requirements up to Key Stage 4. At Key Stage 4 the course is particularly appropriate for the AQA specifications. Na klar! is part of a series of three brand new Modern Foreign Languages courses for the National Framework with Voil a! for French and As ! for Spanish.

Author: Colin Christie
Publisher: Nelson Thornes
ISBN: 9780748794218
Category :
Languages : en
Pages : 424

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Book Description
Serving as a KS4 course, this title focuses on knowledge and skills required for success at GCSE. This evaluation pack contains a student workbook, teacher file and samples of the audio, copymasters and resource and assessment file.

Author: Michael Spencer
Publisher: Nelson Thornes
ISBN: 9780748791590
Category : Foreign Language Study
Languages : de
Pages : 166

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Book Description
Don't let them drop German! A major growing concern in schools is the decline in the number of language students and the impact this could have on your department. Which is why we've developed Na klar! - the only course to captivate all your students and help keep German alive in your school!

Author: National Research Council
Publisher: National Academies Press
ISBN: 0309134412
Category : Political Science
Languages : en
Pages : 322

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Book Description
Federal agencies have taken steps to include the public in a wide range of environmental decisions. Although some form of public participation is often required by law, agencies usually have broad discretion about the extent of that involvement. Approaches vary widely, from holding public information-gathering meetings to forming advisory groups to actively including citizens in making and implementing decisions. Proponents of public participation argue that those who must live with the outcome of an environmental decision should have some influence on it. Critics maintain that public participation slows decision making and can lower its quality by including people unfamiliar with the science involved. This book concludes that, when done correctly, public participation improves the quality of federal agencies' decisions about the environment. Well-managed public involvement also increases the legitimacy of decisions in the eyes of those affected by them, which makes it more likely that the decisions will be implemented effectively. This book recommends that agencies recognize public participation as valuable to their objectives, not just as a formality required by the law. It details principles and approaches agencies can use to successfully involve the public.

Author: Arthur James Wells
Publisher:
ISBN:
Category : Bibliography, National
Languages : en
Pages : 1674

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Book Description

Author: Peter Bruce
Publisher: "O'Reilly Media, Inc."
ISBN: 1491952911
Category : Computers
Languages : en
Pages : 395

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Book Description
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not. Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format. With this book, you’ll learn: Why exploratory data analysis is a key preliminary step in data science How random sampling can reduce bias and yield a higher quality dataset, even with big data How the principles of experimental design yield definitive answers to questions How to use regression to estimate outcomes and detect anomalies Key classification techniques for predicting which categories a record belongs to Statistical machine learning methods that “learn” from data Unsupervised learning methods for extracting meaning from unlabeled data

Author: William R. Shadish
Publisher: Cengage Learning
ISBN:
Category : Education
Languages : en
Pages : 664

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Book Description
Sections include: experiments and generalised causal inference; statistical conclusion validity and internal validity; construct validity and external validity; quasi-experimental designs that either lack a control group or lack pretest observations on the outcome; quasi-experimental designs that use both control groups and pretests; quasi-experiments: interrupted time-series designs; regresssion discontinuity designs; randomised experiments: rationale, designs, and conditions conducive to doing them; practical problems 1: ethics, participation recruitment and random assignment; practical problems 2: treatment implementation and attrition; generalised causal inference: a grounded theory; generalised causal inference: methods for single studies; generalised causal inference: methods for multiple studies; a critical assessment of our assumptions.

Author: Max Kuhn
Publisher: Springer Science & Business Media
ISBN: 1461468493
Category : Medical
Languages : en
Pages : 600

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Book Description
Applied Predictive Modeling covers the overall predictive modeling process, beginning with the crucial steps of data preprocessing, data splitting and foundations of model tuning. The text then provides intuitive explanations of numerous common and modern regression and classification techniques, always with an emphasis on illustrating and solving real data problems. The text illustrates all parts of the modeling process through many hands-on, real-life examples, and every chapter contains extensive R code for each step of the process. This multi-purpose text can be used as an introduction to predictive models and the overall modeling process, a practitioner’s reference handbook, or as a text for advanced undergraduate or graduate level predictive modeling courses. To that end, each chapter contains problem sets to help solidify the covered concepts and uses data available in the book’s R package. This text is intended for a broad audience as both an introduction to predictive models as well as a guide to applying them. Non-mathematical readers will appreciate the intuitive explanations of the techniques while an emphasis on problem-solving with real data across a wide variety of applications will aid practitioners who wish to extend their expertise. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis. While the text is biased against complex equations, a mathematical background is needed for advanced topics.