This book is designed to help Education students gain confidence in analysing and interpreting quantitative data and using appropriate statistical tests, by exploring, in plain language, a variety of data analysis methods.\nThis book is designed to help Education students gain confidence in analysing and interpreting quantitative data and using appropriate statistical tests, by exploring, in plain language, a variety of data analysis methods. Highly practical, each chapter includes step-by-step instructions on how to run specific statistical tests using R, practical tips on how to interpret results correctly and exercises to put into practice what students have learned. It also includes guidance on how to use R and RStudio, how to visualise quantitative data, and the fundamentals of inferential statistics, estimations and hypothesis testing. Nicoleta Gaciu is Senior Lecturer in Education at Oxford Brookes University.
UX Knowledge Base Sketch #56
In an era defined by an overwhelming influx of information, data has become the lifeblood of research, innovation, and decision-making. Researchers and professionals often find themselves swimming in…
Are you looking for ideas about how to hold your students accountable during book clubs?
Use of quantitative data, especially in financial markets, may provide rapid results due to the ease-of-use and availability of fast computational software, but this book advises caution and helps to understand and avoid potential pitfalls.It deals with often underestimated issues related to the use of financial quantitative data, such as non-stationarity issues, accuracy issues and modeling issues. It provides practical remedies or ways to develop new calculation methodologies to avoid pitfalls in using data, as well as solutions for risk management issues in financial market. The book is intended to help professionals in financial industry to use quantitative data in a safer way.
Quantitative data collection refers to the collection of numerical data that can be analyzed using statistical methods
Are you planning an introduction to variability unit? Want a fun lesson on generating statistical questions? These categorical data and numerical data guided notes, exit ticket, homework, and writing prompt focus on distinguishing between data through word analysis and generating statistical questions.These materials will engage kids as they learn about this important math concept. Answer keys are included. (1) Guided notes: information that gets straight to the point in easy-to-understand language and visuals. (2) Exit ticket: small, but effective tool to assess student learning. (3) Homework: reinforce the lesson at home. (4) Quick write: Numerical data and categorical data Each purchase of the Categorical Data and Numerical Data is for individual use only. The individual licensee can make copies for one classroom only. Licensee is not permitted to upload this product to the Internet for any reason. Additional licenses are available on TeachersPayTeachers.com/ © 2015 NJM All Rights Reserved
As teachers, we know that we are ALWAYS assessing, and using that information to guide our instruction. With the help of tech, that has never been easier.
What is the difference between Quantitative and Qualitative Research? Quantitative research is known to be objective. Qualitative research is known to be ..
In this activity, students can look inside the model that resembles the atom and find information that reinforces what an isotope actually is. Furthermore, the quantitative data forces students to examine beliefs about different types of averages and what the numbers really mean. This takes a bit of effort to set up but is inexpensive and can be used year after year.
We all know and love Kahoot, right? If you do not know about Kahoot then we have a few blog posts with tips and ideas to get you up to sp...
Qualitative research relies on oral or written data to arrive at conclusions. Qualitative research takes into account logic and statistics.
Understanding Research in Early Childhood Education: Quantitative and Qualitative Methods prepares readers to be informed consumers of early childhood research. Rather than following the traditional format of covering quantitative and qualitative methods separately, this innovative textbook offers side-by-side coverage and comparison about the assumptions, questions, purposes and methods for each, offering unique perspectives for understanding young children and early care and education programs. Understanding Research in Early Childhood Education is broadly based across the major research paradigms, and numerous examples are offered throughout the text. Through the use of this book, students will be able to more knowledgeably read, evaluate, and use empirical literature. These skills are becoming more important as early childhood educators are increasingly expected to use evidence-based research in practice and to participate in collecting and analyzing data to inform their teaching.
Implement machine learning, time-series analysis, algorithmic trading and moreAbout This Book- Understand the basics of R and how they can be applied in various Quantitative Finance scenarios- Learn various algorithmic trading techniques and ways to optimize them using the tools available in R.- Contain different methods to manage risk and explore trading using Machine Learning.Who This Book Is ForIf you want to learn how to use R to build quantitative finance models with ease, this book is for you. Analysts who want to learn R to solve their quantitative finance problems will also find this book useful. Some understanding of the basic financial concepts will be useful, though prior knowledge of R is not required.What You Will Learn- Get to know the basics of R and how to use it in the field of Quantitative Finance- Understand data processing and model building using R- Explore different types of analytical techniques such as statistical analysis, time-series analysis, predictive modeling, and econometric analysis- Build and analyze quantitative finance models using real-world examples- How real-life examples should be used to develop strategies- Performance metrics to look into before deciding upon any model- Deep dive into the vast world of machine-learning based trading- Get to grips with algorithmic trading and different ways of optimizing it- Learn about controlling risk parameters of financial instrumentsIn DetailThe role of a quantitative analyst is very challenging, yet lucrative, so there is a lot of competition for the role in top-tier organizations and investment banks. This book is your go-to resource if you want to equip yourself with the skills required to tackle any real-world problem in quantitative finance using the popular R programming language.You'll start by getting an understanding of the basics of R and its relevance in the field of quantitative finance. Once you've built this foundation, we'll dive into the practicalities of building financial models in R. This will help you have a fair understanding of the topics as well as their implementation, as the authors have presented some use cases along with examples that are easy to understand and correlate.We'll also look at risk management and optimization techniques for algorithmic trading. Finally, the book will explain some advanced concepts, such as trading using machine learning, optimizations, exotic options, and hedging.By the end of this book, you will have a firm grasp of the techniques required to implement basic quantitative finance models in R.Style and approachThis book introduces you to the essentials of quantitative finance with the help of easy-to-understand, practical examples and use cases in R. Each chapter presents a specific financial concept in detail, backed with relevant theory and the implementation of a real-life example. | Author: Dr Param Jeet, Prashant Vats | Publisher: Packt Publishing | Publication Date: Mar 28, 2017 | Number of Pages: 284 pages | Language: English | Binding: Paperback | ISBN-10: 1786462419 | ISBN-13: 9781786462411
Whether they're completed on a piece of paper, a tablet, or a whiteboard, exit tickets are a powerful tool to use in your classroom. Exit tickets are formative assessments that provide a quick snapshot about your students' learning and understanding of a topic faster than any other tool in the classroom.
This 20 question multiple choice (4 choices) assessment will help determine your students' understanding of the scientific method. Topics include steps of the scientific method, dependent and independent variables and quantitative data. Ideal for any middle or high school science course and can be...
