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Level 2 Probability Learning Workbook
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Book Synopsis Level 2 Probability Learning Workbook by : Phyl Haydock
Download or read book Level 2 Probability Learning Workbook written by Phyl Haydock and published by . This book was released on 2015 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Level 2 Simulations Learning Workbook by : Phyl Haydock
Download or read book Level 2 Simulations Learning Workbook written by Phyl Haydock and published by . This book was released on 2015 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Walker Maths Senior 2. 12 Probability Methods Workbook by : Charlotte Walker
Download or read book Walker Maths Senior 2. 12 Probability Methods Workbook written by Charlotte Walker and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Walker Maths is a series of single standard workbooks containing high-quality, up to date material at NCEA Mathematics levels 1, 2 and 3. The well-designed, write-on workbooks contain teaching material, including relevant formulae, and ample practice exercises along with sample tasks and questions. The workbooks reflect the content and style of the new standards, and allow teachers total flexibility in course design for students at all levels. As a single standard series, Walker Maths offers Maths department the ability to buy titles all at once, or throughout the year as required. A Walker Maths Digital Teacher Resource is available for $9.95 per year for a single download. Each Digital Teacher Resource includes a Walker Maths eBook/projection file. Plus a selection of ' Worksheets ' Extra questions ' Teacher notes ' Videos ' Puzzle sheets ' Practice quizzes ' Worked solutions Schools qualify by adopting the corresponding workbook. Please contact your Sales Representative for more information.
Book Synopsis Level 2 Mathematics and Statistics Externals Learning Workbook by : Phyl Haydock
Download or read book Level 2 Mathematics and Statistics Externals Learning Workbook written by Phyl Haydock and published by . This book was released on 2020-11-06 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Level 2 Mathematics and Statistics Learning Workbook by : Rory Barrett
Download or read book Level 2 Mathematics and Statistics Learning Workbook written by Rory Barrett and published by . This book was released on 2012-02 with total page 296 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Level 2 Mathematics and Statistics Learning Workbook by : Rory Barrett
Download or read book Level 2 Mathematics and Statistics Learning Workbook written by Rory Barrett and published by . This book was released on 2018 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Year 10 Mathematics and Statistics Learning Workbook by : Phyl Haydock
Download or read book Year 10 Mathematics and Statistics Learning Workbook written by Phyl Haydock and published by . This book was released on 2009 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: For use in classroom programmes or for home study. Will cover all the skills needed to for Year 10 of the new Mathematics Curriculum. Features bite-sized sections of theory, numerous worked examples and relevant sets of exercises for student practice. Students write their answers in the workbook and all answers are given in the back of the book.
Book Synopsis Introduction to Probability by : David F. Anderson
Download or read book Introduction to Probability written by David F. Anderson and published by Cambridge University Press. This book was released on 2017-11-02 with total page 447 pages. Available in PDF, EPUB and Kindle. Book excerpt: This classroom-tested textbook is an introduction to probability theory, with the right balance between mathematical precision, probabilistic intuition, and concrete applications. Introduction to Probability covers the material precisely, while avoiding excessive technical details. After introducing the basic vocabulary of randomness, including events, probabilities, and random variables, the text offers the reader a first glimpse of the major theorems of the subject: the law of large numbers and the central limit theorem. The important probability distributions are introduced organically as they arise from applications. The discrete and continuous sides of probability are treated together to emphasize their similarities. Intended for students with a calculus background, the text teaches not only the nuts and bolts of probability theory and how to solve specific problems, but also why the methods of solution work.
Book Synopsis Research on Teaching and Learning Probability by : Carmen Batanero
Download or read book Research on Teaching and Learning Probability written by Carmen Batanero and published by Springer. This book was released on 2016-07-12 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book summarizes the vast amount of research related to teaching and learning probability that has been conducted for more than 50 years in a variety of disciplines. It begins with a synthesis of the most important probability interpretations throughout history: intuitive, classical, frequentist, subjective, logical propensity and axiomatic views. It discusses their possible applications, philosophical problems, as well as their potential and the level of interest they enjoy at different educational levels. Next, the book describes the main features of probabilistic thinking and reasoning, including the contrast to classical logic, probability language features, the role of intuitions, as well as paradoxes and the relevance of modeling. It presents an analysis of the differences between conditioning and causation, the variability expression in data as a sum of random and causal variations, as well as those of probabilistic versus statistical thinking. This is followed by an analysis of probability’s role and main presence in school curricula and an outline of the central expectations in recent curricular guidelines at the primary, secondary and high school level in several countries. This book classifies and discusses in detail the three different research periods on students’ and people’s intuitions and difficulties concerning probability: early research focused on cognitive development, a period of heuristics and biases programs, and the current period marked by a multitude of foci, approaches and theoretical frameworks.
Book Synopsis High-Dimensional Probability by : Roman Vershynin
Download or read book High-Dimensional Probability written by Roman Vershynin and published by Cambridge University Press. This book was released on 2018-09-27 with total page 299 pages. Available in PDF, EPUB and Kindle. Book excerpt: An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.
Book Synopsis Introduction to Probability by : Dimitri Bertsekas
Download or read book Introduction to Probability written by Dimitri Bertsekas and published by Athena Scientific. This book was released on 2008-07-01 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: An intuitive, yet precise introduction to probability theory, stochastic processes, statistical inference, and probabilistic models used in science, engineering, economics, and related fields. This is the currently used textbook for an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students, and for a leading online class on the subject. The book covers the fundamentals of probability theory (probabilistic models, discrete and continuous random variables, multiple random variables, and limit theorems), which are typically part of a first course on the subject. It also contains a number of more advanced topics, including transforms, sums of random variables, a fairly detailed introduction to Bernoulli, Poisson, and Markov processes, Bayesian inference, and an introduction to classical statistics. The book strikes a balance between simplicity in exposition and sophistication in analytical reasoning. Some of the more mathematically rigorous analysis is explained intuitively in the main text, and then developed in detail (at the level of advanced calculus) in the numerous solved theoretical problems.
Download or read book Chances Are-- written by Nancy Pfenning and published by PRUFROCK PRESS INC.. This book was released on 1998 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: Do you avoid teaching probability and statistics because the subjects seem confusing and complex? Are you less than sure about your knowledge of the topics? Let Chances Are . . . take you and your students on a fun and exciting mathematical journey none of you will ever forget! Filled with easy-to-understand explanations and creative activities, this book offers teachers a simple method for teaching probability and statistics in an enjoyable way. This book can serve as an introduction for any beginner, from gifted and advanced students in upper elementary school, to high school students needing enrichment or preparation for Advanced Placement Statistics or future college courses. From helping to win a card game, to making life-or-death medical decisions, the uses of probability and statistics are virtually endless. For teachers of elementary students, the book offers simple, hands-on lessons and activities about probability and basic statistics. For teachers of older students, advanced statistical concepts are discussed and activities are provided. Reviewers have found the book's level to be appropriate for a wide range of ages, from fourth graders to post-secondary students.
Book Synopsis All of Statistics by : Larry Wasserman
Download or read book All of Statistics written by Larry Wasserman and published by Springer Science & Business Media. This book was released on 2013-12-11 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Book Synopsis A Modern Introduction to Probability and Statistics by : F.M. Dekking
Download or read book A Modern Introduction to Probability and Statistics written by F.M. Dekking and published by Springer Science & Business Media. This book was released on 2006-03-30 with total page 485 pages. Available in PDF, EPUB and Kindle. Book excerpt: Suitable for self study Use real examples and real data sets that will be familiar to the audience Introduction to the bootstrap is included – this is a modern method missing in many other books
Book Synopsis Learning Statistics with R by : Daniel Navarro
Download or read book Learning Statistics with R written by Daniel Navarro and published by Lulu.com. This book was released on 2013-01-13 with total page 617 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Learning Statistics with R" covers the contents of an introductory statistics class, as typically taught to undergraduate psychology students, focusing on the use of the R statistical software and adopting a light, conversational style throughout. The book discusses how to get started in R, and gives an introduction to data manipulation and writing scripts. From a statistical perspective, the book discusses descriptive statistics and graphing first, followed by chapters on probability theory, sampling and estimation, and null hypothesis testing. After introducing the theory, the book covers the analysis of contingency tables, t-tests, ANOVAs and regression. Bayesian statistics are covered at the end of the book. For more information (and the opportunity to check the book out before you buy!) visit http://ua.edu.au/ccs/teaching/lsr or http://learningstatisticswithr.com
Book Synopsis Introduction to Probability, Statistics, and Random Processes by : Hossein Pishro-Nik
Download or read book Introduction to Probability, Statistics, and Random Processes written by Hossein Pishro-Nik and published by . This book was released on 2014-08-15 with total page 746 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book covers basic concepts such as random experiments, probability axioms, conditional probability, and counting methods, single and multiple random variables (discrete, continuous, and mixed), as well as moment-generating functions, characteristic functions, random vectors, and inequalities; limit theorems and convergence; introduction to Bayesian and classical statistics; random processes including processing of random signals, Poisson processes, discrete-time and continuous-time Markov chains, and Brownian motion; simulation using MATLAB and R.
Book Synopsis Mathematics for Machine Learning by : Marc Peter Deisenroth
Download or read book Mathematics for Machine Learning written by Marc Peter Deisenroth and published by Cambridge University Press. This book was released on 2020-04-23 with total page 392 pages. Available in PDF, EPUB and Kindle. Book excerpt: The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.