Author: Maria Ripol
Publisher: Prentice Hall
Release Date: 2008-02-21
Written as a study tool, the Lab Workbook is keyed directly to the text to provide section by section review and practice for the first ten chapters of Agresti/Franklin 2/e. Print outs of the activities found on the Student CD are included in the Lab Workbook.
STATISTICS: LEARNING FROM DATA, Second Edition, addresses common problems faced by learners of elementary statistics with an innovative approach. The authors have paid particular attention to areas learners often struggle with -- probability, hypothesis testing, and selecting an appropriate method of analysis. Probability coverage is based on current research on how students best learn the subject. A unique chapter that provides an informal introduction to the ideas of statistical inference helps students to develop a framework for choosing an appropriate method. Supported by learning objectives, real-data examples and exercises, and technology notes, this book helps learners to develop conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice. Important Notice: Media content referenced within the product description or the product text may not be available in the ebook version.
Author: R. Mark Sirkin
Release Date: 2006
Popular in previous editions, this Third Edition continues to help build students' confidence and ability in doing statistical analysis by slowly moving from concepts that require little computational work to those that require more. Author R. Mark Sirkin once again demonstrates how statistics can be used so that students come to appreciate their usefulness rather than fear them. Statistics for the Social Sciences emphasizes the analysis and interpretation of data to give students a feel for how data interpretation is related to the methods by which the information was obtained.
Written to appeal to students and instructors who appreciate statistics for its precision and logic, Introductory Statistics: A Problem-Solving Approach helps students learn statistical concepts by using a stepped problem-solving approach. After completing an introductory statistics course with this textbook, students should understand the process of basic statistical arguments. They should grasp the importance of assumptions and be able to follow valid arguments or identify inaccurate conclusions. Most importantly, they should understand the process of statistical inference. The philosophy of this text is simple: statistics is often hard for students, and in order to understand concepts, the material must be presented in an orderly, precise, friendly manner. It must be easy to read and follow, and there must be numerous examples and exercises. The text aims to be easy-to-read, down-to-earth, systematic, and methodical. Each new idea builds upon concepts presented earlier. A touch of humor is important, especially for many students who are afraid of, and even dislike, mathematics and statistics.
Student-Friendly Coverage of Probability, Statistical Methods, Simulation, and Modeling Tools Incorporating feedback from instructors and researchers who used the previous edition, Probability and Statistics for Computer Scientists, Second Edition helps students understand general methods of stochastic modeling, simulation, and data analysis; make optimal decisions under uncertainty; model and evaluate computer systems and networks; and prepare for advanced probability-based courses. Written in a lively style with simple language, this classroom-tested book can now be used in both one- and two-semester courses. New to the Second Edition Axiomatic introduction of probability Expanded coverage of statistical inference, including standard errors of estimates and their estimation, inference about variances, chi-square tests for independence and goodness of fit, nonparametric statistics, and bootstrap More exercises at the end of each chapter Additional MATLAB® codes, particularly new commands of the Statistics Toolbox In-Depth yet Accessible Treatment of Computer Science-Related Topics Starting with the fundamentals of probability, the text takes students through topics heavily featured in modern computer science, computer engineering, software engineering, and associated fields, such as computer simulations, Monte Carlo methods, stochastic processes, Markov chains, queuing theory, statistical inference, and regression. It also meets the requirements of the Accreditation Board for Engineering and Technology (ABET). Encourages Practical Implementation of Skills Using simple MATLAB commands (easily translatable to other computer languages), the book provides short programs for implementing the methods of probability and statistics as well as for visualizing randomness, the behavior of random variables and stochastic processes, convergence results, and Monte Carlo simulations. Preliminary knowledge of MATLAB is not required. Along with numerous computer science applications and worked examples, the text presents interesting facts and paradoxical statements. Each chapter concludes with a short summary and many exercises.
Author: Stephen Kokoska
Publisher: W. H. Freeman
Release Date: 2014-12-15
Stephen Kokoska’s Introductory Statistics: A Problem-Solving Approach demonstrated that when presented in a precise step-by-step manner, with an understanding of what makes the material difficult, statistics can be made accessible, meaningful, and useful, even to the most skeptical students. In this thoroughly updated new edition, Kokoska again combines a traditional, classic approach to teaching statistics with contemporary examples and pedagogical features, blending solid mathematics with lucid, often humorous writing and a distinctive stepped “Solution Trail” problem-solving approach to help students understand the processes behind basic statistical arguments, statistical inference, and data-based decision making. The second edition is a fully integrated text/media package with its own dedicated version of LaunchPad, W.H. Freeman’s breakthrough online course space.
Author: Peter Bruce
Publisher: "O'Reilly Media, Inc."
Release Date: 2017-05-10
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: Howard T. Tokunaga
Publisher: SAGE Publications
Release Date: 2015-01-29
Genre: Social Science
This thematically-based textbook places statistics within the research process, illustrating how statistics are used to answer questions and test ideas. Students learn not only how to calculate statistics, but also how to interpret the results of statistical analyses in light of a study’s research hypothesis and to communicate their results and interpretations to a broader audience. Featuring accessible writing and well-integrated research examples, Fundamental Statistics for the Social and Behavioral Sciences by Howard T. Tokunaga is designed to help readers develop an appreciation of how statistics are applied to topics and questions of interest, gain an appreciation of issues related to the use of statistics, and enhance their understanding of the research process and the role of statistics within it.
Facts101 is your complete guide to Introductory Statistics , A Problem Solving Approach Preliminary Edition. In this book, you will learn topics such as as those in your book plus much more. With key features such as key terms, people and places, Facts101 gives you all the information you need to prepare for your next exam. Our practice tests are specific to the textbook and we have designed tools to make the most of your limited study time.
Probability and Statistics for Science and Engineering with Examples in R teaches students how to use R software to obtain summary statistics, calculate probabilities and quantiles, find confidence intervals, and conduct statistical testing. The first chapter introduces methods for describing statistics. Over the course of the subsequent eight chapters students will learn about probability, discrete and continuous distributions, multiple random variables, point estimation and testing, and inferences based on one and two samples. The book features a comprehensive table for each type of test to help students choose appropriate statistical tests and confidence intervals. Based on years of classroom experience and extensively class-tested, Probability and Statistics for Science and Engineering with Examples in R is designed for one-semester courses in probability and statistics, and specifically for students in the natural sciences or engineering. The material is also suitable for business and economics students who have studied calculus. Hongshik Ahn holds a Ph.D. in statistics from the University of Wisconsin, Madison. Dr. Ahn is currently a professor in the Department of Applied Mathematics and Statistics at Stony Brook University. He worked at National Center for Toxicological Research, FDA before joining Stony Brook University. Recently he served as the vice president of SUNY Korea. His research interests include tree-structured regression and classification, bioinformatics, generalized linear modeling, and risk assessment. Dr. Ahn has been working on NIH grants on various biostatistical and medical researches. He has been published in three book chapters and over 60 peer-reviewed journals. Dr. Ahn also published a book entitled Mathematical Analysis of Genesis, from Shinil Books.
Author: Mario Mazzocchi
Release Date: 2008-05-22
Genre: Business & Economics
Balancing simplicity with technical rigour, this practical guide to the statistical techniques essential to research in marketing and related fields, describes each method as well as showing how they are applied. The book is accompanied by two real data sets to replicate examples and with exercises to solve, as well as detailed guidance on the use of appropriate software including: - 750 powerpoint slides with lecture notes and step-by-step guides to run analyses in SPSS (also includes screenshots) - 136 multiple choice questions for tests This is augmented by in-depth discussion of topics including: - Sampling - Data management and statistical packages - Hypothesis testing - Cluster analysis - Structural equation modelling
STATISTICS: LEARNING FROM DATA, by respected and successful author Roxy Peck, resolves common problems faced by both students and instructors with an innovative approach to elementary statistics. Peck tackles the areas students struggle with most--probability, hypothesis testing, and selecting an appropriate method of analysis--unlike any text on the market. Probability coverage is based on current research that shows how students best learn the subject. Two unique chapters, one on statistical inference and another on learning from experiment data, address two common areas of student confusion: choosing a particular inference method and using inference methods with experimental data. Supported by learning objectives, real-data examples and exercises, and technology notes, this brand new text guides students in gaining conceptual understanding, mechanical proficiency, and the ability to put knowledge into practice.
Author: Robert H. Riffenburgh
Publisher: Academic Press
Release Date: 2012-08-13
Statistics in Medicine, Third Edition makes medical statistics easy to understand by students, practicing physicians, and researchers. The book begins with databases from clinical medicine and uses such data to give multiple worked-out illustrations of every method. The text opens with how to plan studies from conception to publication and what to do with your data, and follows with step-by-step instructions for biostatistical methods from the simplest levels (averages, bar charts) progressively to the more sophisticated methods now being seen in medical articles (multiple regression, noninferiority testing). Examples are given from almost every medical specialty and from dentistry, nursing, pharmacy, and health care management. A preliminary guide is given to tailor sections of the text to various lengths of biostatistical courses. User-friendly format includes medical examples, step-by-step methods, and check-yourself exercises appealing to readers with little or no statistical background, across medical and biomedical disciplines Facilitates stand-alone methods rather than a required sequence of reading and references to prior text Covers trial randomization, treatment ethics in medical research, imputation of missing data, evidence-based medical decisions, how to interpret medical articles, noninferiority testing, meta-analysis, screening number needed to treat, and epidemiology Fills the gap left in all other medical statistics books between the reader’s knowledge of how to go about research and the book’s coverage of how to analyze results of that research New in this Edition: New chapters on planning research, managing data and analysis, Bayesian statistics, measuring association and agreement, and questionnaires and surveys New sections on what tests and descriptive statistics to choose, false discovery rate, interim analysis, bootstrapping, Bland-Altman plots, Markov chain Monte Carlo (MCMC), and Deming regression Expanded coverage on probability, statistical methods and tests relatively new to medical research, ROC curves, experimental design, and survival analysis 35 Databases in Excel format used in the book and can be downloaded and transferred into whatever format is needed along with PowerPoint slides of figures, tables, and graphs from the book included on the companion site, http://www.elsevierdirect.com/companion.jsp?ISBN=9780123848642 Medical subject index offers additional search capabilities