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Practical Statistics: A Quick and Easy Guide to IBM® SPSS® Statistics, STATA, and Other Statistical Software
by David KremelbergMaking statistics—and statistical software—accessible and rewarding This book provides readers with step-by-step guidance on running a wide variety of statistical analyses in IBM® SPSS® Statistics, Stata, and other programs. Author David Kremelberg begins his user-friendly text by covering charts and graphs through regression, time-series analysis, and factor analysis. He provides a background of the method, then explains how to run these tests in IBM SPSS and Stata. He then progresses to more advanced kinds of statistics such as HLM and SEM, where he describes the tests and explains how to run these tests in their appropriate software including HLM and AMOS. This is an invaluable guide for upper-level undergraduate and graduate students across the social and behavioral sciences who need assistance in understanding the various statistical packages.
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
by Peter Bruce Andrew Bruce Peter GedeckStatistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying 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 or Python programming languages 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 scienceHow random sampling can reduce bias and yield a higher-quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data
Practical Statistics for Engineers and Scientists
by Nicholas P. Cheremisinoff Louise FerranteThis book provides direction in constructing regression routines that can be used with worksheet software on personal computers. The book lists useful references for those readers who desire more in-depth understanding of the mathematical bases, and is helpful for science and engineering students.
Practical Statistics for Medical Research (Chapman & Hall/CRC Texts in Statistical Science)
by Douglas G. AltmanPractical Statistics for Medical Research is a problem-based text for medical researchers, medical students, and others in the medical arena who need to use statistics but have no specialized mathematics background. The author draws on twenty years of experience as a consulting medical statistician to provide clear explanations to key statistical concepts, with a firm emphasis on practical aspects of designing and analyzing medical research. Using real data and including dozens of interesting data sets, this bestselling text gives special attention to the presentation and interpretation of results and the many real problems that arise in medical research.
Practical Statistics Simply Explained
by Dr Russell LangleyFor those who need to know statistics but shy away from math, this book teaches how to extract truth and draw valid conclusions from numerical data using logic and the philosophy of statistics rather than complex formulae. Lucid discussion of averages and scatter, investigation design, more. Problems with solutions.
Practical Text Analytics: Maximizing the Value of Text Data (Advances in Analytics and Data Science #2)
by Murugan Anandarajan Chelsey Hill Thomas NolanThis book introduces text analytics as a valuable method for deriving insights from text data. Unlike other text analytics publications, Practical Text Analytics: Maximizing the Value of Text Data makes technical concepts accessible to those without extensive experience in the field. Using text analytics, organizations can derive insights from content such as emails, documents, and social media. Practical Text Analytics is divided into five parts. The first part introduces text analytics, discusses the relationship with content analysis, and provides a general overview of text mining methodology. In the second part, the authors discuss the practice of text analytics, including data preparation and the overall planning process. The third part covers text analytics techniques such as cluster analysis, topic models, and machine learning. In the fourth part of the book, readers learn about techniques used to communicate insights from text analysis, including data storytelling. The final part of Practical Text Analytics offers examples of the application of software programs for text analytics, enabling readers to mine their own text data to uncover information.
Practical Time Series Analysis: Prediction with Statistics and Machine Learning
by Aileen NielsenTime series data analysis is increasingly important due to the massive production of such data through the internet of things, the digitalization of healthcare, and the rise of smart cities. As continuous monitoring and data collection become more common, the need for competent time series analysis with both statistical and machine learning techniques will increase.Covering innovations in time series data analysis and use cases from the real world, this practical guide will help you solve the most common data engineering and analysis challengesin time series, using both traditional statistical and modern machine learning techniques. Author Aileen Nielsen offers an accessible, well-rounded introduction to time series in both R and Python that will have data scientists, software engineers, and researchers up and running quickly.You’ll get the guidance you need to confidently:Find and wrangle time series dataUndertake exploratory time series data analysisStore temporal dataSimulate time series dataGenerate and select features for a time seriesMeasure errorForecast and classify time series with machine or deep learningEvaluate accuracy and performance
Practical Time Series Analysis in Natural Sciences (Progress in Geophysics)
by Victor PrivalskyThis book presents an easy-to-use tool for time series analysis and allows the user to concentrate upon studying time series properties rather than upon how to calculate the necessary estimates. The two attached programs provide, in one run of the program, a time and frequency domain description of scalar or multivariate time series approximated with a sequence of autoregressive models of increasing orders. The optimal orders are chosen by five order selection criteria. The results for scalar time series include time domain stochastic difference equations, spectral density estimates, predictability properties, and a forecast of scalar time series based upon the Kolmogorov-Wiener theory. For the bivariate and trivariate time series, the results contain a time domain description with multivariate stochastic difference equations, statistical predictability criterion, and information for calculating feedback and Granger causality properties in the bivariate case. The frequency domain information includes spectral densities, ordinary, multiple, and partial coherence functions, ordinary and multiple coherent spectra, gain, phase, and time lag factors. The programs seem to be unique and using them does not require professional knowledge of theory of random processes. The book contains many examples including three from engineering.
Practical Tools for Designing and Weighting Survey Samples
by Jill A. Dever Frauke Kreuter Richard ValliantSurvey sampling is fundamentally an applied field. The goal in this book is to put an array of tools at the fingertips of practitioners by explaining approaches long used by survey statisticians, illustrating how existing software can be used to solve survey problems, and developing some specialized software where needed. This book serves at least three audiences: (1) Students seeking a more in-depth understanding of applied sampling either through a second semester-long course or by way of a supplementary reference; (2) Survey statisticians searching for practical guidance on how to apply concepts learned in theoretical or applied sampling courses; and (3) Social scientists and other survey practitioners who desire insight into the statistical thinking and steps taken to design, select, and weight random survey samples. Several survey data sets are used to illustrate how to design samples, to make estimates from complex surveys for use in optimizing the sample allocation, and to calculate weights. Realistic survey projects are used to demonstrate the challenges and provide a context for the solutions. The book covers several topics that either are not included or are dealt with in a limited way in other texts. These areas include: sample size computations for multistage designs; power calculations related to surveys; mathematical programming for sample allocation in a multi-criteria optimization setting; nuts and bolts of area probability sampling; multiphase designs; quality control of survey operations; and statistical software for survey sampling and estimation. An associated R package, PracTools, contains a number of specialized functions for sample size and other calculations. The data sets used in the book are also available in PracTools, so that the reader may replicate the examples or perform further analyses.
Practical Tools for Designing and Weighting Survey Samples (Statistics For Social And Behavioral Sciences Ser. #51)
by Frauke Kreuter Jill A. Dever Richard ValliantThe goal of this book is to put an array of tools at the fingertips of students, practitioners, and researchers by explaining approaches long used by survey statisticians, illustrating how existing software can be used to solve survey problems, and developing some specialized software where needed. This volume serves at least three audiences: (1) students of applied sampling techniques; 2) practicing survey statisticians applying concepts learned in theoretical or applied sampling courses; and (3) social scientists and other survey practitioners who design, select, and weight survey samples. The text thoroughly covers fundamental aspects of survey sampling, such as sample size calculation (with examples for both single- and multi-stage sample design) and weight computation, accompanied by software examples to facilitate implementation. Features include step-by-step instructions for calculating survey weights, extensive real-world examples and applications, and representative programming code in R, SAS, and other packages. Since the publication of the first edition in 2013, there have been important developments in making inferences from nonprobability samples, in address-based sampling (ABS), and in the application of machine learning techniques for survey estimation. New to this revised and expanded edition: • Details on new functions in the PracTools package • Additional machine learning methods to form weighting classes • New coverage of nonlinear optimization algorithms for sample allocation • Reflecting effects of multiple weighting steps (nonresponse and calibration) on standard errors • A new chapter on nonprobability sampling • Additional examples, exercises, and updated references throughout Richard Valliant, PhD, is Research Professor Emeritus at the Institute for Social Research at the University of Michigan and at the Joint Program in Survey Methodology at the University of Maryland. He is a Fellow of the American Statistical Association, an elected member of the International Statistical Institute, and has been an Associate Editor of the Journal of the American Statistical Association, Journal of Official Statistics, and Survey Methodology. Jill A. Dever, PhD, is Senior Research Statistician at RTI International in Washington, DC. She is a Fellow of the American Statistical Association, Associate Editor for Survey Methodology and the Journal of Official Statistics, and an Assistant Research Professor in the Joint Program in Survey Methodology at the University of Maryland. She has served on several panels for the National Academy of Sciences and as a task force member for the American Association of Public Opinion Research’s report on nonprobability sampling. Frauke Kreuter, PhD, is Professor and Director of the Joint Program in Survey Methodology at the University of Maryland, Professor of Statistics and Methodology at the University of Mannheim, and Head of the Statistical Methods Research Department at the Institute for Employment Research (IAB) in Nürnberg, Germany. She is a Fellow of the American Statistical Association and has been Associate Editor of the Journal of the Royal Statistical Society, Journal of Official Statistics, Sociological Methods and Research, Survey Research Methods, Public Opinion Quarterly, American Sociological Review, and the Stata Journal. She is founder of the International Program for Survey and Data Science and co-founder of the Coleridge Initiative.
Practically Speaking: A Dictionary of Quotations on Engineering, Technology and Architecture
by C.C. Gaither Alma E Cavazos-GaitherThis book brings together over 1,100 quotes pertinent and illuminating to engineering, technology and architecture. It includes extensive author and subject indexes for locating quotations. The book can be read for entertainment or used as a handy reference by students and professional engineers.
Practice: Mathematics Applications and Concepts, Course 1
by McGraw-HillPractice: Skills Workbook provides ample exercises to help students develop computational skills, lesson by lesson.
Practice: Mathematics Applications and Concepts, Course 1
by McGraw-HillPractice: Word Problems mimics the verbal problems in each lesson at an average level.
Practice And Homework Journal Grade 1 (Into Math)
by Houghton Mifflin HarcourtNIMAC-sourced textbook
Practice of Bayesian Probability Theory in Geotechnical Engineering
by Wan-Huan Zhou Zhen-Yu Yin Ka-Veng YuenThis book introduces systematically the application of Bayesian probabilistic approach in soil mechanics and geotechnical engineering. Four typical problems are analyzed by using Bayesian probabilistic approach, i.e., to model the effect of initial void ratio on the soil–water characteristic curve (SWCC) of unsaturated soil, to select the optimal model for the prediction of the creep behavior of soft soil under one-dimensional straining, to identify model parameters of soils and to select constitutive model of soils considering critical state concept. This book selects the simple and easy-to-understand Bayesian probabilistic algorithm, so that readers can master the Bayesian method to analyze and solve the problem in a short time. In addition, this book provides MATLAB codes for various algorithms and source codes for constitutive models so that readers can directly analyze and practice.This book is useful as a postgraduate textbook for civil engineering, hydraulic engineering, transportation, railway, engineering geology and other majors in colleges and universities, and as an elective course for senior undergraduates. It is also useful as a reference for relevant professional scientific researchers and engineers.
Practice of Constitutive Modelling for Saturated Soils
by Zhen-Yu Yin Pierre-Yves Hicher Yin-Fu JinThis book describes the development of a constitutive modeling platform for soil testing, which is one of the key components in geomechanics and geotechnics. It discusses the fundamentals of the constitutive modeling of soils and illustrates the use of these models to simulate various laboratory tests. To help readers understand the fundamentals and modeling of soil behaviors, it first introduces the general stress–strain relationship of soils and the principles and modeling approaches of various laboratory tests, before examining the ideas and formulations of constitutive models of soils. Moving on to the application of constitutive models, it presents a modeling platform with a practical, simple interface, which includes various kinds of tests and constitutive models ranging from clay to sand, that is used for simulating most kinds of laboratory tests. The book is intended for undergraduate and graduate-level teaching in soil mechanics and geotechnical engineering and other related engineering specialties. Thanks to the inclusion of real-world applications, it is also of use to industry practitioners, opening the door to advanced courses on modeling within the industrial engineering and operations research fields.
The Practice of Econometric Theory
by Charles G. RenfroEconometric theory, as presented in textbooks and the econometric literature generally, is a somewhat disparate collection of findings. Its essential nature is to be a set of demonstrated results that increase over time, each logically based on a specific set of axioms or assumptions, yet at every moment, rather than a finished work, these inevitably form an incomplete body of knowledge. The practice of econometric theory consists of selecting from, applying, and evaluating this literature, so as to test its applicability and range. The creation, development, and use of computer software has led applied economic research into a new age. This book describes the history of econometric computation from 1950 to the present day, based upon an interactive survey involving the collaboration of the many econometricians who have designed and developed this software. It identifies each of the econometric software packages that are made available to and used by economists and econometricians worldwide.
The Practice of Statistics (Prep for the AP* Exam Guide)
by Michael LegacyPractice of Statistics: AP Exam Guide 3rd Edition by Michael Legacy
The Practice of Statistics: TI-83/84/89 Graphing Calculator Enhanced
by David S. Moore Daniel S. Yates Darren S. StarnesNIMAC-sourced textbook
The Practice of Statistics: TI-83/89 Graphing Calculator Enhanced (Prep for the AP Exam Guide)
by Larry PetersonBuilding on the "Prep for the AP Exam" feature on the Web, this study guide contains four full-length sample exams to help student refresh their skills and prepare for the actual AP Exam.
The Practice of Statistics
by Daren S. Starnes Josh Tabor Daniel S. Yates David S. MooreCombining a data analysis approach with the power of technology, innovative pedagogy, and a number of new features, this fifth edition has been updated to incorporate Learning Objectives in each section and link them to chapter reviews.
The Practice Of Statistics: Ti-83/84/89 Graphing Calculator Enhanced (Third Edition)
by Dan S. Yates David S. Moore Daren S. StarnesThe Practice of Statistics: TI-83/84/89 Graphing Calculator Enhanced (TPS), Third Edition, is an introductory text that focuses on data and statistical reasoning. It is intended for high school, college, and university students whose primary technological tool is the TI-83, TI-84, or TI-89 graphing calculator. <P><P> This book is based on the successful college textbooks The Basic Practice of Statistics (BPS) by David Moore and Introduction to the Practice of Statistics (IPS) by David Moore and George McCabe. <P>The Practice of Statistics was the first book written specifically for the College Board AP1 Statistics course. Statisticians have reached general consensus about the nature of a modern introductory statistics course. <P> A joint committee of the American Statistical Association and the Mathematical Association of America summarized this consensus as follows: Emphasize statistical thinking Present more data and concepts with less theory and fewer formulas Foster active learning
The Practice of Statistics: TI-83/89 Graphing Calculator Enhanced (2nd Edition)
by Daniel S. Yates David S. Moore Daren S. StarnesTailored to mirror the AP Statistics course, The Practice of Statistics became a classroom favorite. This edition incorporates a number of first-time features to help students prepare for the AP exam, plus more simulations and statistical thinking help, and instructions for the TI-89 graphic calculator.
The Practice Of Statistics
by Yates Daren S. Starnes Dan Yates David S. MooreCombining a data analysis approach with the power of technology, innovative pedagogy, and a number of new features, The Practice of Statistics is an impressively effective text for learning statistics. The fifth edition has been updated to incorporate Learning Objectives in each section and link them to chapter reviews.