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R 4,578.00
Essential First Steps to Data Analysis
Carol S. Parke′s Essential First Steps to Data Analysis: Scenario-Based Examples Using SPSS provides instruction and guidance on preparing quantitative data sets prior to answering a study′s research questions. Such preparation may involve data management and manipulation tasks, data organization, structural changes to the data files, or conducting preliminary analysis. Twelve research-based scenarios are used to present the content. Each scenario tells the "story" of a researcher who thoroughly examined their data and the decisions they made along the way. The scenario begins with a description of the researcher′s study and his/her data file(s), then describes the issues the researcher must address, explains why they are important, shows how SPSS was used to address the issues and prepare data, and shares the researcher′s reflections and any additional decision-making. Finally, each scenario ends with the researcher′s written summary of the procedures and outcomes from the initial data preparation or analysis.
R 2,363.48
Data Analysis
This book explores the many provocative questions concerning the fundamentals of data analysis. It is based on the time-tested experience of one of the gurus of the subject matter. Why should one study data analysis? How should it be taught? What techniques work best, and for whom? How valid are the results? How much data should be tested? Which machine languages should be used, if used at all? Emphasis on apprenticeship (through hands-on case studies) and anecdotes (through real-life applications) are the tools that Peter J. Huber uses in this volume. Concern with specific statistical techniques is not of immediate value; rather, questions of strategy – when to use which technique – are employed. Central to the discussion is an understanding of the significance of massive (or robust) data sets, the implementation of languages, and the use of models. Each is sprinkled with an ample number of examples and case studies. Personal practices, various pitfalls, and existing controversies are presented when applicable. The book serves as an excellent philosophical and historical companion to any present-day text in data analysis, robust statistics, data mining, statistical learning, or computational statistics.
R 4,818.00
Data Analysis
Bridging the gap between statistical theory and physical experiment, this is a thorough introduction to the statistical methods used in the experimental physical sciences and to the numerical methods used to implement them. The treatment emphasises concise but rigorous mathematics but always retains its focus on applications. Readers are assumed to have a sound basic knowledge of differential and integral calculus and some knowledge of vectors and matrices. After an introduction to probability, random variables, computer generation of random numbers and important distributions, the book turns to statistical samples, the maximum likelihood method, and the testing of statistical hypotheses. The discussion concludes with several important statistical methods: least squares, analysis of variance, polynomial regression, and analysis of time series. Appendices provide the necessary methods of matrix algebra, combinatorics, and many sets of useful algorithms and formulae.
R 1,618.51
R 519.00
Step-By-Step Introduction to Statistics for Business
A clear and concise introduction to statistics for business and management students, demonstrating how important statistics are in the business decision-making process and covering everything from conducting a survey and collecting data, to summarizing statistical data, and presenting findings. Each chapter features a real-world business situation and accompanying dataset, the reader is then encouraged to identify the correct statistical concept in the chapter and solve the problem outlined. Offering students a chance to use the newly learned theory in a practical way. New to the second edition: A "Review of Essential Mathematics" prologue, featuring tests and further links to help students refresh their knowledge of the core mathematical concepts used to calculate basic statistics. Updated screenshots on using IBM SPSS and Excel. A "Statistics in the Real World" feature included at the end of each chapter, demonstrating how statistics are applied in real-world business settings and research, accompanied by reflective questions. Updated case studies, examples and diagrams, illustrating key points and helping to reinforce learning. The book is accompanied by free online resources including step-by-step video tutorials on how to use Excel and IBM SPSS, datasets and worked solutions, an Instructors’ Manual, Testbank, and PowerPoint presentation slides for lecturers. Essential reading for business students wanting to know how to use statistics in a business setting.
R 3,825.00
Data Analysis
The fourth edition of this successful textbook presents a comprehensive introduction to statistical and numerical methods for the evaluation of empirical and experimental data. Equal weight is given to statistical theory and practical problems. The concise mathematical treatment of the subject matter is illustrated by many examples and for the present edition a library of Java programs has been developed. It comprises methods of numerical data analysis and graphical representation as well as many example programs and solutions to programming problems.The book is conceived both as an introduction and as a work of reference. In particular it addresses itself to students, scientists and practitioners in science and engineering as a help in the analysis of their data in laboratory courses, in working for bachelor or master degrees, in thesis work, and in research and professional work.
R 1,642.45
Discovering Statistics Using IBM SPSS Statistics
With its unique combination of humour and step-by-step instruction, this award-winning book is the statistics lifesaver for everyone. From initial theory through to regression, factor analysis and multilevel modelling, Andy Field animates statistics and SPSS software with his famously bizarre examples and activities.Features:• Flexible coverage to support students across disciplines and degree programmes• Can support classroom or lab learning and assessment• Analysis of real data with opportunities to practice statistical skills• Highlights common misconceptions and errors• A revamped online resource that uses video, case studies, datasets, testbanks and more to help students negotiate project work, master data management techniques, and apply key writing and employability skills• Covers the range of versions of IBM SPSS Statistics©.All the online resources above (video, case studies, datasets, testbanks) can be easily integrated into your institution′s virtual learning environment or learning management system. This allows you to customize and curate content for use in module preparation, delivery and assessment.
R 2,859.00
Introductory Statistics for Data Analysis
This book describes the probability theory associated with frequently used statistical procedures and the relation between probability theory and statistical inference. The first third of the book is dedicated to probability theory including topics relating to events, random variables, and the Central Limit Theorem. Statistical topics then include parameter estimation with confidence intervals, hypothesis testing, chi-square tests, t tests, and several non-parametric tests. Flow charts are frequently used to facilitate an understanding of the material considered. The examples and problems in the book all concern simple data sets which can be analyzed with a simple calculator; however, the R code required to complete many examples and problems is provided as well for those that are interested.
R 3,034.51
Introduction to Python in Earth Science Data Analysis
This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.
R 2,624.51
Introduction to Survey Research, Polling, and Data Analysis
"The writing style is mature and experienced. There is noticeable care in the presentation of materials and appropriate sensitivity to the intended audience. . . . The book categorizes large bodies of wisdom in the conduct of survey research over the past half century and provides ′′rules-of-thumb′′ guidance to the production, analysis, and consumption of survey data." --Steve Seitz, University of Illinois "The authors have provided a well-written update of their textbook, which will be useful as a textbook or supplement in upper-division undergraduate or introductory graduate courses on survey research, statistical data analysis, or research design. The book provides readers with the knowledge that enables them to evaluate the results of survey research. It sensitizes the readers to potential problems that arise from flaws in research design, sampling, questionnaire construction, data collection, coding, data analysis techniques, and improper presentation or interpretation of findings." --Mike Margolis, University of Cincinnati Why should we believe the results of surveys based on relatively few interviews? What are the appropriate cautions in interpreting survey reports? Aimed at answering these questions, An Introduction to Survey Research, Polling, and Data Analysis, Third Edition describes how surveys are conducted in such a way that they can be believed, explains how to read statistical reports and analyze data, and provides guidelines that are useful in evaluating polls. Using frequent examples from contemporary, large national surveys and polls as well as from the media, the authors stress understanding tables thoroughly before moving to interval statistics. In addition, they cover survey design, sampling and question-writing steps, interviewing and coding strategies, and survey analysis from frequency distributions and cross-tabulations to control tables and correlation/regression. They also cover the ethics of survey research and how to read and write survey research reports. Readers of this book will learn to distinguish good from bad polls and to better design and conduct their own surveys and polls.
R 4,937.57
Matrix-Based Introduction to Multivariate Data Analysis
This is the first textbook that allows readers who may be unfamiliar with matrices to understand a variety of multivariate analysis procedures in matrix forms. By explaining which models underlie particular procedures and what objective function is optimized to fit the model to the data, it enables readers to rapidly comprehend multivariate data analysis. Arranged so that readers can intuitively grasp the purposes for which multivariate analysis procedures are used, the book also offers clear explanations of those purposes, with numerical examples preceding the mathematical descriptions.Supporting the modern matrix formulations by highlighting singular value decomposition among theorems in matrix algebra, this book is useful for undergraduate students who have already learned introductory statistics, as well as for graduate students and researchers who are not familiar with matrix-intensive formulations of multivariate data analysis.The book begins by explaining fundamental matrix operations and the matrix expressions of elementary statistics. Then, it offers an introduction to popular multivariate procedures, with each chapter featuring increasing advanced levels of matrix algebra. Further the book includes in six chapters on advanced procedures, covering advanced matrix operations and recently proposed multivariate procedures, such as sparse estimation, together with a clear explication of the differences between principal components and factor analyses solutions. In a nutshell, this book allows readers to gain an understanding of the latest developments in multivariate data science.
R 4,762.00
Data Analysis in Management with SPSS Software
This book provides readers with a greater understanding of a variety of statistical techniques along with the procedure to use the most popular statistical software package SPSS. It strengthens the intuitive understanding of the material, thereby increasing the ability to successfully analyze data in the future. The book provides more control in the analysis of data so that readers can apply the techniques to a broader spectrum of research problems. This book focuses on providing readers with the knowledge and skills needed to carry out research in management, humanities, social and behavioural sciences by using SPSS.
R 2,800.67
Using R for Data Analysis in Social Sciences
Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for undergraduate and graduate students in the social sciences on how to use R to manage, visualize, and analyze data. The focus is on how to address substantive questions with data analysis and replicate published findings.Using R for Data Analysis in Social Sciences adopts a minimalist approach and covers only the most important functions and skills in R to conduct reproducible research. It emphasizes the practical needs of students using R by showing how to import, inspect, and manage data, understand the logic of statistical inference, visualize data and findings via histograms, boxplots, scatterplots, and diagnostic plots, and analyze data using one-sample t-test, difference-of-means test,covariance, correlation, ordinary least squares (OLS) regression, and model assumption diagnostics. It also demonstrates how to replicate the findings in published journal articles and diagnose model assumption violations. Because the book integrates R programming, the logic and steps of statistical inference, and theprocess of empirical social scientific research in a highly accessible and structured fashion, it is appropriate for any introductory course on R, data analysis, and empirical social-scientific research.
R 2,105.89
Introduction to Research Methods and Data Analysis in Psychology
This third edition of Introduction to Research Methods and Data Analysis in Psychology provides you with a unique, balanced blend of quantitative and qualitative research methods. Highly practical in nature, the book guides you, step-by-step, through the research process and is underpinned by SPSS screenshots, diagrams and examples throughout.
R 3,004.00
Introduction to Data Analysis with R for Forensic Scientists
Statistical methods provide a logical, coherent framework in which data from experimental science can be analyzed. However, many researchers lack the statistical skills or resources that would allow them to explore their data to its full potential. Introduction to Data Analysis with R for Forensic Sciences minimizes theory and mathematics and focuses on the application and practice of statistics to provide researchers with the dexterity necessary to systematically analyze data discovered from the fruits of their research.Using traditional techniques and employing examples and tutorials with real data collected from experiments, this book presents the following critical information necessary for researchers: A refresher on basic statistics and an introduction to RConsiderations and techniques for the visual display of data through graphicsAn overview of statistical hypothesis tests and the reasoning behind themA comprehensive guide to the use of the linear model, the foundation of most statistics encounteredAn introduction to extensions to the linear model for commonly encountered scenarios, including logistic and Poisson regressionInstruction on how to plan and design experiments in a way that minimizes cost and maximizes the chances of finding differences that may existFocusing on forensic examples but useful for anyone working in a laboratory, this volume enables researchers to get the most out of their experiments by allowing them to cogently analyze the data they have collected, saving valuable time and effort.
R 3,714.61
Applied Multivariate Data Analysis
An easy to read survey of data analysis, linear regression models and analysis of variance. The extensive development of the linear model includes the use of the linear model approach to analysis of variance provides a strong link to statistical software packages, and is complemented by a thorough overview of theory. It is assumed that the reader has the background equivalent to an introductory book in statistical inference. Can be read easily by those who have had brief exposure to calculus and linear algebra. Intended for first year graduate students in business, social and the biological sciences. Provides the student with the necessary statistics background for a course in research methodology. In addition, undergraduate statistics majors will find this text useful as a survey of linear models and their applications.
R 1,612.06
Quantitative Data Analysis
This book is an accessible introduction to quantitative data analysis, concentrating on the key issues facing those new to research, such as how to decide which statistical procedure is suitable, and how to interpret the subsequent results. Each chapter includes illustrative examples and a set of exercises that allows readers to test their understanding of the topic. The book, written for graduate students in the social sciences, public health, and education, offers a practical approach to making sociological sense out of a body of quantitative data. The book also will be useful to more experienced researchers who need a readily accessible handbook on quantitative methods. The author has posted stata files, updates and data sets at his website http://tinyurl.com/Treiman-stata-files-data-sets.
R 2,824.00
Statistical Data Analysis for the Physical Sciences
Data analysis lies at the heart of every experimental science. Providing a modern introduction to statistics, this book is ideal for undergraduates in physics. It introduces the necessary tools required to analyse data from experiments across a range of areas, making it a valuable resource for students. In addition to covering the basic topics, the book also takes in advanced and modern subjects, such as neural networks, decision trees, fitting techniques and issues concerning limit or interval setting. Worked examples and case studies illustrate the techniques presented, and end-of-chapter exercises help test the reader's understanding of the material.
R 3,091.15
Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences
Guides readers through the quantitative data analysis process including contextualizing data within a research situation, connecting data to the appropriate statistical tests, and drawing valid conclusions Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences presents a clear and accessible introduction to the basics of quantitative data analysis and focuses on how to use statistical tests as a key tool for analyzing research data. The book presents the entire data analysis process as a cyclical, multiphase process and addresses the processes of exploratory analysis, decision-making for performing parametric or nonparametric analysis, and practical significance determination. In addition, the author details how data analysis is used to reveal the underlying patterns and relationships between the variables and connects those trends to the data’s contextual situation. Filling the gap in quantitative data analysis literature, this book teaches the methods and thought processes behind data analysis, rather than how to perform the study itself or how to perform individual statistical tests. With a clear and conversational style, readers are provided with a better understanding of the overall structure and methodology behind performing a data analysis as well as the needed techniques to make informed, meaningful decisions during data analysis. The book features numerous data analysis examples in order to emphasize the decision and thought processes that are best followed, and self-contained sections throughout separate the statistical data analysis from the detailed discussion of the concepts allowing readers to reference a specific section of the book for immediate solutions to problems and/or applications. Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences also features coverage of the following: • The overall methodology and research mind-set for how to approach quantitative data analysis and how to use statistics tests as part of research data analysis • A comprehensive understanding of the data, its connection to a research situation, and the most appropriate statistical tests for the data • Numerous data analysis problems and worked-out examples to illustrate the decision and thought processes that reveal underlying patterns and trends • Detailed examples of the main concepts to aid readers in gaining the needed skills to perform a full analysis of research problems • A conversational tone to effectively introduce readers to the basics of how to perform data analysis as well as make meaningful decisions during data analysis Introduction to Quantitative Data Analysis in the Behavioral and Social Sciences is an ideal textbook for upper-undergraduate and graduate-level research method courses in the behavioral and social sciences, statistics, and engineering. This book is also an appropriate reference for practitioners who require a review of quantitative research methods. Michael J. Albers, P
R 3,288.00
Introduction to Multilevel Modeling Techniques
Multilevel modelling is a data analysis method that is frequently used to investigate hierarchal data structures in educational, behavioural, health, and social sciences disciplines. Multilevel data analysis exploits data structures that cannot be adequately investigated using single-level analytic methods such as multiple regression, path analysis, and structural modelling. This text offers a comprehensive treatment of multilevel models for univariate and multivariate outcomes. It explores their similarities and differences and demonstrates why one model may be more appropriate than another, given the research objectives. New to this edition: An expanded focus on the nature of different types of multilevel data structures (e.g., cross-sectional, longitudinal, cross-classified, etc.) for addressing specific research goals; Varied modelling methods for examining longitudinal data including random-effect and fixed-effect approaches; Expanded coverage illustrating different model-building sequences and how to use results to identify possible model improvements; An expanded set of applied examples used throughout the text; Use of four different software packages (i.e., Mplus, R, SPSS, Stata), with selected examples of model-building input files included in the chapter appendices and a more complete set of files available online.This is an ideal text for graduate courses on multilevel, longitudinal, latent variable modelling, multivariate statistics, or advanced quantitative techniques taught in psychology, business, education, health, and sociology. Recommended prerequisites are introductory univariate and multivariate statistics.
R 3,450.08
Introduction to Categorical Data Analysis
A valuable new edition of a standard reference The use of statistical methods for categorical data has increased dramatically, particularly for applications in the biomedical and social sciences. An Introduction to Categorical Data Analysis, Third Edition summarizes these methods and shows readers how to use them using software. Readers will find a unified generalized linear models approach that connects logistic regression and loglinear models for discrete data with normal regression for continuous data. Adding to the value in the new edition is: • Illustrations of the use of R software to perform all the analyses in the book • A new chapter on alternative methods for categorical data, including smoothing and regularization methods (such as the lasso), classification methods such as linear discriminant analysis and classification trees, and cluster analysis • New sections in many chapters introducing the Bayesian approach for the methods of that chapter • More than 70 analyses of data sets to illustrate application of the methods, and about 200 exercises, many containing other data sets • An appendix showing how to use SAS, Stata, and SPSS, and an appendix with short solutions to most odd-numbered exercises Written in an applied, nontechnical style, this book illustrates the methods using a wide variety of real data, including medical clinical trials, environmental questions, drug use by teenagers, horseshoe crab mating, basketball shooting, correlates of happiness, and much more. An Introduction to Categorical Data Analysis, Third Edition is an invaluable tool for statisticians and biostatisticians as well as methodologists in the social and behavioral sciences, medicine and public health, marketing, education, and the biological and agricultural sciences.
R 4,677.00
Secondary Data Analysis
In recent decades, social work and other social science research disciplines have become increasingly reliant on large secondary data sets, which have increased in both number and accessibility. When starting a new research project, how does one determine whether to use a secondary data set? Which of the thousands available should be used? This invaluable and expertly written guide provides an in-depth introduction to 29 of the most widely used data sets in socialwork, such as the Early Childhood Longitudinal Study, the National Health and Nutrition Examination Survey, and the U.S. Census. This book also examines the years covered by these cross-sectional and longitudinal data sets, the units of analysis, and the sample sizes.Readers will learn where to find the data and the key variables contained within, and how to use them in SAS and Stata. Screen shots guide researchers through data sets in a step-by-step process: how to download the data, how to merge it with other data sets, and how to program it when necessary. Each section also profiles studies that have used the respective data sets, giving researchers a clear feel for the depth and range of questions that a given data source can be used to answer, like theuse of government data to explore issues ranging from pathways out of poverty to the relationship between marital dissolution and women's health and well-being. Exceptionally well calibrated and filled with real-world examples, this pocket guide will give beginning and advanced researchers a comprehensive understanding of these data sets that they can use in their research on clinical, policy, and other types studies.
R 2,554.85
Everything You Wanted to Know About Data Analysis and Fitting but Were Afraid to Ask
These notes describe how to average and fit numerical data that have been obtained either by simulation or measurement. Following an introduction on how to estimate various average values, they discuss how to determine error bars on those estimates, and how to proceed for combinations of measured values. Techniques for fitting data to a given set of models will be described in the second part of these notes. This primer equips readers to properly derive the results covered, presenting the content in a style suitable for a physics audience. It also includes scripts in python, perl and gnuplot for performing a number of tasks in data analysis and fitting, thereby providing readers with a useful reference guide.
R 1,835.75
R 519.00
Applied Multivariate Data Analysis
An easy to read survey of data analysis, linear regression models and analysis of variance. The extensive development of the linear model includes the use of the linear model approach to analysis of variance provides a strong link to statistical software packages, and is complemented by a thorough overview of theory. It is assumed that the reader has the background equivalent to an introductory book in statistical inference. Can be read easily by those who have had brief exposure to calculus and linear algebra. Intended for first year graduate students in business, social and the biological sciences. Provides the student with the necessary statistics background for a course in research methodology. In addition, undergraduate statistics majors will find this text useful as a survey of linear models and their applications.
R 1,612.95
Data Analysis
Data analysis has been a hot topic for a number of years, and many future data scientists have backgrounds that are relatively light in mathematics. This slim volume provides a very approachable guide to the techniques of the subject, designed with such people in mind. Formulae are kept to a minimum, but the book's scope is broad, introducing the basic ideas of probability and statistics and more advanced techniques such as generalised linear models, classificationusing logistic regression, and support-vector machines.An essential feature of the book is that it does not tie to any particular software. The methods introduced in this book could also be implemented using any other statistical software and applying any major statistical package. Academically, the book amounts to a first course, practical for those at the undergraduate level, either as part of a mathematics/statistics degree or as a data-oriented option for a non-mathematics degree. The book appeals to would-be data scientists who may be formula shy. However, it could also be a relevant purchase for statisticians and mathematicians, for whom data science is a new departure, overall appealing to any computer-literate reader with data to analyse.
R 3,867.00
Introduction to Python in Earth Science Data Analysis
This textbook introduces the use of Python programming for exploring and modelling data in the field of Earth Sciences. It drives the reader from his very first steps with Python, like setting up the environment and starting writing the first lines of codes, to proficient use in visualizing, analyzing, and modelling data in the field of Earth Science. Each chapter contains explicative examples of code, and each script is commented in detail. The book is minded for very beginners in Python programming, and it can be used in teaching courses at master or PhD levels. Also, Early careers and experienced researchers who would like to start learning Python programming for the solution of geological problems will benefit the reading of the book.
R 3,284.80
Introduction to Functional Data Analysis
Introduction to Functional Data Analysis provides a concise textbook introduction to the field. It explains how to analyze functional data, both at exploratory and inferential levels. It also provides a systematic and accessible exposition of the methodology and the required mathematical framework.The book can be used as textbook for a semester-long course on FDA for advanced undergraduate or MS statistics majors, as well as for MS and PhD students in other disciplines, including applied mathematics, environmental science, public health, medical research, geophysical sciences and economics. It can also be used for self-study and as a reference for researchers in those fields who wish to acquire solid understanding of FDA methodology and practical guidance for its implementation. Each chapter contains plentiful examples of relevant R code and theoretical and data analytic problems.The material of the book can be roughly divided into four parts of approximately equal length: 1) basic concepts and techniques of FDA, 2) functional regression models, 3) sparse and dependent functional data, and 4) introduction to the Hilbert space framework of FDA. The book assumes advanced undergraduate background in calculus, linear algebra, distributional probability theory, foundations of statistical inference, and some familiarity with R programming. Other required statistics background is provided in scalar settings before the related functional concepts are developed. Most chapters end with references to more advanced research for those who wish to gain a more in-depth understanding of a specific topic.
R 8,234.87
Social Science Data Analysis
In this book, basic statistical knowledge is conveyed in an understandable and application-oriented manner. The readers should be enabled to carry out their own empirical evaluations and to understand or critically reflect on existing analyses.The third edition is extended by a detailed chapter on the logic of significance tests.A replication syntax for the statistical program Stata is provided online as supplementary material.
R 2,729.91
Data Analysis in Management with SPSS Software
This book provides readers with a greater understanding of a variety of statistical techniques along with the procedure to use the most popular statistical software package SPSS. It strengthens the intuitive understanding of the material, thereby increasing the ability to successfully analyze data in the future. The book provides more control in the analysis of data so that readers can apply the techniques to a broader spectrum of research problems. This book focuses on providing readers with the knowledge and skills needed to carry out research in management, humanities, social and behavioural sciences by using SPSS.
R 1,993.38
Data Analysis
Bridging the gap between statistical theory and physical experiment, this is a thorough introduction to the statistical methods used in the experimental physical sciences and to the numerical methods used to implement them. The treatment emphasises concise but rigorous mathematics but always retains its focus on applications. Readers are assumed to have a sound basic knowledge of differential and integral calculus and some knowledge of vectors and matrices. After an introduction to probability, random variables, computer generation of random numbers and important distributions, the book turns to statistical samples, the maximum likelihood method, and the testing of statistical hypotheses. The discussion concludes with several important statistical methods: least squares, analysis of variance, polynomial regression, and analysis of time series. Appendices provide the necessary methods of matrix algebra, combinatorics, and many sets of useful algorithms and formulae.
R 2,236.78
Quantitative Data Analysis in Education
This book provides a refreshing and user-friendly guide to quantitative data analysis in education for students and researchers. It assumes absolutely no prior knowledge of quantitative methods or statistics. Beginning with the very basics, it provides the reader with the knowledge and skills necessary to be able to undertake routine quantitative data analysis to a level expected of published research. Rather than focusing on teaching statistics through mathematical formulae, the book places an emphasis on using SPSS to gain a real feel for the data and an intuitive grasp of the main concepts and techniques involved. Drawing extensively upon up-to-date and relevant examples, the reader will be encouraged to think critically about quantitative research and its potential as well as its limitations in relation to education.Packed with helpful features, this book: provides illustrated step-by-step guides showing how to use SPSS, with plenty of exercises to encourage the reader to practice and consolidate their new skillsmakes extensive use of real-life educational datasets derived from national surveys in the US and UK to illustrate key points and to bring the material to lifehas a companion website that contains all of the educational datasets used in the book to download as well as comprehensive answers to exercises and a range of other useful resources that are regularly updated.The book will therefore appeal not only to undergraduate and postgraduate students but also to more established and seasoned educational researchers and lecturers and professors who have tended to avoid or shy away from quantitative methods.
R 2,711.00
Introduction to Statistical Analysis of Laboratory Data
Introduction to Statistical Analysis of Laboratory Data presents a detailed discussion of important statistical concepts and methods of data presentation and analysis Provides detailed discussions on statistical applications including a comprehensive package of statistical tools that are specific to the laboratory experiment processIntroduces terminology used in many applications such as the interpretation of assay design and validation as well as “fit for purpose” procedures including real world examplesIncludes a rigorous review of statistical quality control procedures in laboratory methodologies and influences on capabilitiesPresents methodologies used in the areas such as method comparison procedures, limit and bias detection, outlier analysis and detecting sources of variationAnalysis of robustness and ruggedness including multivariate influences on response are introduced to account for controllable/uncontrollable laboratory conditions
R 4,362.00
Data Analysis and Presentation Skills
Data Analysis and Presentation Skills: An Introduction for the Life and Medical Sciences is an invaluable text allowing students to develop appropriate key skills when designing experiments, generating results, analysing data and ultimately presenting findings to academics and referees. Taking a hands-on approach, each of these key areas is introduced clearly and carefully, showing how to access and evaluate information using a variety of resources. Basic analytical theory is gradually introduced alongside practical applications to enhance student understanding. The reader is shown how to present data in charts using Microsoft Excel and statistical analysis is carefully explained showing clearly how to manipulate data in spreadsheets and analyse the results using commonly used tests. A section is also included on the use of PowerPoint as well as giving advice on how to prepare a short talk or seminar. Includes numerous relevant examples and case studies drawn from the Life SciencesGuidance on how to complete and present practical and project work through to postgraduate dissertation.Clear step-by-step introduction to Microsoft Excel, presentation skills and statistical analysis.Invaluable for all students within the Life and Medical Sciences
R 6,381.00
Statistical Analysis of Discrete Data
The Statistical Analysis of Discrete Data provides an introduction to cur rent statistical methods for analyzing discrete response data. The book can be used as a course text for graduate students and as a reference for researchers who analyze discrete data. The book's mathematical prereq uisites are linear algebra and elementary advanced calculus. It assumes a basic statistics course which includes some decision theory, and knowledge of classical linear model theory for continuous response data. Problems are provided at the end of each chapter to give the reader an opportunity to ap ply the methods in the text, to explore extensions of the material covered, and to analyze data with discrete responses. In the text examples, and in the problems, we have sought to include interesting data sets from a wide variety of fields including political science, medicine, nuclear engineering, sociology, ecology, cancer research, library science, and biology. Although there are several texts available on discrete data analysis, we felt there was a need for a book which incorporated some of the myriad recent research advances. Our motivation was to introduce the subject by emphasizing its ties to the well-known theories of linear models, experi mental design, and regression diagnostics, as well as to describe alterna tive methodologies (Bayesian, smoothing, etc. ); the latter are based on the premise that external information is available. These overriding goals, to gether with our own experiences and biases, have governed our choice of topics.
R 1,569.73
Applied Functional Data Analysis
This book contains the ideas of functional data analysis by a number of case studies. The case studies are accessible to research workers in a wide range of disciplines. Every reader should gain not only a specific understanding of the methods of functional data analysis, but more importantly a general insight into the underlying patterns of thought. There is an associated web site with MATLABr and S?PLUSr implementations of the methods discussed.
R 5,447.96
Essentials of Multivariate Data Analysis
Since most datasets contain a number of variables, multivariate methods are helpful in answering a variety of research questions. Accessible to students and researchers without a substantial background in statistics or mathematics, Essentials of Multivariate Data Analysis explains the usefulness of multivariate methods in applied research. Unlike most books on multivariate methods, this one makes straightforward analyses easy to perform for those who are unfamiliar with advanced mathematical formulae. An easily understood dataset is used throughout to illustrate the techniques. The accompanying add-in for Microsoft Excel® can be used to carry out the analyses in the text. The dataset and Excel add-in are available for download on the book’s CRC Press web page.Providing a firm foundation in the most commonly used multivariate techniques, this text helps readers choose the appropriate method, learn how to apply it, and understand how to interpret the results. It prepares them for more complex analyses using software such as Minitab®, R, SAS, SPSS, and Stata.
R 3,415.11
Introduction to Survey Research, Polling, and Data Analysis
"The writing style is mature and experienced. There is noticeable care in the presentation of materials and appropriate sensitivity to the intended audience. . . . The book categorizes large bodies of wisdom in the conduct of survey research over the past half century and provides ′′rules-of-thumb′′ guidance to the production, analysis, and consumption of survey data." --Steve Seitz, University of Illinois "The authors have provided a well-written update of their textbook, which will be useful as a textbook or supplement in upper-division undergraduate or introductory graduate courses on survey research, statistical data analysis, or research design. The book provides readers with the knowledge that enables them to evaluate the results of survey research. It sensitizes the readers to potential problems that arise from flaws in research design, sampling, questionnaire construction, data collection, coding, data analysis techniques, and improper presentation or interpretation of findings." --Mike Margolis, University of Cincinnati Why should we believe the results of surveys based on relatively few interviews? What are the appropriate cautions in interpreting survey reports? Aimed at answering these questions, An Introduction to Survey Research, Polling, and Data Analysis, Third Edition describes how surveys are conducted in such a way that they can be believed, explains how to read statistical reports and analyze data, and provides guidelines that are useful in evaluating polls. Using frequent examples from contemporary, large national surveys and polls as well as from the media, the authors stress understanding tables thoroughly before moving to interval statistics. In addition, they cover survey design, sampling and question-writing steps, interviewing and coding strategies, and survey analysis from frequency distributions and cross-tabulations to control tables and correlation/regression. They also cover the ethics of survey research and how to read and write survey research reports. Readers of this book will learn to distinguish good from bad polls and to better design and conduct their own surveys and polls.
R 7,155.37
Introduction to Time Series Analysis and Forecasting
A time series is a set of repeated measurements of the same phenomenon taken sequentially over time. Capturing the data creates a time series "memory" to document correlations or lack, and to help them make decisions based on this data.;This introductory exploration of the principle approaches to time series and forecasting processes explains theories and applications for the social science student. Readers can investigate examples from public opinion research, policy analysis, political science, economics and sociology using SAS and SPSS programme syntax and data sets available on a supporting website.
R 4,090.00
Data Analysis Using SAS
Data Analysis Using SAS offers a comprehensive core text focused on key concepts and techniques in quantitative data analysis using the most current SAS commands and programming language. The coverage of the text is more evenly balanced among statistical analysis, SAS programming, and data/file management than any available text on the market. It provides students with a hands-on, exercise-heavy method for learning basic to intermediate SAS commands while understanding how to apply statistics and reasoning to real-world problems. Designed to be used in order of teaching preference by instructor, the book is comprised of two primary sections: the first half of the text instructs students in techniques for data and file managements such as concatenating and merging files, conditional or repetitive processing of variables, and observations. The second half of the text goes into great depth on the most common statistical techniques and concepts - descriptive statistics, correlation, analysis of variance, and regression - used to analyze data in the social, behavioral, and health sciences using SAS commands. A student study comes replete with a multitude of computer programs, their output, specific details on how to check assumptions, as well as all data sets used in the book. Data Analysis Using SAS is a complete resource for Data Analysis I and II, Statistics I and II, Quantitative Reasoning, and SAS Programming courses across the social and behavioral sciences and health - especially those that carry a lab component.
R 7,191.76
R 759.00
Introduction to Statistical Learning
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance, marketing, and astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, deep learning, survival analysis, multiple testing, and more. Color graphics and real-world examples are used to illustrate the methods presented. This book is targeted at statisticians and non-statisticians alike, who wish to use cutting-edge statistical learning techniques to analyze their data. Four of the authors co-wrote An Introduction to Statistical Learning, With Applications in R (ISLR), which has become a mainstay of undergraduate and graduate classrooms worldwide, as well as an important reference book for data scientists. One of the keys to its success was that each chapter contains a tutorial on implementing the analyses and methods presented in the R scientific computing environment. However, in recent years Python has become a popular language for data science, and there has been increasing demand for a Python-based alternative to ISLR. Hence, this book (ISLP) covers the same materials as ISLR but with labs implemented in Python. These labs will be useful both for Python novices, as well as experienced users.
R 4,396.00
Applied Multivariate Data Analysis
A Second Course in Statistics The past decade has seen a tremendous increase in the use of statistical data analysis and in the availability of both computers and statistical software. Business and government professionals, as well as academic researchers, are now regularly employing techniques that go far beyond the standard two-semester, introductory course in statistics. Even though for this group of users shorl courses in various specialized topics are often available, there is a need to improve the statistics training of future users of statistics while they are still at colleges and universities. In addition, there is a need for a survey reference text for the many practitioners who cannot obtain specialized courses. With the exception of the statistics major, most university students do not have sufficient time in their programs to enroll in a variety of specialized one-semester courses, such as data analysis, linear models, experimental de sign, multivariate methods, contingency tables, logistic regression, and so on. There is a need for a second survey course that covers a wide variety of these techniques in an integrated fashion. It is also important that this sec ond course combine an overview of theory with an opportunity to practice, including the use of statistical software and the interpretation of results obtained from real däta.
R 3,113.73
Spectral Analysis of Time-Series Data
This book provides a thorough introduction to methods for detecting and describing cyclic patterns in time-series data. It is written both for researchers and students new to the area and for those who have already collected time-series data but wish to learn new ways of understanding and presenting them. Facilitating the interpretation of observations of behavior, physiology, mood, perceptual threshold, social indicator variables, and other responses, the book focuses on practical applications and requires much less mathematical background than most comparable texts. Using real data sets and currently available software (SPSS for Windows), the author employs extensive examples to clarify key concepts. Topics covered include research design issues, preliminary data screening, identification and description of cycles, summary of results across time series, and assessment of relations between time series. Also considered are theoretical questions, problems of interpretation, and potential sources of artifact.
R 2,166.00
