## Essentials Of Multivariate Data Analysis

Author: Neil H. Spencer
Publisher: CRC Press
ISBN: 1466584785
Size: 51.30 MB
Format: PDF, Kindle
View: 487

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.

## Essential First Steps To Data Analysis

Author: Carol S. Parke
Publisher: SAGE Publications
ISBN: 1483316025
Size: 35.50 MB
Format: PDF, Kindle
View: 6709

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.

## The Essentials Of Marketing Research

Author: Lawrence S. Silver
Publisher: Routledge
ISBN: 041589929X
Size: 47.26 MB
Format: PDF, Kindle
View: 2519

Identifying and assessing the ways in which changes in the marketing mix affect consumer behavior is key to a successful marketing strategy. The Essentials of Marketing Research guides the student in designing, conducting and interpreting marketing research. This comprehensive textbook covers the full range of topics, including: Secondary research and data mining Internet marketing research Qualitative and exploratory research Statistical analysis Marketing research ethics With learning objectives at the beginning of each chapter, a host of cases and a comprehensive companion website, this book offers a range of tools to help students develop and test their research and analytical skills.

## Mathematical Tools For Applied Multivariate Analysis

Author: Paul E. Green
ISBN: 1483214044
Size: 50.11 MB
Format: PDF, ePub, Docs
View: 5295

Mathematical Tools for Applied Multivariate Analysis provides information pertinent to the aspects of transformational geometry, matrix algebra, and the calculus that are most relevant for the study of multivariate analysis. This book discusses the mathematical foundations of applied multivariate analysis. Organized into six chapters, this book begins with an overview of the three problems in multiple regression, principal components analysis, and multiple discriminant analysis. This text then presents a standard treatment of the mechanics of matrix algebra, including definitions and operations on matrices, vectors, and determinants. Other chapters consider the topics of eigenstructures and linear transformations that are important to the understanding of multivariate techniques. This book discusses as well the eigenstructures and quadratic forms. The final chapter deals with the geometric aspects of linear transformations. This book is a valuable resource for students.

## Essentials Of Business Research Methods

Author: Joseph F. Hair, Jr
Publisher: Routledge
ISBN: 1317471237
Size: 79.18 MB
Format: PDF, ePub
View: 3604

Managers increasingly must make decisions based on almost unlimited information. How can they navigate and organize this vast amount of data? Essentials of Business Research Methods provides research techniques for people who aren't data analysts. The authors offer a straightforward, hands-on approach to the vital managerial process of gathering and using data to make clear business decisions. They include such critical topics as the increasing role of online research, ethical issues, data mining, customer relationship management, and how to conduct information-gathering activities more effectively in a rapidly changing business environment. This is the only such book that includes a chapter on qualitative data analysis, and the coverage of quantitative data analysis is more extensive and much easier to understand than in other works. The book features a realistic continuing case throughout the text that enables students to see how business research information is used in the real world. It includes applied research examples in all chapters, as well as Ethical Dilemma mini - cases, and interactive Internet applications and exercises.

## Essentials Of Nursing Research

Author: Denise F. Polit
Publisher: Lippincott Williams & Wilkins
ISBN: 1609130049
Size: 10.31 MB
Format: PDF, Mobi
View: 6161

Essentials of Nursing Research is designed to teach students how to read, understand, analyze, and evaluate research reports in nursing practice. The Seventh Edition has been updated with stronger coverage of evidence-based practice, including content on how to read, interpret, and critique systematic reviews, which are considered by many to be a cornerstone of evidence-based practice. Also included in the Seventh Edition: a more balanced presentation of medical and social science methods and nomenclature; enhanced coverage of qualitative research; and more.

## Machine Learning Essentials

Publisher: STHDA
ISBN: 1986406857
Size: 62.70 MB
Format: PDF, ePub
View: 4174

Discovering knowledge from big multivariate data, recorded every days, requires specialized machine learning techniques. This book presents an easy to use practical guide in R to compute the most popular machine learning methods for exploring real word data sets, as well as, for building predictive models. The main parts of the book include: A) Unsupervised learning methods, to explore and discover knowledge from a large multivariate data set using clustering and principal component methods. You will learn hierarchical clustering, k-means, principal component analysis and correspondence analysis methods. B) Regression analysis, to predict a quantitative outcome value using linear regression and non-linear regression strategies. C) Classification techniques, to predict a qualitative outcome value using logistic regression, discriminant analysis, naive bayes classifier and support vector machines. D) Advanced machine learning methods, to build robust regression and classification models using k-nearest neighbors methods, decision tree models, ensemble methods (bagging, random forest and boosting). E) Model selection methods, to select automatically the best combination of predictor variables for building an optimal predictive model. These include, best subsets selection methods, stepwise regression and penalized regression (ridge, lasso and elastic net regression models). We also present principal component-based regression methods, which are useful when the data contain multiple correlated predictor variables. F) Model validation and evaluation techniques for measuring the performance of a predictive model. G) Model diagnostics for detecting and fixing a potential problems in a predictive model. The book presents the basic principles of these tasks and provide many examples in R. This book offers solid guidance in data mining for students and researchers. Key features: - Covers machine learning algorithm and implementation - Key mathematical concepts are presented - Short, self-contained chapters with practical examples.

## Lattice

Author: Deepayan Sarkar
Publisher: Springer Science & Business Media
ISBN: 9780387759692
Size: 20.36 MB
Format: PDF, Mobi
View: 7690

Written by the author of the lattice system, this book describes lattice in considerable depth, beginning with the essentials and systematically delving into specific low levels details as necessary. No prior experience with lattice is required to read the book, although basic familiarity with R is assumed. The book contains close to 150 figures produced with lattice. Many of the examples emphasize principles of good graphical design; almost all use real data sets that are publicly available in various R packages. All code and figures in the book are also available online, along with supplementary material covering more advanced topics.

## Essentials Of Cognitive Neuroscience

Publisher: John Wiley & Sons
ISBN: 1118468279
Size: 64.53 MB
Format: PDF, ePub, Docs
View: 1169

Essentials of Cognitive Neuroscience guides undergraduate and early-stage graduate students with no previous neuroscientific background through the fundamental principles and themes in a concise, organized, and engaging manner. Provides students with the foundation to understand primary literature, recognize current controversies in the field, and engage in discussions on cognitive neuroscience and its future Introduces important experimental methods and techniques integrated throughout the text Assists student comprehension through four-color images and thorough pedagogical resources throughout the text Accompanied by a robust website with multiple choice questions, experiment vidoes, fMRI data, web links and video narratives from a global group of leading scientists for students. For Instructors there are sample syllabi and exam questions

## Practical Data Analysis In Chemistry

Author: Marcel Maeder
Publisher: Elsevier
ISBN: 9780080548838
Size: 75.75 MB
Format: PDF, ePub
View: 7082

The majority of modern instruments are computerised and provide incredible amounts of data. Methods that take advantage of the flood of data are now available; importantly they do not emulate 'graph paper analyses' on the computer. Modern computational methods are able to give us insights into data, but analysis or data fitting in chemistry requires the quantitative understanding of chemical processes. The results of this analysis allows the modelling and prediction of processes under new conditions, therefore saving on extensive experimentation. Practical Data Analysis in Chemistry exemplifies every aspect of theory applicable to data analysis using a short program in a Matlab or Excel spreadsheet, enabling the reader to study the programs, play with them and observe what happens. Suitable data are generated for each example in short routines, this ensuring a clear understanding of the data structure. Chapter 2 includes a brief introduction to matrix algebra and its implementation in Matlab and Excel while Chapter 3 covers the theory required for the modelling of chemical processes. This is followed by an introduction to linear and non-linear least-squares fitting, each demonstrated with typical applications. Finally Chapter 5 comprises a collection of several methods for model-free data analyses. * Includes a solid introduction to the simulation of equilibrium processes and the simulation of complex kinetic processes. * Provides examples of routines that are easily adapted to the processes investigated by the reader * 'Model-based' analysis (linear and non-linear regression) and 'model-free' analysis are covered