Download or read book Canonical Correlation Analysis of Unobservable Relationships in the New Product Process written by Roger Calantone and published by . This book was released on 2014 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Previous studies of success and failure in new product development have examined the effects of numerous variables upon new product outcomes. Some of these variables are controllable by the firm; many are not. This study employs canonical correlation analysis to investigate the nature of the interactions within and between two sets of variables (controllable and environmental) in the new product process. Several insightful implications for new product management are obtained and presented. Among these is the need for production and marketing synergy in new product development.
Download or read book Escalation of Commitment in the New Product Development Process by Jeffrey Brian Schmidt written by Jeffrey Brian Schmidt and published by . This book was released on 1996 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Canonical Correlation and Marketing Research written by Jagdish N. Sheth and published by Marketing Classics Press. This book was released on 2011-06-30 with total page 23 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Managing New Technology Development written by William E. Souder and published by McGraw Hill Professional. This book was released on 1994 with total page 372 pages. Available in PDF, EPUB and Kindle. Book excerpt: New technology development starts with the generation of an idea. It ends with that idea's commercial application: a new product or a new service. In Between is a complex sequence of stages demanding specialized management methods. With this in depth survey, R&D, marketing, and engineering managers can learn from the foremost experts about the most successful, proven practices and techniques-for managing all the stages of new technology development.
Download or read book Canonical Correlation Analysis of Competitive Market Structure written by Johny K. Johansson and published by . This book was released on 1973 with total page 34 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Science and Technology Management Bibliography 1993 written by Thomas E. Clarke and published by . This book was released on 1993 with total page 958 pages. Available in PDF, EPUB and Kindle. Book excerpt: This bibliography is the fourth edition in a series of bibliographies over the past 20 years containing references to articles, books, conference papers and reports concerned with the management of technological innovation and technical entrepreneurship. Previous editions had the title R&D Management Bibliography. This edition contains over 10,000 references of which 3,000 were contained in the 1981 edition. The section on government science and technology policy is one of the largest in the book.
Download or read book Canonical Correlation Analysis written by Thompson and published by . This book was released on with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Statistical Analysis of Management Data written by Hubert Gatignon and published by Springer Science & Business Media. This book was released on 2013-10-17 with total page 576 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Analysis of Management Data provides a comprehensive approach to multivariate statistical analyses that are important for researchers in all fields of management, including finance, production, accounting, marketing, strategy, technology, and human resources. This book is especially designed to provide doctoral students with a theoretical knowledge of the concepts underlying the most important multivariate techniques and an overview of actual applications. It offers a clear, succinct exposition of each technique with emphasis on when each technique is appropriate and how to use it. This third edition, fully revised, updated, and expanded, reflects the most current evolution in the methods for data analysis in management and the social sciences. In particular, this edition includes: · A new chapter on the analysis of mediation and moderation effects · Examples using STATA for most of the statistical methods · Example of XLSTAT applications Featuring numerous examples, the book may serve as an advanced text or as a resource for applied researchers in industry who want to understand the foundations of the methods particularly relevant and typically used in management research, and to learn how they can be applied using widely available statistical software.
Download or read book Modern Methods for Epidemiology written by Yu-Kang Tu and published by Springer Science & Business Media. This book was released on 2012-05-22 with total page 315 pages. Available in PDF, EPUB and Kindle. Book excerpt: Routine applications of advanced statistical methods on real data have become possible in the last ten years because desktop computers have become much more powerful and cheaper. However, proper understanding of the challenging statistical theory behind those methods remains essential for correct application and interpretation, and rarely seen in the medical literature. Modern Methods for Epidemiology provides a concise introduction to recent development in statistical methodologies for epidemiological and biomedical researchers. Many of these methods have become indispensible tools for researchers working in epidemiology and medicine but are rarely discussed in details by standard textbooks of biostatistics or epidemiology. Contributors of this book are experienced researchers and experts in their respective fields. This textbook provides a solid starting point for those who are new to epidemiology, and for those looking for guidance in more modern statistical approaches to observational epidemiology. Epidemiological and biomedical researchers who wish to overcome the mathematical barrier of applying those methods to their research will find this book an accessible and helpful reference for self-learning and research. This book is also a good source for teaching postgraduate students in medical statistics or epidemiology.
Download or read book Data Science for Business and Decision Making written by Luiz Paulo Fávero and published by Academic Press. This book was released on 2019-04-11 with total page 1240 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Science for Business and Decision Making covers both statistics and operations research while most competing textbooks focus on one or the other. As a result, the book more clearly defines the principles of business analytics for those who want to apply quantitative methods in their work. Its emphasis reflects the importance of regression, optimization and simulation for practitioners of business analytics. Each chapter uses a didactic format that is followed by exercises and answers. Freely-accessible datasets enable students and professionals to work with Excel, Stata Statistical Software®, and IBM SPSS Statistics Software®. Combines statistics and operations research modeling to teach the principles of business analytics Written for students who want to apply statistics, optimization and multivariate modeling to gain competitive advantages in business Shows how powerful software packages, such as SPSS and Stata, can create graphical and numerical outputs
Download or read book Mixed Effects Models for Complex Data written by Lang Wu and published by CRC Press. This book was released on 2009-11-11 with total page 431 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although standard mixed effects models are useful in a range of studies, other approaches must often be used in correlation with them when studying complex or incomplete data. Mixed Effects Models for Complex Data discusses commonly used mixed effects models and presents appropriate approaches to address dropouts, missing data, measurement errors, censoring, and outliers. For each class of mixed effects model, the author reviews the corresponding class of regression model for cross-sectional data. An overview of general models and methods, along with motivating examples After presenting real data examples and outlining general approaches to the analysis of longitudinal/clustered data and incomplete data, the book introduces linear mixed effects (LME) models, generalized linear mixed models (GLMMs), nonlinear mixed effects (NLME) models, and semiparametric and nonparametric mixed effects models. It also includes general approaches for the analysis of complex data with missing values, measurement errors, censoring, and outliers. Self-contained coverage of specific topics Subsequent chapters delve more deeply into missing data problems, covariate measurement errors, and censored responses in mixed effects models. Focusing on incomplete data, the book also covers survival and frailty models, joint models of survival and longitudinal data, robust methods for mixed effects models, marginal generalized estimating equation (GEE) models for longitudinal or clustered data, and Bayesian methods for mixed effects models. Background material In the appendix, the author provides background information, such as likelihood theory, the Gibbs sampler, rejection and importance sampling methods, numerical integration methods, optimization methods, bootstrap, and matrix algebra. Failure to properly address missing data, measurement errors, and other issues in statistical analyses can lead to severely biased or misleading results. This book explores the biases that arise when naïve methods are used and shows which approaches should be used to achieve accurate results in longitudinal data analysis.
Download or read book SAS STAT 9 3 User s Guide written by Sas Institute and published by . This book was released on 2011-07 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The GLIMMIX procedure fits and analyzes generalized linear mixed models (GLMM), models with random effects for data that can be nonnormally distributed. This title is also available online.
Download or read book Principal Component Analysis written by I.T. Jolliffe and published by Springer Science & Business Media. This book was released on 2013-03-09 with total page 283 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principal component analysis is probably the oldest and best known of the It was first introduced by Pearson (1901), techniques ofmultivariate analysis. and developed independently by Hotelling (1933). Like many multivariate methods, it was not widely used until the advent of electronic computers, but it is now weIl entrenched in virtually every statistical computer package. The central idea of principal component analysis is to reduce the dimen sionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. Computation of the principal components reduces to the solution of an eigenvalue-eigenvector problem for a positive-semidefinite symmetrie matrix. Thus, the definition and computation of principal components are straightforward but, as will be seen, this apparently simple technique has a wide variety of different applications, as weIl as a number of different deri vations. Any feelings that principal component analysis is a narrow subject should soon be dispelled by the present book; indeed some quite broad topics which are related to principal component analysis receive no more than a brief mention in the final two chapters.
Download or read book Machine Learning in Python for Dynamic Process Systems written by Ankur Kumar and published by MLforPSE. This book was released on 2023-06-01 with total page 208 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is designed to help readers gain a working-level knowledge of machine learning-based dynamic process modeling techniques that have proven useful in process industry. Readers can leverage the concepts learned to build advanced solutions for process monitoring, soft sensing, inferential modeling, predictive maintenance, and process control for dynamic systems. The application-focused approach of the book is reader friendly and easily digestible to the practicing and aspiring process engineers, and data scientists. The authors of this book have drawn from their years of experience in developing data-driven industrial solutions to provide a guided tour along the wide range of available ML methods and declutter the world of machine learning for dynamic process modeling. Upon completion, readers will be able to confidently navigate the system identification literature and make judicious selection of modeling approaches suitable for their problems. This book has been divided into three parts. Part 1 of the book provides perspectives on the importance of ML for dynamic process modeling and lays down the basic foundations of ML-DPM (machine learning for dynamic process modeling). Part 2 provides in-detail presentation of classical ML techniques and has been written keeping in mind the different modeling requirements and process characteristics that determine a model’s suitability for a problem at hand. These include, amongst others, presence of multiple correlated outputs, process nonlinearity, need for low model bias, need to model disturbance signal accurately, etc. Part 3 is focused on artificial neural networks and deep learning. The following topics are broadly covered: · Exploratory analysis of dynamic dataset · Best practices for dynamic modeling · Linear and discrete-time classical parametric and non-parametric models · State-space models for MIMO systems · Nonlinear system identification and closed-loop identification · Neural networks-based dynamic process modeling
Download or read book Statistics for Marketing and Consumer Research written by Mario Mazzocchi and published by SAGE. This book was released on 2008-05-22 with total page 433 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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
Download or read book Theory Based Data Analysis for the Social Sciences written by Carol S. Aneshensel and published by SAGE. This book was released on 2013 with total page 473 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the elaboration model for the multivariate analysis of observational quantitative data. This model entails the systematic introduction of "third variables" to the analysis of a focal relationship between one independent and one dependent variable to ascertain whether an inference of causality is justified. Two complementary strategies are used: an exclusionary strategy that rules out alternative explanations such as spuriousness and redundancy with competing theories, and an inclusive strategy that connects the focal relationship to a network of other relationships, including the hypothesized causal mechanisms linking the focal independent variable to the focal dependent variable. The primary emphasis is on the translation of theory into a logical analytic strategy and the interpretation of results. The elaboration model is applied with case studies drawn from newly published research that serve as prototypes for aligning theory and the data analytic plan used to test it; these studies are drawn from a wide range of substantive topics in the social sciences, such as emotion management in the workplace, subjective age identification during the transition to adulthood, and the relationship between religious and paranormal beliefs. The second application of the elaboration model is in the form of original data analysis presented in two Analysis Journals that are integrated throughout the text and implement the full elaboration model. Using real data, not contrived examples, the text provides a step-by-step guide through the process of integrating theory with data analysis in order to arrive at meaningful answers to research questions.
Download or read book Unobserved Variables written by David J. Bartholomew and published by Springer Science & Business Media. This book was released on 2013-09-07 with total page 87 pages. Available in PDF, EPUB and Kindle. Book excerpt: The classical statistical problem typically involves a probability distribution which depends on a number of unknown parameters. The form of the distribution may be known, partially or completely, and inferences have to be made on the basis of a sample of observations drawn from the distribution; often, but not necessarily, a random sample. This brief deals with problems where some of the sample members are either unobserved or hypothetical, the latter category being introduced as a means of better explaining the data. Sometimes we are interested in these kinds of variable themselves and sometimes in the parameters of the distribution. Many problems that can be cast into this form are treated. These include: missing data, mixtures, latent variables, time series and social measurement problems. Although all can be accommodated within a Bayesian framework, most are best treated from first principles.
