Jmp For Mixed Models
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Jmp For Mixed Models
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Author : Ruth Hummel
language : en
Publisher:
Release Date : 2021-06-09
Jmp For Mixed Models written by Ruth Hummel and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-09 with categories.
Discover the power of mixed models with JMP and JMP Pro. Mixed models are now the mainstream method of choice for analyzing experimental data. Why? They are arguably the most straightforward and powerful way to handle correlated observations in designed experiments. Reaching well beyond standard linear models, mixed models enable you to make accurate and precise inferences about your experiments and to gain deeper understanding of sources of signal and noise in the system under study. Well-formed fixed and random effects generalize well and help you make the best data-driven decisions. JMP for Mixed Models brings together two of the strongest traditions in SAS software: mixed models and JMP. JMP's groundbreaking philosophy of tight integration of statistics with dynamic graphics is an ideal milieu within which to learn and apply mixed models, also known as hierarchical linear or multilevel models. If you are a scientist or engineer, the methods described herein can revolutionize how you analyze experimental data without the need to write code. Inside you'll find a rich collection of examples and a step-by-step approach to mixed model mastery. Topics include: Learning how to appropriately recognize, set up, and interpret fixed and random effects Extending analysis of variance (ANOVA) and linear regression to numerous mixed model designs Understanding how degrees of freedom work using Skeleton ANOVA Analyzing randomized block, split-plot, longitudinal, and repeated measures designs Introducing more advanced methods such as spatial covariance and generalized linear mixed models Simulating mixed models to assess power and other important sampling characteristics Providing a solid framework for understanding statistical modeling in general Improving perspective on modern dilemmas around Bayesian methods, p-values, and causal inference
Fundamentals Of Predictive Analytics With Jmp Second Edition
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Author : Ron Klimberg
language : en
Publisher: SAS Institute
Release Date : 2017-12-19
Fundamentals Of Predictive Analytics With Jmp Second Edition written by Ron Klimberg and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-19 with Mathematics categories.
Written for students in undergraduate and graduate statistics courses, as well as for the practitioner who wants to make better decisions from data and models, this updated and expanded second edition of Fundamentals of Predictive Analytics with JMP(R) bridges the gap between courses on basic statistics, which focus on univariate and bivariate analysis, and courses on data mining and predictive analytics. Going beyond the theoretical foundation, this book gives you the technical knowledge and problem-solving skills that you need to perform real-world multivariate data analysis. First, this book teaches you to recognize when it is appropriate to use a tool, what variables and data are required, and what the results might be. Second, it teaches you how to interpret the results and then, step-by-step, how and where to perform and evaluate the analysis in JMP . Using JMP 13 and JMP 13 Pro, this book offers the following new and enhanced features in an example-driven format: an add-in for Microsoft Excel Graph Builder dirty data visualization regression ANOVA logistic regression principal component analysis LASSO elastic net cluster analysis decision trees k-nearest neighbors neural networks bootstrap forests boosted trees text mining association rules model comparison With today’s emphasis on business intelligence, business analytics, and predictive analytics, this second edition is invaluable to anyone who needs to expand his or her knowledge of statistics and to apply real-world, problem-solving analysis. This book is part of the SAS Press program.
Linear Mixed Models In Practice
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Author : Geert Verbeke
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Linear Mixed Models In Practice written by Geert Verbeke and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Medical categories.
The dissemination of the MIXED procedure in SAS has provided a whole class of statistical models for routine use. We believe that both the ideas be hind the techniques and their implementation in SAS are not at all straight forward and users from various applied backgrounds, including the phar maceutical industry, have experienced difficulties in using the procedure effectively. Courses and consultancy on PROC MIXED have been in great demand in recent years, illustrating the clear need for resource material to aid the user. This book is intended as a contribution to bridging this gap. We hope the book will be of value to a wide audience, including applied statisticians and many biomedical researchers, particularly in the pharma ceutical industry, medical and public health research organizations, con tract research organizations, and academic departments. This implies that our book is explanatory rather than research oriented and that it empha sizes practice rather than mathematical rigor. In this respect, clear guidance and advice on practical issues are the main focus of the text. Nevertheless, this does not imply that more advanced topics have been avoided. Sections containing material of a deeper level have been sign posted by means of an asterisk.
Jmp For Basic Univariate And Multivariate Statistics
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Author : Ann Lehman
language : en
Publisher: SAS Institute
Release Date : 2013
Jmp For Basic Univariate And Multivariate Statistics written by Ann Lehman and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Computers categories.
Learn how to manage JMP data and perform the statistical analyses most commonly used in research in the social sciences and other fields with JMP for Basic Univariate and Multivariate Statistics: Methods for Researchers and Social Scientists, Second Edition. Updated for JMP 10 and including new features on the statistical platforms, this book offers clearly written instructions to guide you through the basic concepts of research and data analysis, enabling you to easily perform statistical analyses and solve problems in real-world research. Step by step, you'll discover how to obtain descriptive and inferential statistics, summarize results clearly in a way that is suitable for publication, perform a wide range of JMP analyses, interpret the results, and more. Topics include screening data for errors selecting subsets computing the coefficient alpha reliability index (Cronbach's alpha) for a multiple-item scale performing bivariate analyses for all types of variables performing a one-way analysis of variance (ANOVA), multiple regression, and a one-way multivariate analysis of variance (MANOVA) Advanced topics include analyzing models with interactions and repeated measures. There is also comprehensive coverage of principle components with emphasis on graphical interpretation. This user-friendly book introduces researchers and students of the social sciences to JMP and to elementary statistical procedures, while the more advanced statistical procedures that are presented make it an invaluable reference guide for experienced researchers as well.
Jmp For Mixed Models
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Author : Ruth Hummel
language : en
Publisher: SAS Institute
Release Date : 2021-06-09
Jmp For Mixed Models written by Ruth Hummel and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-09 with Computers categories.
Discover the power of mixed models with JMP and JMP Pro. Mixed models are now the mainstream method of choice for analyzing experimental data. Why? They are arguably the most straightforward and powerful way to handle correlated observations in designed experiments. Reaching well beyond standard linear models, mixed models enable you to make accurate and precise inferences about your experiments and to gain deeper understanding of sources of signal and noise in the system under study. Well-formed fixed and random effects generalize well and help you make the best data-driven decisions. JMP for Mixed Models brings together two of the strongest traditions in SAS software: mixed models and JMP. JMP’s groundbreaking philosophy of tight integration of statistics with dynamic graphics is an ideal milieu within which to learn and apply mixed models, also known as hierarchical linear or multilevel models. If you are a scientist or engineer, the methods described herein can revolutionize how you analyze experimental data without the need to write code. Inside you’ll find a rich collection of examples and a step-by-step approach to mixed model mastery. Topics include: Learning how to appropriately recognize, set up, and interpret fixed and random effects Extending analysis of variance (ANOVA) and linear regression to numerous mixed model designs Understanding how degrees of freedom work using Skeleton ANOVA Analyzing randomized block, split-plot, longitudinal, and repeated measures designs Introducing more advanced methods such as spatial covariance and generalized linear mixed models Simulating mixed models to assess power and other important sampling characteristics Providing a solid framework for understanding statistical modeling in general Improving perspective on modern dilemmas around Bayesian methods, p-values, and causal inference
Sas For Mixed Models
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Author : Walter W. Stroup
language : en
Publisher:
Release Date : 2018-12
Sas For Mixed Models written by Walter W. Stroup and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-12 with Computers categories.
Must-have study guide for the SAS® Certified Statistical Business Analyst Using SAS9: Regression and Modeling exam! Written for both new and experienced SAS programmers, the SAS Certification Prep Guide: Statistical Business Analysis Using SAS9 is an in-depth prep guide for the SAS Certified Statistical Business Analyst Using SAS9: Regression and Modeling exam. The authors step through identifying the business question, generating results with SAS, and interpreting the output in a business context. The case study approach uses both real and simulated data to master the content of the certification exam. Each chapter also includes a quiz aimed at testing the reader's comprehension of the material presented. Major topics include: ANOVA Linear Regression Logistic Regression Inputs for Predictive Modeling Model Performance For those new to statistical topics or those needing a review of statistical foundations, this book also serves as an excellent reference guide for understanding descriptive and inferential statistics.
Jmp Start Statistics
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Author : John Sall
language : en
Publisher: SAS Institute
Release Date : 2017-02-21
Jmp Start Statistics written by John Sall and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-02-21 with Computers categories.
This book provides hands-on tutorials with just the right amount of conceptual and motivational material to illustrate how to use the intuitive interface for data analysis in JMP. Each chapter features concept-specific tutorials, examples, brief reviews of concepts, step-by-step illustrations, and exercises. Updated for JMP 13, JMP Start Statistics, Sixth Edition includes many new features, including: The redesigned Formula Editor. New and improved ways to create formulas in JMP directly from the data table or dialogs. Interface updates, including improved menu layout. Updates and enhancements in many analysis platforms. New ways to get data into JMP and to save and share JMP results. Many new features that make it easier to use JMP.
Methodology For Multilevel Modeling In Educational Research
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Author : Myint Swe Khine
language : en
Publisher: Springer Nature
Release Date : 2022-04-10
Methodology For Multilevel Modeling In Educational Research written by Myint Swe Khine and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-10 with Education categories.
This edited volume documents attempts to conduct systematic and prodigious research using multilevel analysis in educational settings, and present their findings and identify future research directions. It showcases the versatility of multilevel analysis, and elucidates the unique advantages in examining complex and wide-ranging educational issues. This book brings together leading experts around the world to share their works in the field, highlighting recent advances, creative and unique approaches, and innovative methods using multilevel modeling and theoretical and practical aspects of multilevel analysis in culturally and linguistically-diverse educational contexts.
Statistics And Probability With Applications For Engineers And Scientists
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Author : Bhisham C Gupta
language : en
Publisher: John Wiley & Sons
Release Date : 2014-03-06
Statistics And Probability With Applications For Engineers And Scientists written by Bhisham C Gupta and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-03-06 with Mathematics categories.
Introducing the tools of statistics and probability from the ground up An understanding of statistical tools is essential for engineers and scientists who often need to deal with data analysis over the course of their work. Statistics and Probability with Applications for Engineers and Scientists walks readers through a wide range of popular statistical techniques, explaining step-by-step how to generate, analyze, and interpret data for diverse applications in engineering and the natural sciences. Unique among books of this kind, Statistics and Probability with Applications for Engineers and Scientists covers descriptive statistics first, then goes on to discuss the fundamentals of probability theory. Along with case studies, examples, and real-world data sets, the book incorporates clear instructions on how to use the statistical packages Minitab® and Microsoft® Office Excel® to analyze various data sets. The book also features: • Detailed discussions on sampling distributions, statistical estimation of population parameters, hypothesis testing, reliability theory, statistical quality control including Phase I and Phase II control charts, and process capability indices • A clear presentation of nonparametric methods and simple and multiple linear regression methods, as well as a brief discussion on logistic regression method • Comprehensive guidance on the design of experiments, including randomized block designs, one- and two-way layout designs, Latin square designs, random effects and mixed effects models, factorial and fractional factorial designs, and response surface methodology • A companion website containing data sets for Minitab and Microsoft Office Excel, as well as JMP ® routines and results Assuming no background in probability and statistics, Statistics and Probability with Applications for Engineers and Scientists features a unique, yet tried-and-true, approach that is ideal for all undergraduate students as well as statistical practitioners who analyze and illustrate real-world data in engineering and the natural sciences.
Optimal Design Of Experiments
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Author : Peter Goos
language : en
Publisher: John Wiley & Sons
Release Date : 2011-06-28
Optimal Design Of Experiments written by Peter Goos and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-06-28 with Science categories.
"This is an engaging and informative book on the modern practice of experimental design. The authors' writing style is entertaining, the consulting dialogs are extremely enjoyable, and the technical material is presented brilliantly but not overwhelmingly. The book is a joy to read. Everyone who practices or teaches DOE should read this book." - Douglas C. Montgomery, Regents Professor, Department of Industrial Engineering, Arizona State University "It's been said: 'Design for the experiment, don't experiment for the design.' This book ably demonstrates this notion by showing how tailor-made, optimal designs can be effectively employed to meet a client's actual needs. It should be required reading for anyone interested in using the design of experiments in industrial settings." —Christopher J. Nachtsheim, Frank A Donaldson Chair in Operations Management, Carlson School of Management, University of Minnesota This book demonstrates the utility of the computer-aided optimal design approach using real industrial examples. These examples address questions such as the following: How can I do screening inexpensively if I have dozens of factors to investigate? What can I do if I have day-to-day variability and I can only perform 3 runs a day? How can I do RSM cost effectively if I have categorical factors? How can I design and analyze experiments when there is a factor that can only be changed a few times over the study? How can I include both ingredients in a mixture and processing factors in the same study? How can I design an experiment if there are many factor combinations that are impossible to run? How can I make sure that a time trend due to warming up of equipment does not affect the conclusions from a study? How can I take into account batch information in when designing experiments involving multiple batches? How can I add runs to a botched experiment to resolve ambiguities? While answering these questions the book also shows how to evaluate and compare designs. This allows researchers to make sensible trade-offs between the cost of experimentation and the amount of information they obtain.