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"Neville may be the positive-thinking movement's most radical and subtly influential voice."

- Mitch Horowitz, bestselling author of One Simple Idea

DISCOVER HOW THOUGHT EFFECTS SUCCESS…

Taught by one of the great self-improvement teachers of the 20th century, The Wealth Mindset is an extraordinary guide about how to achieve success by transforming your mental attitude. Clear and provocative, this book will reveal to you a fascinating new way to wealth. Get your copy now.

• Sales Rank: #49801 in Books
• Published on: 2016-10-20
• Original language: English
• Dimensions: 8.00" h x .15" w x 5.00" l,
• Binding: Paperback
• 60 pages

About the Author

"Neville's teachings add a dimension that I hadn't really contemplated in depth until now."

- Wayne Dyer, bestselling author of Wishes Fulfilled

"Of all the metaphysical systems with which I am acquainted, Neville's is the most evidently magical."

- Israel Regardie, bestselling author of The Golden Dawn

"Neville may eventually be recognized as one of the world's great mystics."

- Joseph Murphy, bestselling author of The Power of Your Subconscious Mind

"Neville was able to clarify the nature of God and God's relationship to every person. He spoke of God in intimate terms as though he knew god very well, which he did."

- Roy Eugene Davis, founder of the Center for Spiritual Awareness

NEVILLE GODDARD (1905–1972) was one of the great self-improvement teachers of the 20th century. His strikingly practical interpretation of the Bible has helped thousands of people transform their lives, and remains some of the best spiritual advice ever recorded in American history. Neville was a master at explaining how the Bible could be understood metaphysically. This three-part guide reveals several essential and underrated aspects of Neville’s philosophy, taken from material he presented in 1953, at the peak of his speaking career.

Most helpful customer reviews

7 of 7 people found the following review helpful.
So, Let Me Show You A Gift....
By Patrick W. Edwards
I've been reading Nevile, and different people's explanations of him, for a while now. And Tim Grimes is by far the best at packaging, and delivering it in an easy to understand, and actionable way.

I received an advance paperback copy, and 20 pages into it, I bought a Kindle copy so that I could notate, highlight, and have it everywhere I have my Kindle or internet access. AND I'll buy the Audible version whenever that becomes available.

"The Wealth Mindset" is a gift from Tim to adepts and beginners alike.

I was amazed at how many nuances I missed in my previous readings of Neville.

Instead of peeling away layers, this book cuts right to the core of what you need to know to get to where you want to be. It's concise, precise, and incisive.

Take these easy steps: gift this book to yourself, read it, and apply what you learn. What you learn will be one of the best investments you ever make.

2 of 2 people found the following review helpful.
yet such powerful words continues to amaze me.
By Connie Fox
All I can say is incredible! A must read! Neville's awareness and ability to impart it in simple, yet such powerful words continues to amaze me.

1 of 1 people found the following review helpful.
Great perspective on changing ourselves
By Amazon Customer
Best explanation of focusing on our inner perspective to change our outer lives. Have read it twice already... Quick but profound read!

See all 10 customer reviews...

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[H428.Ebook] Free PDF Student Workbook for Kaplan/Saccuzzo's Psychological Testing: Principles, Applications, and Issues, 7th, by Robert M. Kaplan, Dennis P. Sa

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More than a traditional study guide, the Student Workbook--written by Katherine Nicolai of Rockhurst University--truly helps students understand the connections between abstract measurement concepts and the development, evaluation, selection, and use of psychological tests in the real world. The Student Workbook contains interesting hands-on exercises and assignments, including case studies to critique, test profiles to interpret, and studies on the psychometric properties of tests to evaluate. Of course, the Student Workbook also contains traditional features such as chapter outlines and practice multiple-choice quizzes. Best of all, the workbook is presented in a three-ring binder in which students can keep other course notes and handouts. Students will discover that the Student Workbook will help them organize their study of Kaplan and Saccuzzo's text and excel on course exams, assignments, and projects!

• Sales Rank: #2432837 in Books
• Published on: 2008-09-22
• Original language: English
• Number of items: 1
• Dimensions: 1.40" h x 11.10" w x 11.30" l, 2.95 pounds
• Binding: Ring-bound
• 368 pages

About the Author
Robert M. Kaplan is Fred W. and Pamela K. Wasserman Professor and Chair of the Department of Health Services at UCLA and Professor of Medicine at the UCLA David Geffen School of Medicine. From 1997 to 2004 he was Professor and Chair of the Department of Family and Preventive Medicine, at the University of California, San Diego. He is a past President of several organizations, including the American Psychological Association Division of Health Psychology, Section J of the American Association for the Advancement of Science (Pacific), the International Society for Quality of Life Research, the Society for Behavioral Medicine, and the Academy of Behavioral Medicine Research. He is a Past Chair of the Behavioral Science Council of the American Thoracic Society. Dr. Kaplan is currently Editor-in-Chief of Health Psychology and is the former Editor-in-Chief of the Annals of Behavioral Medicine. He has served as Associate Editor of the American Psychologist, and Consulting Editor of four other academic journals. Selected additional honors include APA Division of Health Psychology Annual Award for Outstanding Scientific Contribution (For junior scholar 1987 and again for a senior scholar 2001), SDSU Distinguished Research Lecturer, 1988, and Health Net Distinguished Lecturer in 1991, University of California 125 Anniversary Award for Most Distinguished Alumnus, University of California, Riverside, American Psychological Association Distinguished Lecturer, Distinguished Scientific contribution award from the American Association of Medical School Psychologists, National Leadership Award, from the Society of Behavioral Medicine in 2003, and President's Award for Career Achievement from the International Society for Quality of Life Research in 2004.

Dennis P. Saccuzzo is a professor of psychology at San Diego State University, and an adjunct professor of law at Thomas Jefferson School of Law. He has been a scholar and practitioner of psychological testing for over 32 years and has numerous peer-reviewed publications and professional presentations in the field. Dr. Saccuzzo's research has been supported by the National Science Foundation, the National Institutes of Mental Health, the National Institutes of Health, the U.S. Department of Education, the Scottish Rite Foundation, and the U.S. armed services. He is also a California licensed psychologist and a California licensed attorney. He is board certified in clinical psychology by the American Board of Professional Psychology (ABPP). In addition, he is a Diplomate of the American Board of Assessment Psychology (ABAP) and the American Board of Forensic Medicine He is a fellow of the American Psychological Association, American Psychological Society, and Western Psychological Association for outstanding and unusual contributions to the field of psychology. Dr. Saccuzzo is the author or co-author of over 300 peer-reviewed papers and publications, including eight textbooks and 20 law manuals. He is the president and co-founder of Applications of Psychology to Law, inc, an educational institution that applies psychological science to a legal issues and the study of law.

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1 of 1 people found the following review helpful.
Student Workbook Kaplan/Saccuzzo Psychological Testing Ed 7.
By Psych Major
I ordered it for a graduate level course. It is very easy to understand and the workbook really outlines the imporant issues.

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[Q716.Ebook] PDF Download Statistical Learning from a Regression Perspective (Springer Series in Statistics), by Richard A. Berk

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Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical.

Real applications are emphasized, especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one’s data and not apply statistical learning procedures that require more than the data can provide.

The material is written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R.

• Sales Rank: #1278535 in Books
• Published on: 2008-07-31
• Released on: 2008-07-31
• Original language: English
• Number of items: 1
• Dimensions: 9.25" h x .87" w x 6.10" l, 1.50 pounds
• Binding: Hardcover
• 360 pages

Review

From the reviews:

"I believe that the practical utility of statistical learning over more traditional non- and parametric regression approaches has yet to be truly demonstrate but the procedures presented in this text do show considerable potential…. The mathematical prerequisites for using this book are minimal…. Some familiarity with using a computer is necessary in order to gain the most benefit for the text, and some previous experience of using a statistical software package would be advantageous." (C.M. O’Brien, International Statistical Review, 2009, 77, 1)

"The readers of this book will obtain the knowledge of the dialectic of the regression modeling problems arising in the study of predictor –response. A large percent of the contents is devoted to discuss how to understand phenomena through regression equation fitting. … I recommend it for practitioners and professors have the responsibility of teaching on the subject, the book gives an interesting perspective for dealing with regression." (Sovandep.H. Kumar, Revista Investigación Operacional, Vol. 30 (2), 2009)

"On the positive side, SLRP is a nice addition to the data mining literature, more accessible than ESL. It gives good references and provides statistical detail. In general, I enjoyed the philosophical discussions about how statistical learning fits in with statistical inference. I may not hand it over to my colleagues in Biology and Sociology, but I will seriously consider recommending it to the undergraduates in my data mining seminar." (Richard D. DE VEAUX, The American Statistician, Novemeber 2009, Volume 63, Number 4, pp. 297-411)

“…The strength of this book is its extensive discussion of practical issues. Algorithmic details are a starting point for discussing why and how methods work, comparison with other methodologies, limitations and strengths, and so on. Throughout the book, examples are worked through in detail. Each chapter except the first and the last end with a section headed ‘Software Considerations’, followed by ‘Summary and Conclusions’ and data analysis exercises. …Regression methods, both the theory and the practice, remain a work in progress… .Berk has made a good start in pulling together commentary on issues of major importance.” (Journal of Statistical Software, Vol. 29, Book Review 12, February 2009)

“This book is unique in that statistical learning is discussed by a sociology–PhD scientist, Professor Richard Berk, who has extensive research  accomplishments in the intersection of social science and statistics. …The key strength of this book is in its emphasis on practical applications and hands-on learning of the statistical learning methods. Each chapter has real data examples … and  goes through their analyses using statistical software R (2009). This design effectively illustrates the use of the methods in practice. ‘Software consideration’  given at the end of each chapter provides discussions on currently available computational tools, both functions/packages of  R and other software, and is useful in practice. Emphasis on using R that is freely available worldwide is a major advantage in terms of readers’ accessibility to the methods. Furthermore, each chapter contains exercises for practicing different aspects of the methods in the chapter. The solutions and R codes of these exercises are provided at the author’s website…: this is another useful feature enhancing the hands-on learning. …A notable difference…is that this book is written with little mathematics. …Consequently, emphasis is not to understand the statistical-learning methods mathematically. Rather, the methods are explained mostly algorithmically in English, providing readers story-like descriptions of them. This would appeal to readers who are users of the statistical-learning methods but are not mathematically oriented. …” (Biometrics 65, 1309–1310, December 2009)

“The author covers a remarkable terrain in a relatively short book. Up-to-date methods are presented, and their main features are explained with a minimum of mathematical notation. … The problems at the end of each chapter are a real jewel: they lead the reader to a clear understanding of the issues treated in the chapter … . The book will no doubt be useful for the intended readership. Even the mathematically trained reader … may find useful ideas in it.” (Ricardo Maronna, Statistical Papers, Vol. 52, 2011)

From the Back Cover

Statistical Learning from a Regression Perspective considers statistical learning applications when interest centers on the conditional distribution of the response variable, given a set of predictors, and when it is important to characterize how the predictors are related to the response. As a first approximation, this is can be seen as an extension of nonparametric regression. Among the statistical learning procedures examined are bagging, random forests, boosting, and support vector machines. Response variables may be quantitative or categorical.

Real applications are emphasized, especially those with practical implications. One important theme is the need to explicitly take into account asymmetric costs in the fitting process. For example, in some situations false positives may be far less costly than false negatives. Another important theme is to not automatically cede modeling decisions to a fitting algorithm. In many settings, subject-matter knowledge should trump formal fitting criteria. Yet another important theme is to appreciate the limitation of one’s data and not apply statistical learning procedures that require more than the data can provide.

The material is written for graduate students in the social and life sciences and for researchers who want to apply statistical learning procedures to scientific and policy problems. Intuitive explanations and visual representations are prominent. All of the analyses included are done in R.

Richard Berk is Distinguished Professor of Statistics Emeritus from the Department of Statistics at UCLA and currently a Professor at the University of Pennsylvania in the Department of Statistics and in the Department of Criminology. He is an elected fellow of the American Statistical Association and the American Association for the Advancement of Science and has served in a professional capacity with a number of organizations such as the Committee on Applied and Theoretical Statistics for the National Research Council and the Board of Directors of the Social Science Research Council. His research has ranged across a variety of applications in the social and natural sciences.

About the Author
Richard Berk is Distinguished Professor of Statistics Emeritus from the Department of Statistics at UCLA and currently a Professor at the University of Pennsylvania in the Department of Statistics and in the Department of Criminology. He is an elected fellow of the American Statistical Association and the American Association for the Advancement of Science and has served in a professional capacity with a number of organizations such as the Committee on Applied and Theoretical Statistics for the National Research Council and the Board of Directors of the Social Science Research Council. His research has ranged across a variety of applications in the social and natural sciences.

Most helpful customer reviews

6 of 6 people found the following review helpful.
A smooth transition from "Statistics" to "Machine Learning"
By Prabhanjan Tattar
I must admit that I am really surprised that this beautiful book does not yet have a amazon customer review! This book was purchased by me a year back and completely benefited from it.

The translation from the regression models to machine learning is really smooth. The first chapter "Statistical Learning as a Regression Problem" quickly drives home the point the interrelationship and using the basic regression model, the expectation is really setup to expect the contents in rest of the book. The second chapter is where the connection is pedagogically setup and the presentation is really neat and concise. In fact, if the reader tries to imitate the models through software and data analysis, the returns are really rewarding.

Breiman, et. al. (1984) is the starting point of CART and rightly followed up in Hastie, et. al. (2001-9) ESL classic. In my experience, and strictly mine only, if the reader is slightly not familiar with a good regression course, there is some chance that he may be lost by the time he reaches the decision tree chapter. Prof. Berk's Chapter 3 "Classification and Regression Trees (CART)" is what exactly is required for a thorough understanding of the CART approach. The dividends are so apparent that I have no shame in admitting that the Chapters 8-10 of my book "R Statistical Application Development by Example" have a strong influence of Prof. Berk's book. It is my firm belief that the three chapters from my book, and Berk's book is what is required for the uninitiated reader for a even smoother reading of ESL.

Chapters 4-6 then cover the CART extensions in a very useful way. These chapters are written as a very natural and gradual development of the CART.

It is also remarkable that the concluding section "Summary and Conclusions" of each chapter provides a very nice and logical discussion of the chapters and a simple reading of these pages is in itself a good experience of the recent developments.

Overall, I believe that Prof. Berks book is really useful and must be there on most data miners shelf. It is also very useful for Statisticians who want to pick up on the machine learning tools. The only flip side is that I was really unable to download the data sets used in this book. A glaring mistake in the preface is this sentence "In addition, there is a web site where many examples, including the data, can be found (WEB ADDRESS TO BE ADDED)." I genuinely hope to see a correction to this in the next reprint or edition. However, despite the lack of data sets and the web address, the complete coverage itself merits all the 5 stars and does not warrant any reduction.

0 of 0 people found the following review helpful.
Good bridge between statistics and machine learning
By W. YIP
This book covers a number of machine learning techniques: CART, Bagging, Random Forest, Boosting and SVM, Smoothers (e.g. splines, GAM) are included as they fit into the regression paradigm. A number of machine learning books mostly present the algorithm, this book discusses the underlying statistics to some detail. Although the theoretical statistical work behind some of these techniques have not been worked out completely, the author gives a good intuitive discussion about some of the issues. Also, the author includes a number of real life examples on how these techniques are actually used in practice. This book is really written for statisticians who want to apply some of the machine learning techniques in their work. You will need a fair amount of statistics knowledge to read this book.

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[H910.Ebook] Download Ebook The Art of Fluid Animation, by Jos Stam

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Fluid simulation is a computer graphic used to develop realistic animation of liquids in modern games. The Art of Fluid Animation describes visually rich techniques for creating fluid-like animations that do not require advanced physics or mathematical skills. It explains how to create fluid animations like water, smoke, fire, and explosions through computer code in a fun manner.

The book presents concepts that drive fluid animation and gives a historical background of the computation of fluids. It covers many research areas that include stable fluid simulation, flows on surfaces, and control of flows. It also gives one-paragraph summaries of the material after each section for reinforcement.

This book includes computer code that readers can download and run on several platforms so they can extend their work beyond what is described in the book. The material provided here is designed to serve as a starting point for aspiring programmers to begin creating their own programs using fluid animation.

• Sales Rank: #889039 in eBooks
• Published on: 2015-11-04
• Released on: 2015-11-04
• Format: Kindle eBook

About the Author

Jos Stam is a senior research scientist with Autodesk. He was born in the Netherlands and educated in Geneva, Switzerland, where he received dual bachelor degrees in computer science and pure mathematics. In 1989 he moved to Toronto, where he completed his master’s and PhD in computer science. His research spans several areas of computer graphics: natural phenomena, physics-based simulation, rendering, and surface modeling, especially subdivision surfaces. He has published papers in all of these areas in journals and at conferences. In 2005 he received the SIGGRAPH Computer Graphics Achievement Award, one of the most prestigious awards in computer graphics. He also won two Technical Achievement Awards from the Academy of Motion Picture Arts and Science and has been featured in Wired magazine.

Most helpful customer reviews

3 of 4 people found the following review helpful.
Great book, wrong publisher.
By Bernard Van der Stichele
This is a great book. Computational fluid dynamics is not easy and there are very few, if any, math or physics books that cover the material with real implementation in mind - the few I've come across are terribly boring. This is a different kind of book and is a nice supplement to the author's well-known paper on implementing fluid solvers.

The missing star is for the choice of publisher and quality of the illustrations..... CRC Press is hardly coding hacker mainstream and is very expensive. Some of the illustrations/images are of very poor quality, as are some of the code listings.

Still, a very good guide to getting started with fluid animation.

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