IBM SPSS Statistics Base Be Confident in Your Analytical Results and in the Business Decisions You Make
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Wu Washington 0250E 15755.Pdf (3.086Mb)
© Copyright 2016 Sang Wu Contributions to Physics-Based Aeroservoelastic Uncertainty Analysis Sang Wu A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy University of Washington 2016 Reading Committee: Eli Livne, Chair Mehran Mesbahi Dorothy Reed Frode Engelsen Program Authorized to Offer Degree: Aeronautics and Astronautics University of Washington Abstract Contributions to Physics-Based Aeroservoelastic Uncertainty Analysis Sang Wu Chair of the Supervisory Committee: Professor Eli Livne The William E. Boeing Department of Aeronautics and Astronautics The thesis presents the development of a new fully-integrated, MATLAB based simulation capability for aeroservoelastic (ASE) uncertainty analysis that accounts for uncertainties in all disciplines as well as discipline interactions. This new capability allows probabilistic studies of complex configuration at a scope and with depth not known before. Several statistical tools and methods have been integrated into the capability to guide the tasks such as parameter prioritization, uncertainty reduction, and risk mitigation. The first task of the thesis focuses on aeroservoelastic uncertainty assessment considering control component uncertainty. The simulation has shown that attention has to be paid, if notch filters are used in the aeroservoelastic loop, to the variability and uncertainties of the aircraft involved. The second task introduces two innovative methodologies to characterize the unsteady aerodynamic uncertainty. One is a physically based aerodynamic influence coefficients element by element correction uncertainty scheme and the other is an alternative approach focusing on rational function approximation matrix uncertainties to evaluate the relative impact of uncertainty in aerodynamic stiffness, damping, inertia, or lag terms. Finally, the capability has been applied to obtain the gust load response statistics accounting for uncertainties in both aircraft and gust profiles. -
Fortran Reference Guide
FORTRAN REFERENCE GUIDE Version 2018 TABLE OF CONTENTS Preface............................................................................................................ xv Audience Description......................................................................................... xv Compatibility and Conformance to Standards............................................................ xv Organization................................................................................................... xvi Hardware and Software Constraints...................................................................... xvii Conventions................................................................................................... xvii Related Publications........................................................................................ xviii Chapter 1. Language Overview............................................................................... 1 1.1. Elements of a Fortran Program Unit.................................................................. 1 1.1.1. Fortran Statements................................................................................. 1 1.1.2. Free and Fixed Source............................................................................. 2 1.1.3. Statement Ordering................................................................................. 2 1.2. The Fortran Character Set.............................................................................. 3 1.3. Free Form Formatting.................................................................................. -
Introduction to Linux on System Z
IBM Linux and Technology Center Introduction to Linux on System z Mario Held IBM Lab Boeblingen, Germany © 2009 IBM Corporation IBM Linux and Technology Center Trademarks The following are trademarks of the International Business Machines Corporation in the United States, other countries, or both. Not all common law marks used by IBM are listed on this page. Failure of a mark to appear does not mean that IBM does not use the mark nor does it mean that the product is not actively marketed or is not significant within its relevant market. Those trademarks followed by ® are registered trademarks of IBM in the United States; all others are trademarks or common law marks of IBM in the United States. For a complete list of IBM Trademarks, see www.ibm.com/legal/copytrade.shtml: *, AS/400®, e business(logo)®, DBE, ESCO, eServer, FICON, IBM®, IBM (logo)®, iSeries®, MVS, OS/390®, pSeries®, RS/6000®, S/30, VM/ESA®, VSE/ESA, WebSphere®, xSeries®, z/OS®, zSeries®, z/VM®, System i, System i5, System p, System p5, System x, System z, System z9®, BladeCenter® The following are trademarks or registered trademarks of other companies. Adobe, the Adobe logo, PostScript, and the PostScript logo are either registered trademarks or trademarks of Adobe Systems Incorporated in the United States, and/or other countries. Cell Broadband Engine is a trademark of Sony Computer Entertainment, Inc. in the United States, other countries, or both and is used under license therefrom. Java and all Java-based trademarks are trademarks of Sun Microsystems, Inc. in the United States, other countries, or both. -
AIX Migration to Cloud with IBM Power Virtual Server
AIX Migration to Cloud with IBM Power Virtual Server An IBM Systems Lab Services Tutorial Aaron Bolding Berjis Patel Vess Natchev [email protected] TABLE OF CONTENTS CHAPTER 1: SOLUTION OVERVIEW............................. 1 Introduction ................................................................................ 1 Use Cases .................................................................................. 1 Migration via PowerVC OVA ..................................................... 1 Transfer System Backup Using the Public Internet ..................... 2 Transfer System Backup Using Cloud Object Storage ................. 2 Solution Components and Requirements ........................................ 2 Components .......................................................................... 2 Migration via PowerVC OVA ..................................................... 2 Transfer System Backup Using the Public Internet ..................... 2 Transfer System Backup Using Cloud Object Storage ................. 2 Requirements ........................................................................ 3 Solution Diagrams ....................................................................... 3 Transfer System Backup Using the Public Internet ..................... 3 Transfer System Backup Using Cloud Object Storage ................. 4 CHAPTER 2: IMPLEMENTATION .................................. 5 Migration via PowerVC OVA .......................................................... 5 Procedure to Configure IBM Cloud Object Storage ..................... -
Analysing Census at School Data
Analysing Census At School Data www.censusatschool.ie Project Maths Development Team 2015 Page 1 of 7 Excel for Analysing Census At School Data Having retrieved a class’s data using CensusAtSchool: 1. Save the downloaded class data which is a .csv file as an .xls file. 2. Delete any unwanted variables such as the teacher name, date stamp etc. 3. Freeze the top row so that one can scroll down to view all of the data while the top row remains in view. 4. Select certain columns (not necessarily contiguous) for use as a class worksheet. Aim to have a selection of categorical data (nominal and ordered) and numerical data (discrete and continuous) unless one requires one type exclusively. The number of columns will depend on the class as too much data might be intimidating for less experienced classes. Students will need to have the questionnaire to hand to identify the question which produced the data. 5. Make a new worksheet within the workbook of downloaded data using the selected columns. 6. Name the worksheet i.e. “data for year 2 2014” or “data for margin of error investigation”. 7. Format the data to show all borders (useful if one is going to make a selection of the data and put it into Word). 8. Order selected columns of this data perhaps (and use expand selection) so that rogue data such as 0 for height etc. can be eliminated. It is perhaps useful for students to see this. (Use Sort & Filter.) 9. Use a filter on data, for example based on gender, to generate further worksheets for future use 10. -
A Monte Carlo Simulation Approach to the Reliability Modeling of the Beam Permit System of Relativistic Heavy Ion Collider (Rhic) at Bnl* P
Proceedings of ICALEPCS2013, San Francisco, CA, USA MOPPC075 A MONTE CARLO SIMULATION APPROACH TO THE RELIABILITY MODELING OF THE BEAM PERMIT SYSTEM OF RELATIVISTIC HEAVY ION COLLIDER (RHIC) AT BNL* P. Chitnis#, T.G. Robertazzi, Stony Brook University, Stony Brook, NY 11790, U.S.A. K.A. Brown, Brookhaven National Laboratory, Upton, NY 11973, U.S.A. Abstract called the Permit Carrier Link, Blue Carrier Link and The RHIC Beam Permit System (BPS) monitors the Yellow Carrier link. These links carry 10 MHz signals health of RHIC subsystems and takes active decisions whose presence allows the beam in the ring. Support regarding beam-abort and magnet power dump, upon a systems report their status to BPS through “Input subsystem fault. The reliability of BPS directly impacts triggers” called Permit Inputs (PI) and Quench Inputs the RHIC downtime, and hence its availability. This work (QI). If any support system PI fails, the permit carrier assesses the probability of BPS failures that could lead to terminates, initiating a beam dump. If QI fails, then the substantial downtime. A fail-safe condition imparts blue and yellow carriers also terminate, initiating magnet downtime to restart the machine, while a failure to power dump in blue and yellow ring magnets. The carrier respond to an actual fault can cause potential machine failure propagates around the ring to inform other PMs damage and impose significant downtime. This paper about the occurrence of a fault. illustrates a modular multistate reliability model of the Other than PMs, BPS also has 4 Abort Kicker Modules BPS, with modules having exponential lifetime (AKM) that see the permit carrier failure and send the distributions. -
2 3 Practice Problems.Pdf
Exam Name___________________________________ MULTIPLE CHOICE. Choose the one alternative that best completes the statement or answers the question. 1) The following frequency distribution presents the frequency of passenger vehicles that 1) pass through a certain intersection from 8:00 AM to 9:00 AM on a particular day. Vehicle Type Frequency Motorcycle 5 Sedan 95 SUV 65 Truck 30 Construct a frequency bar graph for the data. A) B) 1 C) D) 2) The following bar graph presents the average amount a certain family spent, in dollars, on 2) various food categories in a recent year. On which food category was the most money spent? A) Dairy products B) Cereals and baked goods C) Fruits and vegetables D) Meat poultry, fish, eggs 3) The following frequency distribution presents the frequency of passenger vehicles that 3) pass through a certain intersection from 8:00 AM to 9:00 AM on a particular day. Vehicle Type Frequency Motorcycle 9 Sedan 54 SUV 27 2 Truck 53 Construct a relative frequency bar graph for the data. A) B) C) D) 3 4) The following frequency distribution presents the frequency of passenger vehicles that 4) pass through a certain intersection from 8:00 AM to 9:00 AM on a particular day. Vehicle Type Frequency Motorcycle 9 Sedan 20 SUV 25 Truck 39 Construct a pie chart for the data. A) B) C) D) 5) The following pie chart presents the percentages of fish caught in each of four ratings 5) categories. Match this pie chart with its corresponding Parato chart. 4 A) B) C) 5 D) SHORT ANSWER. -
RACF Command Tips
RACF Command Tips SHARE ‐ March 2015 Session 18875 RSH Consulting ‐ Robert S. Hansel RSH Consulting, Inc. is an IT security professional services firm established in 1992 and dedicated to helping clients strengthen their IBM z/OS mainframe access controls by fully exploiting all the capabilities and latest innovations in RACF. RSH's services include RACF security reviews and audits, initial implementation of new controls, enhancement and remediation of existing controls, and training. • www.rshconsulting.com • 617‐969‐9050 Robert S. Hansel is Lead RACF Specialist and founder of RSH Consulting, Inc. He began working with RACF in 1986 and has been a RACF administrator, manager, auditor, instructor, developer, and consultant. Mr. Hansel is especially skilled at redesigning and refining large‐scale implementations of RACF using role‐based access control concepts. He is a leading expert in securing z/OS Unix using RACF. Mr. Hansel has created elaborate automated tools to assist clients with RACF administration, database merging, identity management, and quality assurance. • 617‐969‐8211 • [email protected] • www.linkedin.com/in/roberthansel • http://twitter.com/RSH_RACF RACF Command Tips SHARE 2 © 2016 RSH Consulting, Inc. All Rights Reserved. March 2016 Topics . User Commands . Group Commands . Dataset Command . General Resource Commands . PERMIT Command . Generic Profile Refresh . List Commands . SEARCH Command . Console Command Entry . Building Commands with Microsoft Excel RACF and z/OS are Trademarks of the International Business Machines Corporation RACF Command Tips SHARE 3 © 2016 RSH Consulting, Inc. All Rights Reserved. March 2016 User Commands . ADDUSER Defaults: • OWNER ‐ Creator's ID • DFLTGRP ‐ Creator's Current Connect Group • PASSWORD ‐ Pre‐z/OS 2.2: Default Group z/OS 2.2: NOPASSWORD • Always specify when creating new ID . -
Implementing Nfsv4 in the Enterprise: Planning and Migration Strategies
Front cover Implementing NFSv4 in the Enterprise: Planning and Migration Strategies Planning and implementation examples for AFS and DFS migrations NFSv3 to NFSv4 migration examples NFSv4 updates in AIX 5L Version 5.3 with 5300-03 Recommended Maintenance Package Gene Curylo Richard Joltes Trishali Nayar Bob Oesterlin Aniket Patel ibm.com/redbooks International Technical Support Organization Implementing NFSv4 in the Enterprise: Planning and Migration Strategies December 2005 SG24-6657-00 Note: Before using this information and the product it supports, read the information in “Notices” on page xi. First Edition (December 2005) This edition applies to Version 5, Release 3, of IBM AIX 5L (product number 5765-G03). © Copyright International Business Machines Corporation 2005. All rights reserved. Note to U.S. Government Users Restricted Rights -- Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Contents Notices . xi Trademarks . xii Preface . xiii The team that wrote this redbook. xiv Acknowledgments . xv Become a published author . xvi Comments welcome. xvii Part 1. Introduction . 1 Chapter 1. Introduction. 3 1.1 Overview of enterprise file systems. 4 1.2 The migration landscape today . 5 1.3 Strategic and business context . 6 1.4 Why NFSv4? . 7 1.5 The rest of this book . 8 Chapter 2. Shared file system concepts and history. 11 2.1 Characteristics of enterprise file systems . 12 2.1.1 Replication . 12 2.1.2 Migration . 12 2.1.3 Federated namespace . 13 2.1.4 Caching . 13 2.2 Enterprise file system technologies. 13 2.2.1 Sun Network File System (NFS) . 13 2.2.2 Andrew File System (AFS) . -
Type Here Title of the Paper
ICME 11, TSG 13, Monterrey, Mexico, 2008: Dave Pratt Shaping the Experience of Young and Naïve Probabilists Pratt, Dave Institute of Education, University of London. [email protected] Summary This paper starts by briefly summarizing research which has focused on student misconceptions with respect to understanding probability, before outlining the author’s research, which, in contrast, presents what students of age 11-12 years(Grade 7) do know and can construct, given access to a carefully designed environment. These students judged randomness according to unpredictability, lack of pattern in results, lack of control over outcomes and fairness. In many respects their judgments were based on similar characteristics to those of experts. However, it was only through interaction with a virtual environment, ChanceMaker, that the students began to express situated meanings for aggregated long-term randomness. That data is then re-analyzed in order to reflect upon the design decisions that shaped the environment itself. Four main design heuristics are identified and elaborated: Testing personal conjectures, Building on student knowledge, Linking purpose and utility, Fusing control and representation. In conclusion, it is conjectured that these heuristics are of wider relevance to teachers and lecturers, who aspire to shape the experience of young and naïve probabilists through their actions as designers of tasks and pedagogical settings. Preamble The aim in this paper is to propose heuristics that might usefully guide design decisions for pedagogues, who might be building software, creating new curriculum approaches, imagining a novel task or writing a lesson plan. (Here I use the term “heuristics” with its standard meaning as a rule-of-thumb, a guiding rule, rather than in the specialized sense that has been adopted by researchers of judgments of chance.) With this aim in mind, I re-analyze data that have previously informed my research on students’ meanings for randomness as emergent during activity. -
Revisiting the Sustainable Happiness Model and Pie Chart: Can Happiness Be Successfully Pursued?
The Journal of Positive Psychology Dedicated to furthering research and promoting good practice ISSN: 1743-9760 (Print) 1743-9779 (Online) Journal homepage: https://www.tandfonline.com/loi/rpos20 Revisiting the Sustainable Happiness Model and Pie Chart: Can Happiness Be Successfully Pursued? Kennon M. Sheldon & Sonja Lyubomirsky To cite this article: Kennon M. Sheldon & Sonja Lyubomirsky (2019): Revisiting the Sustainable Happiness Model and Pie Chart: Can Happiness Be Successfully Pursued?, The Journal of Positive Psychology, DOI: 10.1080/17439760.2019.1689421 To link to this article: https://doi.org/10.1080/17439760.2019.1689421 Published online: 07 Nov 2019. Submit your article to this journal View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=rpos20 THE JOURNAL OF POSITIVE PSYCHOLOGY https://doi.org/10.1080/17439760.2019.1689421 Revisiting the Sustainable Happiness Model and Pie Chart: Can Happiness Be Successfully Pursued? Kennon M. Sheldona,b and Sonja Lyubomirskyc aUniversity of Missouri, Columbia, MO, USA; bNational Research University Higher School of Economics, Moscow, Russian Federation; cUniversity of California, Riverside, CA, USA ABSTRACT ARTICLE HISTORY The Sustainable Happiness Model (SHM) has been influential in positive psychology and well-being Received 4 September 2019 science. However, the ‘pie chart’ aspect of the model has received valid critiques. In this article, we Accepted 8 October 2019 start by agreeing with many such critiques, while also explaining the context of the original article KEYWORDS and noting that we were speculative but not dogmatic therein. We also show that subsequent Subjective well-being; research has supported the most important premise of the SHM – namely, that individuals can sustainable happiness boost their well-being via their intentional behaviors, and maintain that boost in the longer-term. -
IBM SPSS Decision Trees Business Analytics
IBM Software IBM SPSS Decision Trees Business Analytics IBM SPSS Decision Trees Easily identify groups and predict outcomes IBM® SPSS® Decision Trees creates classification and decision trees to Highlights help you better identify groups, discover relationships between groups and predict future events. • Identify groups, segments, and patterns in a highly visual manner with classification trees. You can use classification and decision trees for: • Choose from CHAID, Exhaustive • Segmentation CHAID, C&RT and QUEST to find the • Stratification best fit for your data. • Prediction • Present results in an intuitive manner— • Data reduction and variable screening perfect for non-technical audiences. • Interaction identification • Save information from trees as new • Category merging variables in data (information such as • Discretizing continuous variables terminal node number, predicted value and predicted probabilities). Highly visual diagrams enable you to present categorical results in an intuitive manner—so you can more clearly explain the results to non-technical audiences. These trees enable you to explore your results and visually determine how your model flows. Visual results can help you find specific subgroups and relationships that you might not uncover using more traditional statistics. Because classification trees break the data down into branches and nodes, you can easily see where a group splits and terminates. IBM Software IBM SPSS Decision Trees Business Analytics Use SPSS Decision Trees in a variety of applications, • Marketing