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Administering Unidata on UNIX Platforms
C:\Program Files\Adobe\FrameMaker8\UniData 7.2\7.2rebranded\ADMINUNIX\ADMINUNIXTITLE.fm March 5, 2010 1:34 pm Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta UniData Administering UniData on UNIX Platforms UDT-720-ADMU-1 C:\Program Files\Adobe\FrameMaker8\UniData 7.2\7.2rebranded\ADMINUNIX\ADMINUNIXTITLE.fm March 5, 2010 1:34 pm Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Beta Notices Edition Publication date: July, 2008 Book number: UDT-720-ADMU-1 Product version: UniData 7.2 Copyright © Rocket Software, Inc. 1988-2010. All Rights Reserved. Trademarks The following trademarks appear in this publication: Trademark Trademark Owner Rocket Software™ Rocket Software, Inc. Dynamic Connect® Rocket Software, Inc. RedBack® Rocket Software, Inc. SystemBuilder™ Rocket Software, Inc. UniData® Rocket Software, Inc. UniVerse™ Rocket Software, Inc. U2™ Rocket Software, Inc. U2.NET™ Rocket Software, Inc. U2 Web Development Environment™ Rocket Software, Inc. wIntegrate® Rocket Software, Inc. Microsoft® .NET Microsoft Corporation Microsoft® Office Excel®, Outlook®, Word Microsoft Corporation Windows® Microsoft Corporation Windows® 7 Microsoft Corporation Windows Vista® Microsoft Corporation Java™ and all Java-based trademarks and logos Sun Microsystems, Inc. UNIX® X/Open Company Limited ii SB/XA Getting Started The above trademarks are property of the specified companies in the United States, other countries, or both. All other products or services mentioned in this document may be covered by the trademarks, service marks, or product names as designated by the companies who own or market them. License agreement This software and the associated documentation are proprietary and confidential to Rocket Software, Inc., are furnished under license, and may be used and copied only in accordance with the terms of such license and with the inclusion of the copyright notice. -
Python for Economists Alex Bell
Python for Economists Alex Bell [email protected] http://xkcd.com/353 This version: October 2016. If you have not already done so, download the files for the exercises here. Contents 1 Introduction to Python 3 1.1 Getting Set-Up................................................. 3 1.2 Syntax and Basic Data Structures...................................... 3 1.2.1 Variables: What Stata Calls Macros ................................ 4 1.2.2 Lists.................................................. 5 1.2.3 Functions ............................................... 6 1.2.4 Statements............................................... 7 1.2.5 Truth Value Testing ......................................... 8 1.3 Advanced Data Structures .......................................... 10 1.3.1 Tuples................................................. 10 1.3.2 Sets .................................................. 11 1.3.3 Dictionaries (also known as hash maps) .............................. 11 1.3.4 Casting and a Recap of Data Types................................. 12 1.4 String Operators and Regular Expressions ................................. 13 1.4.1 Regular Expression Syntax...................................... 14 1.4.2 Regular Expression Methods..................................... 16 1.4.3 Grouping RE's ............................................ 18 1.4.4 Assertions: Non-Capturing Groups................................. 19 1.4.5 Portability of REs (REs in Stata).................................. 20 1.5 Working with the Operating System.................................... -
LATEX for Beginners
LATEX for Beginners Workbook Edition 5, March 2014 Document Reference: 3722-2014 Preface This is an absolute beginners guide to writing documents in LATEX using TeXworks. It assumes no prior knowledge of LATEX, or any other computing language. This workbook is designed to be used at the `LATEX for Beginners' student iSkills seminar, and also for self-paced study. Its aim is to introduce an absolute beginner to LATEX and teach the basic commands, so that they can create a simple document and find out whether LATEX will be useful to them. If you require this document in an alternative format, such as large print, please email [email protected]. Copyright c IS 2014 Permission is granted to any individual or institution to use, copy or redis- tribute this document whole or in part, so long as it is not sold for profit and provided that the above copyright notice and this permission notice appear in all copies. Where any part of this document is included in another document, due ac- knowledgement is required. i ii Contents 1 Introduction 1 1.1 What is LATEX?..........................1 1.2 Before You Start . .2 2 Document Structure 3 2.1 Essentials . .3 2.2 Troubleshooting . .5 2.3 Creating a Title . .5 2.4 Sections . .6 2.5 Labelling . .7 2.6 Table of Contents . .8 3 Typesetting Text 11 3.1 Font Effects . 11 3.2 Coloured Text . 11 3.3 Font Sizes . 12 3.4 Lists . 13 3.5 Comments & Spacing . 14 3.6 Special Characters . 15 4 Tables 17 4.1 Practical . -
An Evaluation of Statistical Software for Research and Instruction
Behavior Research Methods, Instruments, & Computers 1985, 17(2),352-358 An evaluation of statistical software for research and instruction DARRELL L. BUTLER Ball State University, Muncie, Indiana and DOUGLAS B. EAMON University of Houston-Clear Lake, Houston, Texas A variety of microcomputer statistics packages were evaluated. The packages were compared on a number of dimensions, including error handling, documentation, statistical capability, and accuracy. Results indicated that there are some very good packages available both for instruc tion and for analyzing research data. In general, the microcomputer packages were easier to learn and to use than were mainframe packages. Furthermore, output of mainframe packages was found to be less accurate than output of some of the microcomputer packages. Many psychologists use statistical programs to analyze ware packages available on the VAX computers at Ball research data or to teach statistics, and many are interested State University: BMDP, SPSSx, SCSS, and MINITAB. in using computer software for these purposes (e.g., Versions of these programs are widely available and are Butler, 1984; Butler & Kring, 1984). The present paper used here as points ofreference to judge the strengths and provides some program descriptions, benchmarks, and weaknesses of the microcomputer packages. Although evaluations of a wide variety of marketed statistical pro there are many programs distributed by individuals (e.g., grams that may be useful in research and/or instruction. see Academic Computer Center of Gettysburg College, This review differs in several respects from other re 1984, and Eamon, 1983), none are included in the present cent reviews of statistical programs (e. g., Carpenter, review. -
Factor — Factor Analysis
Title stata.com factor — Factor analysis Description Quick start Menu Syntax Options for factor and factormat Options unique to factormat Remarks and examples Stored results Methods and formulas References Also see Description factor and factormat perform a factor analysis of a correlation matrix. The commands produce principal factor, iterated principal factor, principal-component factor, and maximum-likelihood factor analyses. factor and factormat display the eigenvalues of the correlation matrix, the factor loadings, and the uniqueness of the variables. factor expects data in the form of variables, allows weights, and can be run for subgroups. factormat is for use with a correlation or covariance matrix. Quick start Principal-factor analysis using variables v1 to v5 factor v1 v2 v3 v4 v5 As above, but retain at most 3 factors factor v1-v5, factors(3) Principal-component factor analysis using variables v1 to v5 factor v1-v5, pcf Maximum-likelihood factor analysis factor v1-v5, ml As above, but perform 50 maximizations with different starting values factor v1-v5, ml protect(50) As above, but set the seed for reproducibility factor v1-v5, ml protect(50) seed(349285) Principal-factor analysis based on a correlation matrix cmat with a sample size of 800 factormat cmat, n(800) As above, retain only factors with eigenvalues greater than or equal to 1 factormat cmat, n(800) mineigen(1) Menu factor Statistics > Multivariate analysis > Factor and principal component analysis > Factor analysis factormat Statistics > Multivariate analysis > Factor and principal component analysis > Factor analysis of a correlation matrix 1 2 factor — Factor analysis Syntax Factor analysis of data factor varlist if in weight , method options Factor analysis of a correlation matrix factormat matname, n(#) method options factormat options matname is a square Stata matrix or a vector containing the rowwise upper or lower triangle of the correlation or covariance matrix. -
Statistics and GIS Assistance Help with Statistics
Statistics and GIS assistance An arrangement for help and advice with regard to statistics and GIS is now in operation, principally for Master’s students. How do you seek advice? 1. The users, i.e. students at INA, make direct contact with the person whom they think can help and arrange a time for consultation. Remember to be well prepared! 2. Doctoral students and postdocs register the time used in Agresso (if you have questions about this contact Gunnar Jensen). Help with statistics Research scientist Even Bergseng Discipline: Forest economy, forest policies, forest models Statistical expertise: Regression analysis, models with random and fixed effects, controlled/truncated data, some time series modelling, parametric and non-parametric effectiveness analyses Software: Stata, Excel Postdoc. Ole Martin Bollandsås Discipline: Forest production, forest inventory Statistics expertise: Regression analysis, sampling Software: SAS, R Associate Professor Sjur Baardsen Discipline: Econometric analysis of markets in the forest sector Statistical expertise: General, although somewhat “rusty”, expertise in many econometric topics (all-rounder) Software: Shazam, Frontier Associate Professor Terje Gobakken Discipline: GIS og long-term predictions Statistical expertise: Regression analysis, ANOVA and PLS regression Software: SAS, R Ph.D. Student Espen Halvorsen Discipline: Forest economy, forest management planning Statistical expertise: OLS, GLS, hypothesis testing, autocorrelation, ANOVA, categorical data, GLM, ANOVA Software: (partly) Shazam, Minitab og JMP Ph.D. Student Jan Vidar Haukeland Discipline: Nature based tourism Statistical expertise: Regression and factor analysis Software: SPSS Associate Professor Olav Høibø Discipline: Wood technology Statistical expertise: Planning of experiments, regression analysis (linear and non-linear), ANOVA, random and non-random effects, categorical data, multivariate analysis Software: R, JMP, Unscrambler, some SAS Ph.D. -
TSP 5.0 Reference Manual
TSP 5.0 Reference Manual Bronwyn H. Hall and Clint Cummins TSP International 2005 Copyright 2005 by TSP International First edition (Version 4.0) published 1980. TSP is a software product of TSP International. The information in this document is subject to change without notice. TSP International assumes no responsibility for any errors that may appear in this document or in TSP. The software described in this document is protected by copyright. Copying of software for the use of anyone other than the original purchaser is a violation of federal law. Time Series Processor and TSP are trademarks of TSP International. ALL RIGHTS RESERVED Table Of Contents 1. Introduction_______________________________________________1 1. Welcome to the TSP 5.0 Help System _______________________1 2. Introduction to TSP ______________________________________2 3. Examples of TSP Programs _______________________________3 4. Composing Names in TSP_________________________________4 5. Composing Numbers in TSP _______________________________5 6. Composing Text Strings in TSP_____________________________6 7. Composing TSP Commands _______________________________7 8. Composing Algebraic Expressions in TSP ____________________8 9. TSP Functions _________________________________________10 10. Character Set for TSP ___________________________________11 11. Missing Values in TSP Procedures _________________________13 12. LOGIN.TSP file ________________________________________14 2. Command summary_______________________________________15 13. Display Commands _____________________________________15 -
Towards a Fully Automated Extraction and Interpretation of Tabular Data Using Machine Learning
UPTEC F 19050 Examensarbete 30 hp August 2019 Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Per Hedbrant Master Thesis in Engineering Physics Department of Engineering Sciences Uppsala University Sweden Abstract Towards a fully automated extraction and interpretation of tabular data using machine learning Per Hedbrant Teknisk- naturvetenskaplig fakultet UTH-enheten Motivation A challenge for researchers at CBCS is the ability to efficiently manage the Besöksadress: different data formats that frequently are changed. Significant amount of time is Ångströmlaboratoriet Lägerhyddsvägen 1 spent on manual pre-processing, converting from one format to another. There are Hus 4, Plan 0 currently no solutions that uses pattern recognition to locate and automatically recognise data structures in a spreadsheet. Postadress: Box 536 751 21 Uppsala Problem Definition The desired solution is to build a self-learning Software as-a-Service (SaaS) for Telefon: automated recognition and loading of data stored in arbitrary formats. The aim of 018 – 471 30 03 this study is three-folded: A) Investigate if unsupervised machine learning Telefax: methods can be used to label different types of cells in spreadsheets. B) 018 – 471 30 00 Investigate if a hypothesis-generating algorithm can be used to label different types of cells in spreadsheets. C) Advise on choices of architecture and Hemsida: technologies for the SaaS solution. http://www.teknat.uu.se/student Method A pre-processing framework is built that can read and pre-process any type of spreadsheet into a feature matrix. Different datasets are read and clustered. An investigation on the usefulness of reducing the dimensionality is also done. -
Why SAS Programmers Should Learn Python Too Michael Stackhouse, Covance, Inc
PharmaSUG 2018 - Paper AD-12 Why SAS Programmers Should Learn Python Too Michael Stackhouse, Covance, Inc. ABSTRACT Day to day work can often require simple, yet repetitive tasks. All companies have tedious processes that may involve moving and renaming files, making redundant edits to code, and more. Often, one might also want to find or summarize information scattered amongst many different files as well. While SAS programmers are very familiar with the power of SAS, the power of some lesser known but readily available tools may go unnoticed. The programming language Python has many capabilities that can easily automate and facilitate these tasks. Additionally, many companies have adopted Linux and UNIX as their OS of choice. With a small amount of background, and an understanding of Linux/UNIX command line syntax, powerful tools can be developed to eliminate these tedious activities. Better yet, these tools can be written to “talk back” to the user, making them more user friendly for those averse to venturing from their SAS editor. This paper will explore the benefits of how Python can be adopted into a SAS programming world. INTRODUCTION SAS is a great language, and it does what it’s designed to do very well, but other languages are better catered to different tasks. One language in particular that can serve a number of different purposes is Python. This is for a number of reasons. First off, it’s easy. Python has a very spoken-word style of syntax that is both intuitive to use and easy to learn. This makes it a very attractive language for newcomers, but also an attractive language to someone like a SAS programmer. -
Base SAS® Software Flexible and Extensible Fourth-Generation Programming Language Designed for Data Access, Transformation and Reporting
FACT SHEET Base SAS® Software Flexible and extensible fourth-generation programming language designed for data access, transformation and reporting What does Base SAS® software do? Base SAS is a fourth-generation programming language (4GL) for data access, data transformation, analysis and reporting. It is included with the SAS Platform. Base SAS is designed for foundational data manipulation, information storage and retrieval, descrip- tive statistics and report writing. It also includes a powerful macro facility that reduces programming time and maintenance headaches. Why is Base SAS® software important? Base SAS runs on all major operating systems. It significantly reduces programming and maintenance time, while enabling your IT organization to produce the analyses and reports that decision makers need in the format they prefer. For whom is Base SAS® software designed? Base SAS is used by SAS programming experts and power users who prefer to code to manipulate data, produce and distribute ad hoc queries and reports, and/or interpret the results of descriptive data analysis. Many IT organizations struggle with functionality, including direct access to familiar Python interface using the SAS problems arising from complex and distrib- standardized data sources and advanced pipefitter package, fostering consis- uted data, spending excessive time synchro- statistical analysis. tency of code in the organization. nizing and reformatting data for various • Simplify reporting and delivery to applications. Producing accurate and visually Key Benefits mobile devices. Base SAS provides appealing reports often requires dispropor- maximum reporting flexibility. Easily • Integrate data across environments. tionate programming resources. Addition- create reports in formats such as RTF, Available with the SAS Platform, Base ally, IT often must manage a plethora of PDF, Microsoft PowerPoint, HTML and SAS integrates into any computing software packages that only support a e-books that can be read on many environment infrastructure, unifying specific demand. -
Statistics with Free and Open-Source Software
Free and Open-Source Software • the four essential freedoms according to the FSF: • to run the program as you wish, for any purpose • to study how the program works, and change it so it does Statistics with Free and your computing as you wish Open-Source Software • to redistribute copies so you can help your neighbor • to distribute copies of your modified versions to others • access to the source code is a precondition for this Wolfgang Viechtbauer • think of ‘free’ as in ‘free speech’, not as in ‘free beer’ Maastricht University http://www.wvbauer.com • maybe the better term is: ‘libre’ 1 2 General Purpose Statistical Software Popularity of Statistical Software • proprietary (the big ones): SPSS, SAS/JMP, • difficult to define/measure (job ads, articles, Stata, Statistica, Minitab, MATLAB, Excel, … books, blogs/posts, surveys, forum activity, …) • FOSS (a selection): R, Python (NumPy/SciPy, • maybe the most comprehensive comparison: statsmodels, pandas, …), PSPP, SOFA, Octave, http://r4stats.com/articles/popularity/ LibreOffice Calc, Julia, … • for programming languages in general: TIOBE Index, PYPL, GitHut, Language Popularity Index, RedMonk Rankings, IEEE Spectrum, … • note that users of certain software may be are heavily biased in their opinion 3 4 5 6 1 7 8 What is R? History of S and R • R is a system for data manipulation, statistical • … it began May 5, 1976 at: and numerical analysis, and graphical display • simply put: a statistical programming language • freely available under the GNU General Public License (GPL) → open-source -
What Is It and Why Should VFP Developers Care?
The X-Sharp Project What is it and why should VFP developers care? X# Vendor Session October 2019 X#: a new incarnation of an old development language X# Vendor Session October 2019 Agenda • A bit of history • Why X# • What is X# • X# and Visual FoxPro • New Features • Where can I get it X# Vendor Session October 2019 xBase languages, a bit of History -1 • Started at JPL with a project called Vulcan, by Wayne Ratliff (PTDOS, Later CP/M) (1978) • Ashton Tate bought it and released it under the name dBASE II for Apple II and DOS (1980) • An improved version dBASE III in 1984 • In the 80’s several competitors appeared on the market: Clipper, FoxBASE, DBXL, QuickSilver, Arago, Force, FlagShip and many more. Together these products were called “xBase”. The share the programming language and many functions and working with DBF files • Some were faster, others allowed to sell royalty free compiled versions of apps • Then Microsoft launched Windows in 1990 X# Vendor Session October 2019 xBase languages, a bit of History -2 • The move to Windows resulted in several product changes, also products were sold and resold: • dBase (AT) -> (Borland, Inprise, Borland) • QuickSilver -> Borland, then vanished • FlagShip -> MultiSoft (Various Unix versions, Windows) • Clipper (Nantucket)-> Visual Objects (Computer Associates) • FoxBASE (Fox Software) -> FoxPro (Microsoft) • Some products “died” • New products appeared, especially as successors of Clipper • (x) Harbour (open source, also for unix) • Xbase++ X# Vendor Session October 2019 xBase languages, a bit of History -3 • Now in the 2010’s most of these products are ‘dead’, no longer developed for various reasons.