Introduction to Stata
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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.................................... -
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. -
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. -
Data Workflows with Stata and Python
In [1]: from IPython.display import IFrame import ipynb_style from epstata import Stpy import pandas as pd from itertools import combinations from importlib import reload In [2]: reload(ipynb_style) ipynb_style.clean() #ipynb_style.presentation() #ipynb_style.pres2() Out[2]: Data Workflows in Stata and Python (http://www.stata.com) (https://www.python.org) Data Workflows in Stata and Python Dejan Pavlic, Education Policy Research Initiative, University of Ottawa Stephen Childs (presenter), Office of Institutional Analysis, University of Calgary (http://ucalgary.ca) (http://uottawa.ca/en) (http://socialsciences.uottawa.ca/irpe- epri/eng/index.asp) Introduction About this talk Objectives know what Python is and what advantages it has know how Python can work with Stata Please save questions for the end. Or feel free to ask me today or after the conference. Outline Introduction Overall Motivation About Python Building Blocks Running Stata from Python Pandas Python language features Workflows ETL/Data Cleaning Stata code generation Processing Stata output About Me Started using Stata in grad school (2006). Using Python for about 3 years. Post-Secondary Education sector University of Calgary - Institutional Analysis (https://oia.ucalgary.ca/Contact) Education Policy Research Initiative (http://socialsciences.uottawa.ca/irpe-epri/eng/index.asp) - University of Ottawa (a Stata shop) Motivation Python is becoming very popular in the data world. Python skills are widely applicable. Python is powerful and flexible and will help you get more done, -
Gretl User's Guide
Gretl User’s Guide Gnu Regression, Econometrics and Time-series Allin Cottrell Department of Economics Wake Forest university Riccardo “Jack” Lucchetti Dipartimento di Economia Università Politecnica delle Marche December, 2008 Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.1 or any later version published by the Free Software Foundation (see http://www.gnu.org/licenses/fdl.html). Contents 1 Introduction 1 1.1 Features at a glance ......................................... 1 1.2 Acknowledgements ......................................... 1 1.3 Installing the programs ....................................... 2 I Running the program 4 2 Getting started 5 2.1 Let’s run a regression ........................................ 5 2.2 Estimation output .......................................... 7 2.3 The main window menus ...................................... 8 2.4 Keyboard shortcuts ......................................... 11 2.5 The gretl toolbar ........................................... 11 3 Modes of working 13 3.1 Command scripts ........................................... 13 3.2 Saving script objects ......................................... 15 3.3 The gretl console ........................................... 15 3.4 The Session concept ......................................... 16 4 Data files 19 4.1 Native format ............................................. 19 4.2 Other data file formats ....................................... 19 4.3 Binary databases .......................................... -
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 -
Introduction to Stata
Introduction to Stata Christopher F Baum Faculty Micro Resource Center Boston College August 2011 Christopher F Baum (Boston College FMRC) Introduction to Stata August 2011 1 / 157 Strengths of Stata What is Stata? Overview of the Stata environment Stata is a full-featured statistical programming language for Windows, Mac OS X, Unix and Linux. It can be considered a “stat package,” like SAS, SPSS, RATS, or eViews. Stata is available in several versions: Stata/IC (the standard version), Stata/SE (an extended version) and Stata/MP (for multiprocessing). The major difference between the versions is the number of variables allowed in memory, which is limited to 2,047 in standard Stata/IC, but can be much larger in Stata/SE or Stata/MP. The number of observations in any version is limited only by memory. Christopher F Baum (Boston College FMRC) Introduction to Stata August 2011 2 / 157 Strengths of Stata What is Stata? Stata/SE relaxes the Stata/IC constraint on the number of variables, while Stata/MP is the multiprocessor version, capable of utilizing 2, 4, 8... processors available on a single computer. Stata/IC will meet most users’ needs; if you have access to Stata/SE or Stata/MP, you can use that program to create a subset of a large survey dataset with fewer than 2,047 variables. Stata runs on all 64-bit operating systems, and can access larger datasets on a 64-bit OS, which can address a larger memory space. All versions of Stata provide the full set of features and commands: there are no special add-ons or ‘toolboxes’. -
Statistical Software
Statistical Software A. Grant Schissler1;2;∗ Hung Nguyen1;3 Tin Nguyen1;3 Juli Petereit1;4 Vincent Gardeux5 Keywords: statistical software, open source, Big Data, visualization, parallel computing, machine learning, Bayesian modeling Abstract Abstract: This article discusses selected statistical software, aiming to help readers find the right tool for their needs. We categorize software into three classes: Statisti- cal Packages, Analysis Packages with Statistical Libraries, and General Programming Languages with Statistical Libraries. Statistical and analysis packages often provide interactive, easy-to-use workflows while general programming languages are built for speed and optimization. We emphasize each software's defining characteristics and discuss trends in popularity. The concluding sections muse on the future of statistical software including the impact of Big Data and the advantages of open-source languages. This article discusses selected statistical software, aiming to help readers find the right tool for their needs (not provide an exhaustive list). Also, we acknowledge our experiences bias the discussion towards software employed in scholarly work. Throughout, we emphasize the software's capacity to analyze large, complex data sets (\Big Data"). The concluding sections muse on the future of statistical software. To aid in the discussion, we classify software into three groups: (1) Statistical Packages, (2) Analysis Packages with Statistical Libraries, and (3) General Programming Languages with Statistical Libraries. This structure -
STAT 3304/5304 Introduction to Statistical Computing
STAT 3304/5304 Introduction to Statistical Computing Statistical Packages Some Statistical Packages • BMDP • GLIM • HIL • JMP • LISREL • MATLAB • MINITAB 1 Some Statistical Packages • R • S-PLUS • SAS • SPSS • STATA • STATISTICA • STATXACT • . and many more 2 BMDP • BMDP is a comprehensive library of statistical routines from simple data description to advanced multivariate analysis, and is backed by extensive documentation. • Each individual BMDP sub-program is based on the most competitive algorithms available and has been rigorously field-tested. • BMDP has been known for the quality of it’s programs such as Survival Analysis, Logistic Regression, Time Series, ANOVA and many more. • The BMDP vendor was purchased by SPSS Inc. of Chicago in 1995. SPSS Inc. has stopped all develop- ment work on BMDP, choosing to incorporate some of its capabilities into other products, primarily SY- STAT, instead of providing further updates to the BMDP product. • BMDP is now developed by Statistical Solutions and the latest version (BMDP 2009) features a new mod- ern user-interface with all the statistical functionality of the classic program, running in the latest MS Win- dows environments. 3 LISREL • LISREL is software for confirmatory factor analysis and structural equation modeling. • LISREL is particularly designed to accommodate models that include latent variables, measurement errors in both dependent and independent variables, reciprocal causation, simultaneity, and interdependence. • Vendor information: Scientific Software International http://www.ssicentral.com/ 4 MATLAB • Matlab is an interactive, matrix-based language for technical computing, which allows easy implementation of statistical algorithms and numerical simulations. • Highlights of Matlab include the number of toolboxes (collections of programs to address specific sets of problems) available. -
Software Packages – Overview There Are Many Software Packages Available for Performing Statistical Analyses. the Ones Highlig
APPENDIX C SOFTWARE OPTIONS Software Packages – Overview There are many software packages available for performing statistical analyses. The ones highlighted here are selected because they provide comprehensive statistical design and analysis tools and a user friendly interface. SAS SAS is arguably one of the most comprehensive packages in terms of statistical analyses. However, the individual license cost is expensive. Primarily, SAS is a macro based scripting language, making it challenging for new users. However, it provides a comprehensive analysis ranging from the simple t-test to Generalized Linear mixed models. SAS also provides limited Design of Experiments capability. SAS supports factorial designs and optimal designs. However, it does not handle design constraints well. SAS also has a GUI available: SAS Enterprise, however, it has limited capability. SAS is a good program to purchase if your organization conducts advanced statistical analyses or works with very large datasets. R R, similar to SAS, provides a comprehensive analysis toolbox. R is an open source software package that has been approved for use on some DoD computer (contact AFFTC for details). Because R is open source its price is very attractive (free). However, the help guides are not user friendly. R is primarily a scripting software with notation similar to MatLab. R’s GUI: R Commander provides limited DOE capabilities. R is a good language to download if you can get it approved for use and you have analysts with a strong programming background. MatLab w/ Statistical Toolbox MatLab has a statistical toolbox that provides a wide variety of statistical analyses. The analyses range from simple hypotheses test to complex models and the ability to use likelihood maximization. -
The Use of PSPP Software in Learning Statistics. European Journal of Educational Research, 8(4), 1127-1136
Research Article doi: 10.12973/eu-jer.8.4.1127 European Journal of Educational Research Volume 8, Issue 4, 1127 - 1136. ISSN: 2165-8714 http://www.eu-jer.com/ The Use of PSPP Software in Learning Statistics Minerva Sto.-Tomas Darin Jan Tindowen* Marie Jean Mendezabal University of Saint Louis, PHILIPPINES University of Saint Louis, PHILIPPINES University of Saint Louis, PHILIPPINES Pyrene Quilang Erovita Teresita Agustin University of Saint Louis, PHILIPPINES University of Saint Louis, PHILIPPINES Received: July 8, 2019 ▪ Revised: August 31, 2019 ▪ Accepted: October 4, 2019 Abstract: This descriptive and correlational study investigated the effects of using PSPP in learning Statistics on students’ attitudes and performance. The respondents of the study were 200 Grade 11 Senior High School students who were enrolled in Probability and Statistics subject during the Second Semester of School Year 2018-2019. The respondents were randomly selected from those classes across the different academic strands that used PSPP in their Probability and Statistics subject through stratified random sampling. The results revealed that the students have favorable attitudes towards learning Statistics with the use of the PSPP software. The students became more interested and engaged in their learning of statistics which resulted to an improved academic performance. Keywords: Probability and statistics, attitude, PSPP software, academic performance, technology. To cite this article: Sto,-Tomas, M., Tindowen, D. J, Mendezabal, M. J., Quilang, P. & Agustin, E. T. (2019). The use of PSPP software in learning statistics. European Journal of Educational Research, 8(4), 1127-1136. http://doi.org/10.12973/eu-jer.8.4.1127 Introduction The rapid development of technology has brought remarkable changes in the modern society and in all aspects of life such as in politics, trade and commerce, and education.