This document provides an overview and instructions for using the InnerSoft STATS statistical analysis software. It covers installation, activation, working in the main windows, and descriptive and inferential statistical tests available in the software. The document recommends limiting the number of columns when working with large datasets to avoid memory issues. It also notes that the software works best for datasets with millions of values rather than for scientific applications requiring very large or small numbers.
This document provides an introduction and overview of the statistical software InnerSoft STATS. It describes the software's installation process, licensing and activation procedures. It also outlines the main program windows including the project manager, worksheet and output windows. Finally, it provides guidance on working within these windows, such as opening and saving files, setting project properties, and entering and analyzing data. The document is intended to help new users understand the basic functionality and navigation of the InnerSoft STATS interface.
The document provides an introduction to SQL and relational databases. It describes how relational databases organize data into tables with rows and columns, and how primary keys and foreign keys link related data across multiple tables. It introduces SQL as a non-procedural language used to communicate with the database management system to query, insert, update and delete data from relational databases in a simple English-like syntax.
Waiting too long for Excel's VLOOKUP? Use SQLite for simple data analysis!Amanda Lam
** This workshop was conducted in the Hong Kong Open Source Conference 2017 **
Excel formulas can be quite slow when you're processing data files with thousands of rows. It's also especially difficult to maintain the files when you have some messy mixture of VLOOKUPs, Pivot Tables, Macros and VBAs.
In this interactive workshop targeted for non-coders, we will make use of SQLite, a very lightweight and portable open source database library, to perform some simple and repeatable data analysis on large datasets that are publicly available. We will also explore what you can further do with the data by using some powerful extensions of SQLite.
While SQLite may not totally replace Excel in many ways, after the workshop you will find that it can improve your work efficiency and make your life much easier in so many use cases!
Who should attend this workshop?
- If you're frustrated with the slow performance of Excel formulas when dealing with large datasets in your daily work
- No coding experience is required
This document introduces the software SPSS by describing its uses, how to open it, its basic layout and interface elements, and how to exit the program. Key points covered include that SPSS can perform statistical analyses and data management, it has menus and toolbars to access commands, and its main windows are the Data Editor and Output Viewer. The document provides instructions for navigating the menus and icons and becoming familiar with SPSS's interface.
SPSS is a statistical software package used for data management and analysis. It allows users to enter and manipulate data, conduct a wide range of statistical analyses, and generate graphs. SPSS files come in three types: .SAV files contain the data, .SPO files contain output, and .SPS files contain syntax. The data editor interface allows users to view, enter, edit, and sort data. It also enables data manipulation through functions like computing new variables and recoding existing ones. SPSS can be used to produce basic descriptive statistics, frequencies, cross-tabulations, and more advanced analyses like correlations, t-tests, regression and ANOVA. Syntax files allow users to save and re-run analyses.
01 laboratory exercise 1 - DESIGN A SIMPLE DATABASE APPLICATIONAnne Lee
This document provides instructions for students to complete a laboratory exercise to design a simple database application in SQL Anywhere. The objectives are to create a database and tables, perform basic data manipulation using SQL statements. The document describes setting up the database named "addbase" and creating tables for a hotel reservation system with sample data. It also includes instructions for activities like updating table data and performing SQL queries.
This document provides definitions and explanations of key concepts in database management systems. It defines DBMS, RDBMS, SQL, databases, tables, fields, primary keys, unique keys, foreign keys, joins, normalization, denormalization, indexes, views, stored procedures, triggers, and more. It also explains differences between concepts like DELETE vs TRUNCATE and local vs global variables.
This document discusses importing and linking data from other databases into Microsoft Access. It provides guidelines for when to import vs link, including importing when the external file is small and not frequently updated, or when replacing an old database. Linking is preferred when the external file is large, frequently updated, or needs to be shared across a network. The document also covers how to import and link Excel, text, and dBASE files, as well as Access tables, queries, forms and other objects. It describes modifying linked tables and using the Linked Table Manager utility.
This document provides an introduction and overview of the statistical software InnerSoft STATS. It describes the software's installation process, licensing and activation procedures. It also outlines the main program windows including the project manager, worksheet and output windows. Finally, it provides guidance on working within these windows, such as opening and saving files, setting project properties, and entering and analyzing data. The document is intended to help new users understand the basic functionality and navigation of the InnerSoft STATS interface.
The document provides an introduction to SQL and relational databases. It describes how relational databases organize data into tables with rows and columns, and how primary keys and foreign keys link related data across multiple tables. It introduces SQL as a non-procedural language used to communicate with the database management system to query, insert, update and delete data from relational databases in a simple English-like syntax.
Waiting too long for Excel's VLOOKUP? Use SQLite for simple data analysis!Amanda Lam
** This workshop was conducted in the Hong Kong Open Source Conference 2017 **
Excel formulas can be quite slow when you're processing data files with thousands of rows. It's also especially difficult to maintain the files when you have some messy mixture of VLOOKUPs, Pivot Tables, Macros and VBAs.
In this interactive workshop targeted for non-coders, we will make use of SQLite, a very lightweight and portable open source database library, to perform some simple and repeatable data analysis on large datasets that are publicly available. We will also explore what you can further do with the data by using some powerful extensions of SQLite.
While SQLite may not totally replace Excel in many ways, after the workshop you will find that it can improve your work efficiency and make your life much easier in so many use cases!
Who should attend this workshop?
- If you're frustrated with the slow performance of Excel formulas when dealing with large datasets in your daily work
- No coding experience is required
This document introduces the software SPSS by describing its uses, how to open it, its basic layout and interface elements, and how to exit the program. Key points covered include that SPSS can perform statistical analyses and data management, it has menus and toolbars to access commands, and its main windows are the Data Editor and Output Viewer. The document provides instructions for navigating the menus and icons and becoming familiar with SPSS's interface.
SPSS is a statistical software package used for data management and analysis. It allows users to enter and manipulate data, conduct a wide range of statistical analyses, and generate graphs. SPSS files come in three types: .SAV files contain the data, .SPO files contain output, and .SPS files contain syntax. The data editor interface allows users to view, enter, edit, and sort data. It also enables data manipulation through functions like computing new variables and recoding existing ones. SPSS can be used to produce basic descriptive statistics, frequencies, cross-tabulations, and more advanced analyses like correlations, t-tests, regression and ANOVA. Syntax files allow users to save and re-run analyses.
01 laboratory exercise 1 - DESIGN A SIMPLE DATABASE APPLICATIONAnne Lee
This document provides instructions for students to complete a laboratory exercise to design a simple database application in SQL Anywhere. The objectives are to create a database and tables, perform basic data manipulation using SQL statements. The document describes setting up the database named "addbase" and creating tables for a hotel reservation system with sample data. It also includes instructions for activities like updating table data and performing SQL queries.
This document provides definitions and explanations of key concepts in database management systems. It defines DBMS, RDBMS, SQL, databases, tables, fields, primary keys, unique keys, foreign keys, joins, normalization, denormalization, indexes, views, stored procedures, triggers, and more. It also explains differences between concepts like DELETE vs TRUNCATE and local vs global variables.
This document discusses importing and linking data from other databases into Microsoft Access. It provides guidelines for when to import vs link, including importing when the external file is small and not frequently updated, or when replacing an old database. Linking is preferred when the external file is large, frequently updated, or needs to be shared across a network. The document also covers how to import and link Excel, text, and dBASE files, as well as Access tables, queries, forms and other objects. It describes modifying linked tables and using the Linked Table Manager utility.
社會網絡分析UCINET Quick Start Guide
This guide provides a quick introduction to UCINET. It assumes that the software has been installedwith the data in the folder C:\Program Files\Analytic Technologies\Ucinet 6\DataFiles and this hasbeen left as the default directory.
Source : https://sites.google.com/site/ucinetsoftware/home
This document provides an overview of using SPSS (Statistical Package for the Social Sciences) software. It introduces the main interfaces for working with data in SPSS, including the data view, variable view, output view, draft view, and syntax view. It also provides instructions for installing sample data files and demonstrates how to generate a basic cross-tabulation output of employment by gender using the automated features.
The document provides information about creating a student profile database in Microsoft Access 2003. It includes 3 activities: (1) starting Microsoft Access and creating a new database file, (2) opening an existing database, and (3) creating a table to store student profile data. The table will include fields for a student number, name, address, and IC number. The document teaches how to define the field types and insert records into the new table.
This document provides an overview of how to use Microsoft Excel 2007. It discusses key Excel concepts like workbooks, worksheets, cells, rows, columns, formulas, and functions. It also provides instructions for common Excel tasks like navigating cells, entering and editing data, using autofill, sorting and filtering data, creating formulas, charts, and pivot tables. The document is intended to help new Excel users learn the basics of the program.
The document describes setting up JasperReports Server and uploading customer service point (CSP) target reports. It discusses authentication and roles, designing reports using iReport, and scheduling reports. It then details uploading CSP target reports for Eko India Financial Services. The steps include importing frameworks, creating Java classes and JSP pages, mapping to Hibernate, and reading the target Excel file to save data to database tables. The output displays new/updated CSP codes and total volumes uploaded for the month. Problems faced were committing for each CSP, slowing the upload.
The document discusses various database concepts in Microsoft Access including relationships between tables, creating select queries using the query design grid, building forms to enter and display data, generating reports from tables, and careers related to database administration. It provides step-by-step instructions on linking tables using primary and foreign keys, designing select queries, building basic forms, and generating simple reports from tables in Access.
This document provides an overview of the basic parts and functions of the Microsoft Excel window and worksheets. It describes:
- The Excel window contains elements like the title bar, menu bar, toolbar, column/row headings, name box, formula bar, and sheets tabs.
- A workbook contains one or more worksheets in the form of a grid made up of columns and rows. Each cell at the intersection of a column and row has a unique address.
- Common file operations in Excel include creating a new blank workbook, saving a workbook for the first time or with changes, and opening a previously saved workbook. Understanding these functions is important for working in Excel.
This document provides an overview of databases and Microsoft Access. It defines what a database is and its key components like fields, records and tables. It describes the two main types of databases: flat file and relational. The document outlines the main database objects in Access: tables, queries, forms and reports. It provides instructions on how to create a blank database or one using templates in Access. It also briefly discusses career opportunities in database administration.
Universe is a file that connects a database to a reporting tool. It contains table definitions, joins, and other metadata. To create a universe, tables are added from a database along with joins between tables. Loops, where tables are connected in a loop, can slow queries and are resolved through techniques like deleting unnecessary joins or using shortcut joins. Classes and hierarchies are then created to organize the data for reporting and allow drill-down/up functionality. The final step is exporting the universe file for use in the reporting tool.
Databases are collections of related files or integrated data that can be processed and stored electronically using database management systems like Microsoft Access. Key database concepts include tables, queries, forms, and reports. Tables store data in records and fields, queries search and filter data, forms display and enter data, and reports present data for printing. Databases offer advantages like sharing data across departments, security controls, fewer duplicate files, and improved data integrity compared to traditional paper-based systems.
A database is an organized collection of related information stored electronically in a computer system. It consists of records, files and fields. Records contain fields, which are individual pieces of information. Databases are useful for organizing, sorting, analyzing and finding information efficiently. Common examples of databases include student records, library catalogs, phone books and more. When designing a database, the goals, types of information, field names and consistency should be determined.
The document discusses how to create and manage a database in Microsoft Access. It explains how to define fields and records, create tables, establish relationships between tables using primary and foreign keys, and use the Access interface to create queries, forms, and reports. It also covers compacting and repairing databases to reduce file size, backing up databases for protection, and restoring databases from backups.
This document provides an overview of how to use Microsoft Access, a relational database management system. It discusses how Access allows for organized storage of data across multiple linked tables. It then outlines the basic steps for creating tables and entering data in Access, including defining fields, setting primary keys, and entering records. It also explains how to sort and search records, create relationships between tables, and design queries to extract specific data from the database. The document serves as a beginner's guide to getting started with the core functionality of Access.
This document provides an overview and introduction to Microsoft Access 2007. It discusses what a database is and how Access allows users to create computerized databases. It describes the basic Access interface elements like the navigation pane, ribbon, and views. It also introduces some common Access objects like tables, queries, forms, reports, macros and modules. The second half of the document focuses on creating and working with tables, including adding fields, assigning data types, setting field properties, and creating lookup columns to relate tables.
- Normalization is the process of structuring a database to minimize duplication and define relationships clearly. There are five normal forms that provide guidelines for optimal database design.
- Normalization specifies design criteria to guide the database structure and identify problems. It provides rules to reorganize data in a consistent, clean fashion.
- Denormalization intentionally introduces some data duplication to improve performance of complex queries by reducing the number of table joins required. It is typically done on read-only systems like data warehouses.
Microsoft Access 2010 allows users to create and manage databases. It includes tools like tables, queries, forms, reports, and a backstage view. The ribbon replaces menus and toolbars for navigating database objects stored in the file. Users can import data from Excel or text files into new or existing tables, and export data and tables to other formats like Excel.
Microsoft Office Access 2003 Tutorial for BeginnersAimina Salsabila
This document provides an overview of how to create and work with databases in Microsoft Access. It discusses how to create tables, enter and manipulate data, build relationships between tables, and construct queries to extract and analyze data. The key steps covered include designing tables with fields and a primary key, entering data, sorting and searching records, linking tables through relationships, and using the query designer to build queries with criteria. Being able to organize data into related tables and then retrieve specific information through queries are fundamental skills for working with relational database systems like Access.
The document provides an overview and demonstration of the SAS UTR application, which allows users to generate Uniform Technical Reports (UTRs) from clinical study data. It discusses the application components, how to import data using the UTR Helper Excel macro, running the SAS conversion macro, generating reports, and common troubleshooting issues.
Must be similar to screenshotsI must be able to run the projects.docxherthaweston
The document provides instructions for building a search engine application in three parts. It discusses requirements for each part, including designing user interfaces, implementing persistent data storage, and completing the application by implementing indexing and search functions. Suggestions are provided for data structures to represent the file list and inverted index, and algorithms for performing Boolean searches. The overall goal is to create an application that can index local text files and allow searching by word or phrase through a graphical user interface.
Laporan Praktikum Keamanan Siber - Tugas 3 - Kelas C - Kelompok 3.pdfIGedeArieYogantaraSu
This document provides instructions for a lab on working with text files in the command line interface (CLI) of a Linux system. It covers using both graphical and command line text editors to open, modify, and save text configuration files. Specifically, it introduces the SciTE graphical text editor and nano command line text editor. It also explains that configuration files for applications are typically found in a user's home directory, while system-wide configuration files are located in the /etc directory.
It is a Web Application, where people can donate leftover foods (in restaurants/hotels), used clothes to nearest Orphanages and it will help the people who are in need. I have used HTML, CSS, JavaScript, PHP, MySQL.
社會網絡分析UCINET Quick Start Guide
This guide provides a quick introduction to UCINET. It assumes that the software has been installedwith the data in the folder C:\Program Files\Analytic Technologies\Ucinet 6\DataFiles and this hasbeen left as the default directory.
Source : https://sites.google.com/site/ucinetsoftware/home
This document provides an overview of using SPSS (Statistical Package for the Social Sciences) software. It introduces the main interfaces for working with data in SPSS, including the data view, variable view, output view, draft view, and syntax view. It also provides instructions for installing sample data files and demonstrates how to generate a basic cross-tabulation output of employment by gender using the automated features.
The document provides information about creating a student profile database in Microsoft Access 2003. It includes 3 activities: (1) starting Microsoft Access and creating a new database file, (2) opening an existing database, and (3) creating a table to store student profile data. The table will include fields for a student number, name, address, and IC number. The document teaches how to define the field types and insert records into the new table.
This document provides an overview of how to use Microsoft Excel 2007. It discusses key Excel concepts like workbooks, worksheets, cells, rows, columns, formulas, and functions. It also provides instructions for common Excel tasks like navigating cells, entering and editing data, using autofill, sorting and filtering data, creating formulas, charts, and pivot tables. The document is intended to help new Excel users learn the basics of the program.
The document describes setting up JasperReports Server and uploading customer service point (CSP) target reports. It discusses authentication and roles, designing reports using iReport, and scheduling reports. It then details uploading CSP target reports for Eko India Financial Services. The steps include importing frameworks, creating Java classes and JSP pages, mapping to Hibernate, and reading the target Excel file to save data to database tables. The output displays new/updated CSP codes and total volumes uploaded for the month. Problems faced were committing for each CSP, slowing the upload.
The document discusses various database concepts in Microsoft Access including relationships between tables, creating select queries using the query design grid, building forms to enter and display data, generating reports from tables, and careers related to database administration. It provides step-by-step instructions on linking tables using primary and foreign keys, designing select queries, building basic forms, and generating simple reports from tables in Access.
This document provides an overview of the basic parts and functions of the Microsoft Excel window and worksheets. It describes:
- The Excel window contains elements like the title bar, menu bar, toolbar, column/row headings, name box, formula bar, and sheets tabs.
- A workbook contains one or more worksheets in the form of a grid made up of columns and rows. Each cell at the intersection of a column and row has a unique address.
- Common file operations in Excel include creating a new blank workbook, saving a workbook for the first time or with changes, and opening a previously saved workbook. Understanding these functions is important for working in Excel.
This document provides an overview of databases and Microsoft Access. It defines what a database is and its key components like fields, records and tables. It describes the two main types of databases: flat file and relational. The document outlines the main database objects in Access: tables, queries, forms and reports. It provides instructions on how to create a blank database or one using templates in Access. It also briefly discusses career opportunities in database administration.
Universe is a file that connects a database to a reporting tool. It contains table definitions, joins, and other metadata. To create a universe, tables are added from a database along with joins between tables. Loops, where tables are connected in a loop, can slow queries and are resolved through techniques like deleting unnecessary joins or using shortcut joins. Classes and hierarchies are then created to organize the data for reporting and allow drill-down/up functionality. The final step is exporting the universe file for use in the reporting tool.
Databases are collections of related files or integrated data that can be processed and stored electronically using database management systems like Microsoft Access. Key database concepts include tables, queries, forms, and reports. Tables store data in records and fields, queries search and filter data, forms display and enter data, and reports present data for printing. Databases offer advantages like sharing data across departments, security controls, fewer duplicate files, and improved data integrity compared to traditional paper-based systems.
A database is an organized collection of related information stored electronically in a computer system. It consists of records, files and fields. Records contain fields, which are individual pieces of information. Databases are useful for organizing, sorting, analyzing and finding information efficiently. Common examples of databases include student records, library catalogs, phone books and more. When designing a database, the goals, types of information, field names and consistency should be determined.
The document discusses how to create and manage a database in Microsoft Access. It explains how to define fields and records, create tables, establish relationships between tables using primary and foreign keys, and use the Access interface to create queries, forms, and reports. It also covers compacting and repairing databases to reduce file size, backing up databases for protection, and restoring databases from backups.
This document provides an overview of how to use Microsoft Access, a relational database management system. It discusses how Access allows for organized storage of data across multiple linked tables. It then outlines the basic steps for creating tables and entering data in Access, including defining fields, setting primary keys, and entering records. It also explains how to sort and search records, create relationships between tables, and design queries to extract specific data from the database. The document serves as a beginner's guide to getting started with the core functionality of Access.
This document provides an overview and introduction to Microsoft Access 2007. It discusses what a database is and how Access allows users to create computerized databases. It describes the basic Access interface elements like the navigation pane, ribbon, and views. It also introduces some common Access objects like tables, queries, forms, reports, macros and modules. The second half of the document focuses on creating and working with tables, including adding fields, assigning data types, setting field properties, and creating lookup columns to relate tables.
- Normalization is the process of structuring a database to minimize duplication and define relationships clearly. There are five normal forms that provide guidelines for optimal database design.
- Normalization specifies design criteria to guide the database structure and identify problems. It provides rules to reorganize data in a consistent, clean fashion.
- Denormalization intentionally introduces some data duplication to improve performance of complex queries by reducing the number of table joins required. It is typically done on read-only systems like data warehouses.
Microsoft Access 2010 allows users to create and manage databases. It includes tools like tables, queries, forms, reports, and a backstage view. The ribbon replaces menus and toolbars for navigating database objects stored in the file. Users can import data from Excel or text files into new or existing tables, and export data and tables to other formats like Excel.
Microsoft Office Access 2003 Tutorial for BeginnersAimina Salsabila
This document provides an overview of how to create and work with databases in Microsoft Access. It discusses how to create tables, enter and manipulate data, build relationships between tables, and construct queries to extract and analyze data. The key steps covered include designing tables with fields and a primary key, entering data, sorting and searching records, linking tables through relationships, and using the query designer to build queries with criteria. Being able to organize data into related tables and then retrieve specific information through queries are fundamental skills for working with relational database systems like Access.
The document provides an overview and demonstration of the SAS UTR application, which allows users to generate Uniform Technical Reports (UTRs) from clinical study data. It discusses the application components, how to import data using the UTR Helper Excel macro, running the SAS conversion macro, generating reports, and common troubleshooting issues.
Must be similar to screenshotsI must be able to run the projects.docxherthaweston
The document provides instructions for building a search engine application in three parts. It discusses requirements for each part, including designing user interfaces, implementing persistent data storage, and completing the application by implementing indexing and search functions. Suggestions are provided for data structures to represent the file list and inverted index, and algorithms for performing Boolean searches. The overall goal is to create an application that can index local text files and allow searching by word or phrase through a graphical user interface.
Laporan Praktikum Keamanan Siber - Tugas 3 - Kelas C - Kelompok 3.pdfIGedeArieYogantaraSu
This document provides instructions for a lab on working with text files in the command line interface (CLI) of a Linux system. It covers using both graphical and command line text editors to open, modify, and save text configuration files. Specifically, it introduces the SciTE graphical text editor and nano command line text editor. It also explains that configuration files for applications are typically found in a user's home directory, while system-wide configuration files are located in the /etc directory.
It is a Web Application, where people can donate leftover foods (in restaurants/hotels), used clothes to nearest Orphanages and it will help the people who are in need. I have used HTML, CSS, JavaScript, PHP, MySQL.
Atlas.ti 8 質性分析軟體新功能介紹!
需購買相關應用軟體請上 http://www.appcenter.com.tw/ or http://www.cheerchain.com.tw/
購買請洽 祺荃企業有限公司-您可以信賴的軟體供應商
www.cheerchain.com.tw or www.appcenter.com.tw
Email : info@cheerchain.com.tw Phone : +8864-23863559
NEW VERSION OUT NOW! We are happy to announce that ATLAS.ti 8 is released now! Completely re-designed in nearly every aspect, ATLAS.ti 8 Windows is poised to set new standards for computer-assisted qualitative data analysis. What's new? Find out about the new powerfull features of ATLAS.ti 8 here http://bit.ly/2hDIK0H.
**ATLAS.ti licenses purchased after April 1, 2015 qualify for a FREE UPGRADE
New Features
These are some of the powerful new features:
Under the hood: Clean separation of data layer, application logic, and user interface, latest technology for safe and reliable performance.
Unicode throughout
Undo/Redo (100 steps)
Direct import of Twitter, Endnote, Evernote data
Powerful Visual Query Editor for creating and modifying SmartCodes and SmartGroups
Full project search (former “Word cruncher”) significantly improved with dynamic fade-in/fade-out hit categories
Elegant and trememdously useful new network layout options
Network groups
Memo comments
State-of-the-art, highly intuitive user interface with ribbons, tabbed views, flexible navigation areas.
All tool windows can be freely positioned
Multiple documents
More powerful “margin” than ever, many new interactive functions.
Features Yet To Come
At the time of the RC1 release, the following areas are still missing or incomplete:
Project exchange between ATLAS.ti Mac and ATLAS.ti Windows
Teamwork scenario with central, shared project directories
Non-English user interface
Some specific functionalities (see below)
Functionality still to be added:
Transcription
Document editing
Print documents with margin
Global filters
Interrater reliability
Relative values in code-doc table
XML converter
需購買相關應用軟體請上 http://www.appcenter.com.tw/ or http://www.cheerchain.com.tw/
購買請洽 祺荃企業有限公司-您可以信賴的軟體供應商
www.cheerchain.com.tw or www.appcenter.com.tw
Email : info@cheerchain.com.tw Phone : +8864-23863559
This document provides an introduction to using SPSS software, including opening and saving data files, understanding the different windows and menus, and basic functions. It discusses that SPSS is a popular statistical software package used for data analysis. It describes the three main windows in SPSS: the Data Editor window for viewing and editing data, the Output Viewer window for viewing analysis results, and the Syntax Editor window for writing syntax commands. It also outlines the various menus within these windows and their functions, such as the Data, File, Edit, View and Transform menus in the Data Editor window and the Insert, Format and Utilities menus in the Output Viewer window.
The document provides an outline and overview of a STATA software training session covering topics such as the STATA environment, entering data, exploring data, descriptive analysis, and data management. Some key points:
- STATA is a statistical software package used for exploring, summarizing, and analyzing datasets. It has a complete set of statistical tools and is relatively easy to use.
- The STATA work environment includes windows for results, commands, variables, and a data editor. Do-files allow writing and saving commands for later execution.
- Data can be entered manually, imported from files like Excel or CSV, or using commands. Variable naming conventions and data types are explained.
- Exploring data
This document provides an introduction to SPSS including what SPSS is, how to open and save data files in SPSS, and an overview of the SPSS interface. SPSS stands for Statistical Package for the Social Sciences and is a popular software for analyzing data. The document discusses opening SPSS, accessing existing data files, the data editor interface which displays data in variable and data views, and how to save data files. It also summarizes the main menus in the data editor including file, edit, data, transform, analyze, and utilities and how they are used. Finally, it outlines the three main windows in SPSS - the data editor for defining and viewing data, the output viewer for viewing analysis results, and the
This document provides an overview of getting started with data analysis using Stata. It discusses what Stata is, describes the Stata screen and interface, and covers first steps like setting the working directory, creating log files, allocating memory, using do-files, opening and saving Stata data files, finding variables quickly, subsetting data using conditional statements, understanding Stata's color-coding system, importing data from other programs like SPSS and SAS, and provides an example of a dataset in Excel. The document serves as an introduction to basic functions and workflows in Stata.
This presentation illustrates DocIndex, InternetMiner and VisioDecompositer - my 3 proprietary test tools - and walks the user through how they are used effectively.
The tools are presented in the context of a Test Strategy and the emphasis is on HOW the tools are used and the rationale behind the esign of the tools.
View this presentation with SPEAKERS NOTES ON.
This document provides instructions for customizing reports generated from FanTestic blower door software. It describes how to open and edit an existing report template to add company logos and headers or footers with identifying information. It notes that template fields containing data from FanTestic tests should not be removed. The document also briefly outlines how to save, print, generate, and export FanTestic test results.
This document provides release notes for ExtendScript Toolkit CS5 (ESTK). It outlines new features such as the ability to change font size in the Console pane, convert tab stops to spaces, and automatically backup documents. It also describes known issues regarding editing read-only scripts, cross-suite script debugging not being supported, and issues with ScriptUI programming. The document provides details on hidden preferences that can be edited, and includes legal notices for third party software.
Tableau allows users to create dashboards that display multiple worksheets and views together for easy comparison of data. To create a dashboard, select Dashboard > New Dashboard from the menu. Views and objects can then be added and arranged on the dashboard. Parameters and filters can be used to make dashboards interactive and allow users to dynamically change the data displayed. Maintaining good performance in Tableau requires limiting the amount of data pulled into views through appropriate filtering and aggregation of data.
The CIMtrek Archiver user guide show how we extract notes data, forms and attachments into a desktop based folder solution or a SharePoint online document library.
Here are the key points covered in the essay:
- Exercise 15.1 involves creating a custom backup job in Windows 7 to back up selected files and folders to a hard disk partition.
- The C: system drive does not appear as a backup destination because you cannot back up a drive to itself.
- A warning appears when selecting the X: drive for backup because although it appears as a separate drive letter, it is physically located on the same hard disk as the system drive C:. Backing up to this location would not provide the benefits of an off-site backup if the hard disk failed.
- When selecting folders and files for backup, you must ensure the selected items are not part of an operating system
This document provides an overview of topics for getting started with data analysis using Stata. It covers basic first steps like setting the working directory, creating log files, and allocating memory. It also reviews opening and saving Stata data files, finding variables, subsetting data, and Stata's color-coding system for variable types. Additional sections describe importing data from Excel, SPSS, and SAS into Stata and exploring data through techniques such as frequencies, cross tabulations, and descriptive statistics.
Microsoft Word Editing Version 1.0Software Requirement Speci.docxbuffydtesurina
Microsoft Word Editing
Version: 1.0
Software Requirement Specification
Date: 7/3/2020
YLLC-001
Yohammed LLCSoftware Requirements SpecificationFor Microsoft WORD
Version 2016
Revision History
Date
Version
Description
Author
7/3/2020
1.0
Initial document
Mohammed Allibalogun
10/3/2020
1.0.1
Revise documentation of Initial document
Mohammed Allibalogun
Table of Contents
Contents
1. Introduction 5
1.1 Purpose 5
1.2 Scope 5
1.3 Definitions, Acronyms, Abbreviations 5
1.4 References 5
1.5 Overview 6
2. Overall Description 6
2.1 Use-Case Model Survey 6
2.1.1 Sign in 6
2.1.2 Open 6
2.1.3 New 7
2.1.4 Save 7
2.1.5 Save As 7
2.1.6 Export 7
2.1.7 Print 7
2.1.8 Change Font 7
2.1.9 Use case Diagram: 7
2.2 Assumptions and Dependencies 7
3. Specific Requirements 7
3.1 Use-Case Reports 8
3.1.1 Sign in 8
3.1.2 Open: 9
3.1.3 New: 10
3.1.4 Save: 11
3.1.5 Save As: 12
3.1.6 Export: 13
3.1.7 Print: 14
3.1.8 Change Font: 15
3.2 Supplementary Requirements 16
3.2.1 Performance: 16
3.2.2 Usability: 16
3.2.3 Supportability: 16
3.2.4 Configurability: 16
3.2.5 Recoverability: 16
Software Requirements SpecificationIntroduction
Microsoft Word is a word processor created by Microsoft. It was first discharged on October 25, 1983, under the name Multi-Tool Word for Xenix frameworks. Microsoft Word 2016 was released in the year 2016. The Microsoft Word application location was made to facilitate its users in ways where they could document things, save them on their hard drives or online, and even print them. With a wide range of scopes, any type of document such as assignments, reports, proposals, brochures, memorandums, etc. can be made on created through MS Word. When the file is saved, a .docx extension file is made and saved on the system. Even though MS Word is a very helpful application location, it still has its drawbacks. One of them is due to the presence of too many options. A novice user may feel overwhelmed with the number of features that can be executed through this software.Purpose
The purpose of the Microsoft Word application location is to document i.e. write any type of document such as assignments, quizzes, reports, etc. This does not mean that you can only write something on the word. You can also use tools to make your document look better such as using different layouts, different shapes, adding pictures and tables, etc. Thus, word lets you make a document and edit it. There are no critical bugs and the defect rate of MS Word is zero. The learning time for an average user is 30 to 60 minutes. Scope
The project aims to efficiently document your need for both, your professional or personal life. The focus of this application location is to provide help for the user to inscribe a document in a multitude of formats. This will provide more options and facilitate the user with different modules so the document can always look professional. Definitions, Acronyms, Abbreviations
Following are the abbreviations in t.
Microsoft Word Editing Version 1.0Software Requirement Speci.docxjessiehampson
This document provides a software requirements specification for Microsoft Word 2016. It includes an introduction, purpose, scope, definitions, and overview. Use cases are defined for signing in, opening, creating new files, saving, saving as, exporting, printing, and changing fonts. Requirements cover performance, usability, supportability, configurability, and recoverability. The 8 use cases are then described in more detail with normal and alternate flows and screenshots.
This guide introduces the user to dashboards in Style Intelligence. It explains that dashboards provide a powerful way to visualize and interact with data. The document describes how to open, close, and interact with dashboards using various input and selection components. It also covers dashboard features like bookmarks and scheduling.
The data processing program creates decision rules from a dataset using the ID3 algorithm. It cleanses data by removing inconsistencies and selects attributes using thresholds. The program builds an ID3 tree visualized in another window. Users can prune the tree and generate rules to test using different testing tools. The program has menus and tools to process data, visualize results, and test rule accuracy.
InnerSoft CAD es un programa para la elaboración de mediciones para proyectos de construcción en el entorno AutoCAD, la ayuda en tareas de topografía y la exportación / importación de datos de objetos entre AutoCAD y MS Excel. Con este programa usted podrá:
Realizar mediciones en AutoCAD y ordenarlas según Capítulos y Partidas previamente a su exportación a Excel o exportación en formato XML.
Crear listados de dibujos *.dxf y *.dwg presentes en el ordenador para su gestión.
Extraer todos los bloques de un dibujo en archivos individuales de AutoCAD (cada definición de bloque en un único archivo *.dwg).
Exportar a una tabla de Excel el área, longitud, volumen o coordenadas (además de otras) de tantos objetos de AutoCAD como elija en pantalla.
Importar desde Excel, según diferentes métodos, las coordenadas de una nube de puntos o bien el listado de vértices de un conjunto de polilíneas o polilíneas-3D. También dispone de un modulo para la importación de textos, según puntos de inserción para cada texto.
Consultar en pantalla la suma total del área o longitud de distintas entidades de AutoCAD, además de la longitud total de un camino trazado por puntos sobre el dibujo.
Hallar el perfil longitudinal o sección para un terreno, mallar terrenos o triangular una nube de puntos. Dibujar clotoides, parabolas, acuerdos verticales parabolicos, catenarias, curvas de transicion con clotoides y acuerdos horizontales circulares.
InnerSoft STATS - Methods and formulas helpInnerSoft
The document defines several statistical formulas for calculating measures such as mean, variance, skewness, and kurtosis from sample data. It also provides formulas for statistical tests like chi-square, Fisher's exact test, McNemar's test, and odds ratios. Sources are cited for each formula, typically a Wikipedia page on the given statistical measure or test.
This document provides an index of sections covered in the manual for the InnerSoft STATS application. It includes sections on installation and activation, an introduction to the application, how to work within the main window and project manager window, the analyze menu options which cover various statistical tests, the graphs menu options for creating different graph types, and contact information.
This document provides an overview of the different graph types and options available in the InnerSoft STATS software. It describes 7 main sections of the graphs menu, including general settings, grouping cases, categorical-quantitative graphs, two categorical variables, graph sequences, candlestick/point and figure/stock graphs, and scatter plots. For each section, it lists the available graph types and statistical options. It also provides brief descriptions and settings for specific graph types like Kagi, Renko, candlestick, and point and figure charts.
This document provides an overview and instructions for using the InnerSoft STATS software to analyze data. It describes 15 different analysis procedures that can be accessed from the software's Analyze Menu, including frequency tables, descriptive statistics, crosstabs, hypothesis tests, ANOVA, correlation, regression, and time series analysis. For each analysis procedure, it provides a brief overview and descriptions of the input options and statistical tests that can be selected. The document is intended to help users understand what types of analyses can be performed and how to set up and interpret the results.
Este documento presenta una guía de campo para las mejores prácticas de gestión de caminos rurales. La guía cubre temas como la planificación, diseño, construcción y mantenimiento de caminos rurales de manera de minimizar los impactos ambientales negativos. Incluye información sobre normas de diseño, hidrología, drenaje, estabilidad de taludes, materiales y control de erosión para construir caminos rurales de manera duradera y respetuosa con el medio ambiente.
InnerSoft CAD is a plug-in for AutoCAD that installs a set of productivity tools for Civil and Survey engineering, Counting, Estimating and measurements in construction project budgets. You can:
Export to an Excel Sheet the values of Area/Length property or coordinates for various AutoCAD entities.
Import from an Excel Sheet the vertex coordinates for a set of 2D polylines or 3D polylines (you can choose between 3 different methods). You can also import a set of points from Excel or a set of Texts with an insertion point for each one.
Extract all block definitions of a drawing in individual AutoCAD files (each block definition in a single file).
Sum the area or length property of a set of objects. Sum accumulated distance of a user defined walk in the drawing.
Draw the longitudinal profile of a set of entities from a user defined axis. Triangulate a set of points or mesh a model surface.
Take measurements on AutoCAD for construction project budgets.
Create, open or save different libraries, which contain a series of AutoCAD drawings (*.dxf or *.dwg) organized by books.
http://innersoft.itspanish.org/en/index.htm
Norma 3.1 ic. trazado, de la instrucción de carreterasInnerSoft
Este documento resume dos órdenes ministeriales que modifican normas sobre el trazado de carreteras en España. La primera orden modifica la Norma 3.1-IC Trazado de 1999 para considerar de forma más específica las carreteras urbanas y circunstancias especiales. La segunda orden modifica la regulación de accesos a carreteras para ajustar las conexiones con carreteras autonómicas y provinciales.
InnerSoft CAD es un programa para la elaboración de mediciones para proyectos de construcción en el entorno AutoCAD, la ayuda en tareas de topografía y la exportación / importación de datos de objetos entre AutoCAD y MS Excel. Con este programa usted podrá:
Realizar mediciones en AutoCAD y ordenarlas según Capítulos y Partidas previamente a su exportación a Excel o exportación en formato XML.
Crear listados de dibujos *.dxf y *.dwg presentes en el ordenador para su gestión.
Extraer todos los bloques de un dibujo en archivos individuales de AutoCAD (cada definición de bloque en un único archivo *.dwg).
Exportar a una tabla de Excel el área, longitud, volumen o coordenadas (además de otras) de tantos objetos de AutoCAD como elija en pantalla.
Importar desde Excel, según diferentes métodos, las coordenadas de una nube de puntos o bien el listado de vértices de un conjunto de polilíneas o polilíneas-3D. También dispone de un modulo para la importación de textos, según puntos de inserción para cada texto.
Consultar en pantalla la suma total del área o longitud de distintas entidades de AutoCAD, además de la longitud total de un camino trazado por puntos sobre el dibujo.
Hallar el perfil longitudinal o sección para un terreno, mallar terrenos o triangular una nube de puntos.
http://innersoft.itspanish.org/es/index.htm
Best 20 SEO Techniques To Improve Website Visibility In SERPPixlogix Infotech
Boost your website's visibility with proven SEO techniques! Our latest blog dives into essential strategies to enhance your online presence, increase traffic, and rank higher on search engines. From keyword optimization to quality content creation, learn how to make your site stand out in the crowded digital landscape. Discover actionable tips and expert insights to elevate your SEO game.
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
GraphRAG for Life Science to increase LLM accuracyTomaz Bratanic
GraphRAG for life science domain, where you retriever information from biomedical knowledge graphs using LLMs to increase the accuracy and performance of generated answers
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
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DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
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- Verstehen des DLAU-Tools und wie man es am besten nutzt
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- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
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See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
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2. APPLICATION MANUAL V0.4 InnerSoft STATS
2
INDEX
1. Installation and Activation
1.1. Installation
1.2. Trial Version
1.3. Activation
1.4. License
1.4.1. Upgrades
1.4.2. License Renewal
2. Introduction to ISSTATS
2.1. Open and Save files
2.2. Technical Limitations and Recommendations
3. Working in the Main window
3.1. File Menu
3.2. Analyze Menu
3.3. Help Menu
4. Working in the Worksheet window
4.1. File Menu
4.2. Edit Menu
4.3. Options Menu
5. Working in the Output window
6. Descriptive Statistics
7. One Sample Test
7.1. Z Test
7.2. T Test
7.3. Variance Test
8. Two-Sample Test
8.1. 2-Sample t-Test
8.2. Paired t-Test
8.3. 2 Variances F-Test
9. One-Way ANOVA
10. Homoscedasticity Tests
11. Methods and Formulas Help
3. APPLICATION MANUAL V0.4 InnerSoft STATS
3
1. – Installation and Activation
1.1. – Installation
Click on setup.exe to install the application.
1.2. – Trial Version
InnerSoft STATS works initially as limited trial version. You will not be allowed to use following tools:
Open or Save any Project or Worksheet.
Using Paste or Insert when editing cells.
1.3. – Activation
In order to use InnerSoft STATS Full Version, user must purchase a license before activating the product.
To purchase InnerSoft STATS Full Version, you must first contact with us by email
innersoft@itspanish.org
innersoft@gmail.com
You can also call us (Spain number phone):
There are different payment methods that you can find at:
http://isstats.itspanish.org/index.htm#buy
Once product has been paid, you can activate it. Just enter Help/Activate ISSTATS and follow instructions.
For a successful activation process, InnerSoft requires permissions to read/write files in its installation
folder, usually: 'C:Program FilesISSTATS'.
So may you have to login to Windows as Administrator or have to modify User Account Control in order
to activate successfully InnerSoft STATS.
4. APPLICATION MANUAL V0.4 InnerSoft STATS
4
1.4. – License
Registering data is chained to a PC Computer. When you pay a license, you can run InnerSoft STATS in
3 different computers. Thus you can ask for 3 Activation Codes.
You will not lose the license when formatting the hard drives or changing Windows S.O., BUT if you
make changes of hardware the activation code may become invalid.
Extra Activation Codes (more than 3) for same user have an additional cost.
1.4.1. – Upgrades
You will receive the Activation Codes for the product upgrades at no cost during one year. If you do not
renew the license after one year, will not lose the license of the product version that you purchased, BUT
will not receive new Activation Codes for the new software versions.
1.4.2. – License Renewal
The renewal will let you receive the Activation Codes for the new versions of the software during a year.
5. APPLICATION MANUAL V0.4 InnerSoft STATS
5
2. – Introduction to ISSTATS
When you start ISSTATS, a new, empty project is opened for you. You will see three windows:
Main window
o Project Manager window
o Worksheet window
o Output window
Controlling Windows
You can open and minimize ISSTATS windows just as you would other windows in your operating
system. You can also control ISSTATS windows with commands on the Window menu.
ISSTATS Environment
The project contains:
A Worksheet that contains your data. You can NOT have multiple worksheets in one project. Your data
will be displayed as columns. There is a Data window for your worksheet in the project. You can enter
and edit data directly in the Data window.
A Project Manager, which contains:
A Root folder for setting the project information.
A Settings folder for setting some options.
A Variables folder for your worksheet. This folder displays a summary of the columns used in the
worksheet.
An Output window that displays your results.
Multiple Toolbars for issuing commands and a Status Bar.
2.1. – Open and Save files
Use the file menu to open, close or save the three file types that ISSTATS can use.
Projects—contain a worksheet, along with project settings and project information
Worksheets—contain only your columns data
Outputs—contain the computed outputs
When you open a project file, the worksheet that were inside that project when you last saved is available
to you. When you save a project, the worksheet is saved within that project file.
The output results must be saved apart from the project file.
ISSTATS does not support multiple Worksheets or multiple Output documents. If you create a new table
or document, remember to save the previous data before it disappears.
You can save a project or worksheet in XML or Binary file format. The advantages and disadvantages of
using each format are:
6. APPLICATION MANUAL V0.4 InnerSoft STATS
6
XML
XML format is human readable and editable in plain text
Can be imported by lot of applications (Excel…)
Takes high size: storing a variable with 1’5 millions of values will take around 50 Mb in a XML
file
Has a high compression rate: a XML file of 50 Mb will get only 1 Mb of size once compressed in
zip format
Opening and saving is fast
Binary
Binary format is not human readable
Can be only open by ISSTATS
Takes less size than XML: storing a variable with 1’5 millions of values will take around 30 Mb
in a Binary file.
Has a low compression rate: a Binary file of 50 Mb will get 6 Mb of size once compressed in zip
format.
Opening and saving is slow.
Thus XML compressed in zip is the better way of storing large amounts of data.
2.2. – Technical Limitations and Recommendations
ISSTATS uses decimal data type instead of double data type. Decimal is specially used for financial and
monetary calculation which requires higher accuracy. It is not adequate for scientific applications because
of its lower range: the largest value is +/–7.9228162514264337593543950335, and the smallest nonzero
value is +/–0.0000000000000000000000000001 (+/–1E–28). So if you need to analyses social or
scientific data with large numbers (astronomy distances, human stupidity…), just use a different statistical
package.
ISSTATS work with on–memory data (no database storage). Thus it can compute quickly. In the other
hand, memory usage is huge.
ISSTATS can handle millions of data. It can compute all descriptive parameters of a variable with 1’5
million values in only a few seconds (Processor: Pentium R Dual Core CPU E 6600 @ 3.06 GHz 3.07
GHz RAM: 8.0 GB SO: Windows 7 64 Bits). In the other hand, the Data table can easily crash if you try
to manipulate this huge data. Operations like copying, pasting, removing and selecting cells in such a big
table can hang the application. It really depends on the total number of cells in the table. To avoid this
problem, limit the number of table columns when operating with huge data. If you want to handle
variables with millions of values, set the number of table columns to minimum in the Worksheet
OPTIONS menu: if you are using only 4 variables, set only 4 columns.
You should perform operations with columns as much as possible. If you want to clear a variable, select
the column and clear the column; avoid selecting all cells in the column and clearing these cells. If you
want to copy a variable then copy the column, not the cells.
Actually, undo/redo history has no limits. A limit will be implemented in a future. If you feel that
ISSTATS is consuming too much memory in your PC, you should save your project, restart the
application and open your project again. This will clear the undo/redo history.
Please, remember this is 0.4 version.
7. APPLICATION MANUAL V0.4 InnerSoft STATS
7
3. – Working in the Main window
3.1. – File Menu
New Project
Creating a new project closes the current project. If your current project has been changed since it was
last saved, ISSTATS will prompt you to save all or part of the project before closing it.
Open Project
Opening a new project closes the current project. If your current project has been changed since it was
last saved, ISSTATS will prompt you to save all or part of the project before closing it.
Save Project
Saves the current project in a XML or Binary file. If the project has been saved before, Save Project
saves the file with the current file name without displaying a dialog box. If the project has not been saved
before, Save Project opens the Save Project As dialog box.
When you save the project, you save the input information about your work:
the contents of project settings and project information
the columns of data in the Worksheet window
The output document will not be saved in your project file.
Save Project As
Saves the current project in a XML or Binary file with a different name. If the project has not been saved
before, choosing File > Save Project also opens this dialog box.
Dialog box items
Save in: Choose a drive and folder.
File name: Enter a file name.
Save as type: Choose a file type from the list.
o XML files (*.xml)
o DAT files (*.dat)
Exit
Exits you from ISSTATS. ISSTATS will prompt you to save the changes before closing the application.
3.2. – Analyze Menu
Descriptive Statistics: See Chapter 6. – Descriptive Statistics
One Sample Test: See Chapter 7. – One Sample Test
Two-Sample Test: See Chapter 8. – Two-Sample Test
8. APPLICATION MANUAL V0.4 InnerSoft STATS
8
One-Way ANOVA: See Chapter 9. – One-Way ANOVA
Homoscedasticity Tests: See Chapter 10. – Homoscedasticity Tests
3.3. – Help Menu
Contents
Open this PDF.
Buy ISSTATS
Open a web page with information about buying InnerSoft STATS.
Activate ISSTATS
Open the menu to activate ISSTATS in order to use all features.
About
Shows ISSTATS version and contact information.
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4. – Working in the Worksheet window
4.1. – File Menu
New Worksheet
Creating a new worksheet adds an empty worksheet to the current project. An empty Data window will
appear.
Open Worksheet
Copies data from a file into the current project. When you open a file, you copy the contents of the file
into the current project. Any changes you make to the file while in the project will not affect the original
file.
Save Worksheet
Saves the current worksheet in the same file and format as the file name displayed in the window title. If
the current worksheet is untitled, File > Save Worksheet brings up a dialog box where you can specify a
file name and location in which to save your data. See File > Save Worksheet As for a description of this
dialog box.
Choose File > Save Worksheet As to rename your worksheet or save it to a new location.
Save Worksheet As
Saves the worksheet data in a file. Use this command if you want to rename your worksheet or save it to a
new location.
Dialog box items
Save in: Choose a drive and folder.
File name: Enter a file name.
Save as type: Choose a file type from the list.
o XML files (*.xml)
o DAT files (*.dat)
Export to Excel
This tool will export to Excel only those columns that has a variable defined. Empty columns will not be
exported.
Exporting to Excel has some limitations. As worksheet limit size in Excel 2010 is 65,536 rows, ISSTATS
will divide the data collection in different sheets of 65530 rows everyone. If you are going to export 1’5
million values, the application will create 23 different Excel worksheets with 65530 rows each one. As
columns limit in Excel 2010 is 256 columns, ISSTATS will not export the data of more than 256
variables.
4.2. – Edit Menu
Undo & Redo
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To undo an action, click Undo Button on the Toolbar.
To redo an action that you undid, click Redo Button on the Toolbar.
Cut Cells
The selection will be copied to the clipboard buffer. The selection will be moved to a new location as
soon as you choose Paste command as described below.
Please note that the selection will remain in its current location until you paste it. If you want to delete a
selection without pasting it to a new location, use Delete command instead.
Copy Cells
The selection will be copied to the clipboard buffer. The selection will be copied to a new location as
soon as you choose Paste command as described below.
You can copy all the cells of a column or a row selecting the rows or columns and using the COPY
CELLS button. To avoid copying the variable names, when you select some columns and click the COPY
CELLS button, the name cells of the column is deselected.
Paste Cells
The contents of the clipboard will be pasted in the spreadsheet so that the selected cell becomes the top
left corner of the selection.
When you paste cells, the blank cells that you cut/copied will not modify any existing value and will not
add any new value to the column. The missing values that you copied will add new missing values or will
replace the existing ones with missing values.
If you copy a discontinuous group of cells, by selecting cells or block of cells while holding down the Ctrl
key, ISSTATS will paste it as a continuous selection where non selected cells are converted in empty
strings.
Insert Cells
You can insert copied cells above of the active cell on your worksheet, shifting other cells in the same
column down.
After you insert cells, the blank cells that you previously copied will add missing values. Inserting cells
below the last value of the column will do as pasting cells: blank cells will not add data.
Clear Cells
Erases the contents of the selected cells, without moving other cells. This menu command is available
when at least one cell is selected in the Data window. To delete selected cells, see Delete Cells.
In a numeric column, ISSTATS inserts system missing values (*) in a cleared cell.
Delete Cells
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If you are deleting cells, other cells automatically shift up. If you click a cell and then press DELETE,
Excel deletes the cell contents.
Select All Cells
Click this button to select all cells on the worksheet.
Copy Columns
When you copy various columns, alternated selected columns will be joined together in a continuous
selection once you paste or insert them (as Excel does).
Paste Columns
On pasting, empty selected columns will not modify any existing variables. Not empty selected columns
will replace any existing variables.
Insert Columns
You can insert columns that previously copied in the worksheet.
To insert a single column, select the column in the column immediately to the right of where you want to
insert the new column. To insert multiple columns, select the columns immediately to the right of where
you want to insert columns.
If you insert columns, other columns automatically shift to the right.
On inserting, empty columns (no variable defined) will add new empty columns (no variable defined).
Clear Columns Content
Erases the contents of the selected column, without moving other columns. This menu command is
available when at least one column is selected in the Data window. To delete selected columns, see Delete
Columns.
The cleared cells remain as blank cells on the worksheet.
Delete Columns
If you are deleting columns, other columns automatically shift to the left.
Select All Columns
The Select All Columns command will create a new selection that includes every column with data.
As rows have not any structural meaning in the table, there is no copy/cut/paste/insert options for rows. If
you want to copy all cells of a row just select the rows and press COPY CELLS button.
4.3. – Options Menu
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Table Decimal Places
For numbers that are already entered on a worksheet, you can increase or decrease the number of places
that are displayed after the decimal point by using the Table Decimal Places buttons. By default,
ISSTATS displays 3 decimal places when you apply a built-in number format to the cells.
Table Columns
You can increase or decrease the number of columns that are available in the worksheet by using the
Table Columns buttons. By default, ISSTATS displays 10 columns.
To avoid memory problems, limit the number of table columns when operating with huge data. If you
want to handle variables with millions of values, set the number of table columns to minimum: if you are
using only 4 variables, set only 4 columns.
Table Rows
You can increase or decrease the number of rows that are available in the worksheet by using the Table
Rows buttons. By default, ISSTATS displays 100 rows.
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5. – Working in the Output window
New Document
Creating a new output document closes the current document. If your current document has been changed
since it was last saved, ISSTATS will prompt you to save the document before closing it.
Open Document
Opening a new document closes the current document. If your current document has been changed since
it was last saved, ISSTATS will prompt you to save the document before closing it.
The file format of the opened document must be ISSTAT XAML package (a XAML package file
produced by ISSTAT).
Save Document
Saves the current document in a ISSTAT XAML package file (a XAML package file produced by
ISSTAT). If the document has been saved before, Save Document saves the file with the current file
name without displaying a dialog box. If the document has not been saved before, Save Document opens
the Save Document As dialog box.
Use Windows Clipboard to copy and paste the document content in other applications, such as MS Word,
MS Excel, Notepad…
Save Document As
Saves the document in a file. Use this command if you want to rename your document or save it to a new
location.
Print Document
Prints the document.
Cut, Copy & Paste
The way to copy or move a selection is to use cut, copy, and paste operations.
Use this commands to copy a selection from Output window to other applications, such as MS Word, MS
Excel, Notepad…
Undo & Redo
To undo an action, click Undo Button on the Toolbar.
To redo an action that you undid, click Redo Button on the Toolbar.
Bold, Italic, Underline, Grow Font, Shrink Font, Bullets, Numbering, Align Left, Align Center,
Align Right, Align Justify, Increase Indent & Decrease Indent
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These are the formatting buttons, familiar to anyone who has used a word processor. These formats are
applied by selecting some text in the window and clicking the button in the editor toolbar.
Select All
The Select All command will create a new selection that includes everything on the document.
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6. – Descriptive Statistics
Overview
Produces descriptive statistics for each column. The data columns must be numeric and contain at least
one numeric value. The dialog box allows to choose the statistics that you wish to display.
Available Variables list shows numeric data columns containing at least one no-missing value.
Dialog box items
Mean: Choose to display the arithmetic mean.
Sample Variance: Choose to display the unbiased variance of the data. Estimates population variance
based on a sample. If your data represents the entire population, then compute the variance by using Total
Variance.
Sample Standard Deviation: Choose to display the standard deviation of the data. Estimates population
standard deviation based on a sample. If your data represents the entire population, then compute the Std.
Deviation by using Total Std. Deviation.
Sample Coefficient of variation: Choose to display the coefficient of variation.
Sample Skewness: Choose to display the skewness value. Estimates population skewness based on a
sample. If your data represents the entire population, then compute the skewness by using Total
Skewness.
Sample Kurtosis: Choose to display the kurtosis value. Estimates population kurtosis based on a sample.
If your data represents the entire population, then compute the kurtosis by using Total Kurtosis.
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Total Variance: Choose to display the variance of the data. Calculates variance based on the entire
population.
Total Standard deviation: Choose to display the standard deviation of the data. Calculates Std. Deviation
based on the entire population.
Total Coefficient of variation: Choose to display the coefficient of variation. Calculates coefficient of
variation based on the entire population.
Total Skewness: Choose to display the skewness value. Calculates skewness based on the entire
population.
Total Kurtosis: Choose to display the kurtosis value. Calculates kurtosis based on the entire population.
SEM: Choose to display the standard error of the mean.
Sum: Choose to display the data sum.
Minimum: Choose to display the data minimum.
Maximum: Choose to display the data maximum.
Range: Choose to display the data range. Data Range is the difference between the maximum and
minimum.
Quartiles: Choose to display the first quartile, the median and the third quartile.
Interquartile range: Choose to display the difference between the first and third quartiles.
Deciles: Choose to display the nine values that divide the sorted data into ten equal parts.
Percentiles: To request a percentile:
Enter the desired value in the box placed in Percentiles group box. For example, if you wanted the
7th
percentile, you would enter a 7 in the box.
Click the add button to add the percentile to the list of requested percentiles.
Repeat Step 1 and 2 to add additional percentiles as desired.
If you need to delete a percentile, select it in the list and click the remove button.
Mode: Choose to display the mode and the number of times it occurs. If multiple modes exist, Minitab
displays the smallest modes, up to a total of four, along with their frequency.
Sum of squares: Choose to display the sum of the squared data values. This is the uncorrected sums of
squares, without first subtracting the mean.
MSSD: Choose to display half the Mean of Successive Squared Differences.
N nonmissing: Choose to display the number of nonmissing column entries.
N missing: Choose to display the number of missing column entries.
N total: Choose to display the total (nonmissing and missing) number of column entries.
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Cut–Points: Divide the data into a number of equal groups. For example, to create deciles, you would
enter 10 in the box .Enter 3 to divide the data into tertiles.
Check statistics
Check None: Choose to clear all check boxes and then individually check the statistics to display.
Check All: Choose to check all boxes. You can uncheck statistics as needed.
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7. – One Sample Test
Overview
In the box Test Type, choose the test to perform:
Z Test
T Test
Variance Test
Hypothesis Test
To perform a hypothesis test, check the box Perform Hypothesis Test and choose the alternative
hypothesis of the test:
Less than: Perform a level test of H0: ≥ 0 against the one-sided alternative H1: < 0
Not Equal: Perform a level test of H0: = 0 against the two-sided alternative H1: ≠ 0
Greater than: Perform a level test of H0: ≤ 0 against the one-sided alternative H1: > 0
Significance level of the test () derive from the 1- value set in the Confidence Level text box.
If you choose a lower-tailed hypothesis test, an upper confidence bound will be constructed. If you
choose an upper-tailed hypothesis test, a lower confidence bound will be constructed.
Available Variables list shows numeric data columns containing at least one no-missing value.
7.1. – Z Test
Use 1-Sample Z to compute a confidence interval or perform a hypothesis test of the mean when the
standard deviation of the population σ is known. The samples should come from a normal population if n
is low; if however n>30 the distribution of the population does not have to be normal.
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Dialog box items
Data: Choose the origin of the data.
Samples in columns: Choose if you have entered raw data in columns. Enter the columns
containing the sample data in the list Computing Variables. Move these variables from Available
Variables list to Computing Variables list using Add and Remove buttons. Enter the value for the
population standard deviation in the text box. Entering multiple columns, ISSTATS performs
separate one-sample analyses on each column.
Summarized data: Choose if you have summary values for the sample size and mean.
o Sample size: Enter the value for the sample size.
o Mean: Enter the value for the sample mean.
o Population Standard deviation: Enter the value for the population standard deviation.
Confidence level: Enter the level of confidence desired. Enter any number between 0 and 1. Entering 0,9
will result in a 90% confidence interval. The default is 0,95 = 95%.
Perform hypothesis test: Check to perform the hypothesis test.
Hypothesized mean: Enter the test mean 0.
Alternative hypothesis: Choose the alternative hypothesis of the test.
7.2. – T Test
Performs a one-sample t-test or t-confidence interval for the mean.
Use T Test for one sample to compute a confidence interval and perform a hypothesis test of the mean
when the population standard deviation, σ, is unknown. Use this test when samples come from a normal
population or n > 30.
Dialog box items
Data: Choose the origin of the data.
Samples in columns: Choose if you have entered raw data in columns. Enter the columns
containing the sample data in the list Computing Variables. Move these variables from Available
Variables list to Computing Variables list using Add and Remove buttons. Entering multiple
columns, ISSTATS performs separate one-sample analyses on each column
Summarized data: Choose if you have summary values for the sample size and mean.
o Sample size: Enter the value for the sample size.
o Mean: Enter the value for the sample mean.
o Sample Standard deviation: Enter the value for the sample standard deviation.
Confidence level: Enter the level of confidence desired. Enter any number between 0 and 1. Entering 0,9
will result in a 90% confidence interval. The default is 0,95 = 95%.
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Perform hypothesis test: Check to perform the hypothesis test.
Hypothesized mean: Enter the test mean 0.
Alternative hypothesis: Choose the alternative hypothesis of the test.
7.3. – Variance Test
This procedure calculates confidence intervals for the variance of a population, and performs a hypothesis
test to determine whether the population variance equals a specified value. Use this test when samples
come from a normal population.
Dialog box items
Data: Choose the origin of the data.
Samples in columns: Choose if you have entered raw data in columns. Enter the columns
containing the sample data in the list Computing Variables. Move these variables from Available
Variables list to Computing Variables list using Add and Remove buttons. Entering multiple
columns, ISSTATS performs separate one-sample analyses on each column.
Summarized data: Choose if you have summary values for the sample size and variance.
o Sample size: Enter the value for the sample size.
o Sample Variance: Enter the value for the sample variance.
Confidence level: Enter the level of confidence desired. Enter any number between 0 and 1. Entering 0,9
will result in a 90% confidence interval. The default is 0,95 = 95%.
Perform hypothesis test: Check to perform the hypothesis test.
Hypothesized variance: Enter the test variance σ2
0.
Alternative hypothesis: Choose the alternative hypothesis of the test.
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8. – Two-Sample Test
In the box Test Type, choose the test to perform:
2-Sample t-Test
Paired t-Test
2 Variances F-Test
Hypothesis Test
To perform a hypothesis test, check the box Perform Hypothesis Test and choose the Alternative
Hypothesis of the test:
Less than: Perform a level test of H0: ≥ d against the one-sided alternative H1: < d
Not Equal: Perform a level test of H0: = d against the two-sided alternative H1: ≠ d
Greater than: Perform a level test of H0: ≤ d against the one-sided alternative H1: > d
Significance level of the test () derive from the 1- value set in the Confidence Level text box.
If you choose a lower-tailed hypothesis test, an upper confidence bound will be constructed. If you
choose an upper-tailed hypothesis test, a lower confidence bound will be constructed.
Available Variables list shows numerical and text columns containing at least one no-missing value.
Text columns may be used as Subscripts.
8.1. – 2 Sample t-Test
Performs an independent 2-sample t-test and generates a confidence interval.
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When you have dependent samples, use 8.2. – Paired t-Test.
Use 2-Sample t-Test to perform a hypothesis test and compute a confidence interval of the difference
between two population means when the population standard deviations, σ's, are unknown.
Dialog box items
Data: Choose the origin of the data.
Samples in one column: Choose if the sample data are in a single column, differentiated by
subscript values (group codes) in a second column.
o Computing Variables: Enter the columns containing the sample data in the list
Computing Variables. Move these variables from Available Variables list to Computing
Variables list using Add / Remove buttons.
o Grouping Variable: Enter the column containing the sample subscripts to define the
groups. It may be a numerical or text column.
o Group 1/Group 2: Enter the subscripts that define both groups.
o Automatic Grouping: Check to let the application define automatically both groups. It
will identify the first two different subscripts in the grouping variable.
Samples in different columns: Choose if the data of the two samples are in separate columns.
o First Sample: Enter the column containing one sample from Available Variables list.
o Second Sample: Enter the column containing the other sample from Available Variables
list.
Summarized data: Choose if you have summary values for the sample size, mean, and variance
for each sample.
o First Sample
Sample size 1: Enter the sample size for the first sample.
Mean 1: Enter the value for the mean of the first sample.
Sample Variance 1: Enter the value for the variance of the first sample.
o Second Sample
Sample size 2: Enter the sample size for the second sample.
Mean 2: Enter the value for the mean of the second sample.
Sample Variance 2: Enter the value for the variance of the second sample.
Assume Population equal variances: Check to assume that the populations have equal variances.
Confidence level: Enter the level of confidence desired. Enter any number between 0 and 1. Entering 0,9
will result in a 90% confidence interval. The default is 0,95 = 95%.
Perform hypothesis test: Check to perform the hypothesis test.
Hypothesized mean: Enter the hypothesized difference between the two population means 1-2.
Alternative hypothesis: Choose the alternative hypothesis of the test.
A difference of 0 suggests in the null hypothesis the equality between mean populations; H0: 1-2 = 0
against an alternative H1: 1-2 ≠ 0
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A difference equal or less than 0 suggest in the null hypothesis that the first mean is equal or less than the
second; H0: 1-2 ≤ 0 against an alternative H1: 1-2 > 0
A difference equal or greater than 0 suggest in the null hypothesis that the first mean is equal or greater
than the second; H0: 1-2 ≥ 0 against an alternative H1: 1-2 < 0
Optionally, test ratios other than 0 (equality) can be specified. A difference of 2 suggests in the null
hypothesis the first mean is the second mean plus 2.
8.2. – Paired t-Test
Performs a paired t-test. This is appropriate for testing the mean difference between paired observations
when the paired differences follow a normal distribution.
Use the Paired t command to compute a confidence interval and perform a hypothesis test of the mean
difference between paired observations in the population. A paired t-test matches responses that are
dependent or related in a pairwise manner. A typical example of the repeated measures t-test would be
where subjects are tested prior to a treatment, say for high blood pressure, and the same subjects are
tested again after treatment with a blood-pressure lowering medication. Paired samples t-tests are often
referred to as "dependent samples t-tests". When the samples are drawn independently from two
populations, use 8.1. – 2 Sample t-Test.
Dialog box items
Data: Choose the origin of the data.
Sample in columns: Choose if you have entered raw data in two columns.
o First sample: Enter the column containing the first sample from Available Variables list.
o Second sample: Enter the column containing the second sample from Available Variables
list.
Pairs must have two numerical values. Pairs that have a missing data in any of the
members are ignored.
Summarized data: Choose if you have summary values for the sample size, mean, and variance
of the difference.
o Sample size: Enter the value for the sample size.
o Mean of Differences: Enter the value for the mean of differences ̅.
o Variance of Differences: Enter the value for the variance of differences s2
d.
Confidence level: Enter the level of confidence desired. Enter any number between 0 and 1. Entering 0,9
will result in a 90% confidence interval. The default is 0,95 = 95%.
Perform hypothesis test: Check to perform the hypothesis test.
Hypothesized mean: Enter the hypothesized population mean of the paired differences d.
Alternative hypothesis: Choose the alternative hypothesis of the test.
8.3. – 2 Variances F-Test
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The 2 Variances F-Test procedure performs hypothesis tests and computes confidence intervals for the
ratios between two populations' variances. Use this test to determine if one treatment condition has more
variability than the other. Each population must follow the normal distribution.
Dialog box items
Data: Choose the origin of the data.
Samples in one column: Choose if you have entered data into a single column with a second
column of subscripts that identify the samples.
o Computing Variables: Enter the columns containing the sample data in the list
Computing Variables. Move these variables from Available Variables list to Computing
Variables list using Add and Remove buttons. Entering multiple columns, ISSTATS
performs separate analyses on each column.
o Grouping Variable: Enter the column containing the sample subscripts to define the
groups. It may be a numerical or text column.
o Group 1/Group 2: Enter the subscripts that define both groups.
o Automatic Grouping: Check to let the application define automatically both groups. It
will identify the first two different subscripts in the grouping variable.
Samples in different columns: Choose if you have entered the data for the two samples into
separate columns.
o First Sample: Enter the column that contains the data for the first sample from Available
Variables list.
o Second Sample: Enter the column that contains the data for the second sample from
Available Variables list.
Summarized data: Choose if you have summary values for the sample sizes and variances.
o First Sample
Sample size 1: Enter the sample size for the first sample.
Sample Variance 1: Enter the variance for the first sample.
o Second Sample
Sample size 2: Enter the sample size for the second sample.
Sample Variance 2: Enter the variance for the second sample.
Confidence level: Enter the level of confidence desired. Enter any number between 0 and 1. Entering 0,9
will result in a 90% confidence interval. The default is 0,95 = 95%.
Perform hypothesis test: Check to perform the hypothesis test.
Hypothesized ratio: Enter the hypothesized ratio between two population’s variances σ2
1/σ2
2.
Alternative hypothesis: Choose the alternative hypothesis of the test.
A ratio of 1 suggests in the null hypothesis the equality between variance populations; H0: σ2
1/σ2
2 = 1
against an alternative H1: σ2
1/σ2
2 ≠ 1.
A ratio equal or less than 1 suggest in the null hypothesis that the first variance is equal or less than the
second; H0: σ2
1/σ2
2 ≤ 1 against an alternative H1: σ2
1/σ2
2 > 1.
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A ratio equal or greater than 1 suggest in the null hypothesis that the first variance is equal or greater than
the second; H0: σ2
1/σ2
2 ≥ 1 against an alternative H1: σ2
1/σ2
2 < 1.
Optionally, test ratios other than 1 (equality) can be specified. A ratio of 2 suggests the first variance is
double the second variance.
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9. – One-Way ANOVA
Performs a one-way analysis of variance. You can also perform multiple comparisons. The response
variable must be numeric. The factor level column (grouping variable) may be numeric or text.
Dialog box items
Data: Choose the origin of the data.
Groups in different Columns
o Computing Variables: Enter the columns containing the response. Each column must
contain the data for one of the groups.
Groups in 1 Column
o Computing Variables: Enter the column or columns containing the response. ISSTATS
performs a ANOVA test for each of these columns.
o Grouping Variable: Enter the column containing the factor levels.
Confidence level: Enter the confidence level.
For further details, see ANOVA Test.
Multiple Comparisons: Use to generate grouping information tables and confidence intervals for the
differences between means, by different methods. Check to obtain confidence intervals for all pairwise
differences between level means using any of these methods.
Scheffe
Tukey HSD: Tukey's Honestly Significant Difference test.
Sidak
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Fisher LSD: Fisher's Least Significant Difference test.
Bonferroni
For further details, see ANOVA Multiple Comparisons.
Include Brown-Forsythe Test for equality of Means
Use to test the equality of means when distribution violates the assumption of equal variances.
The Brown-Forsythe test cannot be computed if all groups have zero variance. To take part into Brown–
Forsythe test, a group must have at least two elements. In the situation that some groups have zero
standard deviations, the statistic can be computed but the approximation may not work.
For further details, see Brown–Forsythe Test for equality of means.
Include Welch’s Test for equality of Means
Use to test the equality of means when distribution violates the assumption of equal variances.
To take part into Welch’s test, a group must have non zero variance. Moreover, sample sizes of a group
have to be greater than or equal to 2.
For further details, see Welch’s Test for equality of means.
Remark: ISSTATS does not cancel a test if any of the groups does not fulfill the conditions. It simply
rejects the group. To take part into a ANOVA test, a group must have at least one element. To take part
into a Welch’s Test, a group must have at least two elements; moreover, the group must have a non-zero
variance. Thus, ISSTATS may use 4 of the groups to perform ANOVA Test but only 2 of the groups to
perform a Welch’s Test. It depends on the number of elements of each group. You should read the
Descriptive Statistics information to check the Rejected Groups and also should read the Total Groups
info of each test to check the number of groups being used in that test.
Outputs
Outputs include:
Descriptive statistic for every level of the factor’s variable.
Total mean.
Degrees Of Freedom: DFTotal, DFInter and DFIntra
The Sum Of The Squares: SSTotal, SSInter and SSIntra
The Mean Square: MSTotal, MSInter and MSIntra
The F ratio
The p-value
R-squared
R-squared adjusted
Inter is also referred as Between Groups or Between Treatments. Intra is also referred as Within Groups
or Error Term.
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R-squared represents the percentage of variation in a response variable that is explained by its relationship
with one predictor variable.
R-squared adjusted is a version of r-squared that has been adjusted for the number of predictors in the
model. R-squared tends to overestimate the strength of the association, especially when there are more
than one independent variables.
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10. – Homoscedasticity Tests
These tests are used to test if k samples are from populations with equal variances. The response variable
has to be numeric. The factor level column (grouping variable) may be numeric or text.
Dialog box items
In the box Test Type, choose the test to perform:
Levene's Test
Brown–Forsythe Test for equality of variances
Bartlett's Test
For further details, see Homoscedasticity Tests.
Remark: ISSTATS does not cancel a test if any of the groups does not fulfill the conditions. It simply
rejects the group. To take part into a Levene or Brown–Forsythe test, a group must have at least one
element. To take part into a Bartlett's Test, a group must have at least two elements; moreover, the group
must have a non-zero variance. Thus, ISSTATS may use 4 of the groups to perform Levene's Test but
only 2 of the groups to perform a Bartlett's Test. It depends on the number of elements of each group.
You should read the Descriptive Statistics information to check the Rejected Groups and also should read
the Total Groups info of each test to check the number of groups being used in that test.
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11. – Methods and Formulas Help
Mean
The arithmetic mean is the sum of a collection of numbers divided by the number of numbers in the
collection.
Sample Variance
The estimator of population variance, also called the unbiased sample variance, is:
∑ ( ̅)
Source: http://en.wikipedia.org/wiki/Variance
Sample Kurtosis
The estimators of population kurtosis is:
( )
( )( )( )
∑ ( ̅) ( )
( )( )
The standard error of the sample kurtosis of a sample of size n from the normal distribution is:
√
( ) ( )
( )( )( )( )( )
Source: http://en.wikipedia.org/wiki/Kurtosis#Estimators_of_population_kurtosis
Sample Skewness
Skewness of a population sample is estimated by the adjusted Fisher–Pearson standardized moment
coefficient:
( )( )
∑ (
̅
)
where n is the sample size and s is the sample standard deviation.
The standard error of the skewness of a sample of size n from a normal distribution is:
√
( )
( )( )( )
Source: https://en.wikipedia.org/wiki/Skewness#Sample_skewness
Total Variance
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Variance of the entire population is:
∑ ( ̅)
Source: http://en.wikipedia.org/wiki/Variance
Total Kurtosis
Kurtosis of the entire population is:
∑ ( ̅)
where n is the sample size and σ is the total standard deviation.
Source: http://en.wikipedia.org/wiki/Kurtosis
Total Skewness
Skewness of the entire population is:
∑ ( ̅)
where n is the sample size and σ is the total standard deviation.
Source: https://en.wikipedia.org/wiki/Skewness
Quantiles of a population
ISSTATS uses the same method as R–7, Excel CUARTIL.INC function, SciPy–(1,1), SPSS and Minitab.
Qp, the estimate for the kth
q–quantile, where p = k / q and h = (N–1)*p + 1, is computing by
Qp =
Linear interpolation of the modes for the order statistics for the uniform distribution on [0,1]. When p = 1,
use xN.
Source: http://en.wikipedia.org/wiki/Quantile#Estimating_the_quantiles_of_a_population
MSSD (Mean of the squared successive differences)
It is calculated by taking the sum of the differences between consecutive observations squared, then
taking the mean of that sum and dividing by two.
∑ ( )
( )
The MSSD has the desirable property that one half the MSSD is an unbiased estimator of true variance.
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Confidence Bounds and One-Sided Tests
Suppose you are testing the null hypothesis H0: ≥ 0 against the one-sided alternative H1: < 0. Rather
than give a two-sided confidence interval for , the more appropriate procedure is to give an upper
confidence bound in this setting. This upper confidence bound has a direct relationship to the one-sided
test, namely:
1. A level test of H0: ≥ 0 against the one-sided alternative H1: < 0 rejects H0 exactly when
the value 0 is above the 1–α upper confidence bound.
2. A level test of H0: ≤ 0 against the one-sided alternative H1: > 0 rejects H0 exactly when
the value 0 is above the 1–α lower confidence bound.
ANOVA Test
∑ ∑( ̅)
∑ (̅ ̅)
∑ ∑( ̅ )
DFTotal = N – 1
DFInter = k – 1
DFIntra = N – k
where
F is the result of the test
k is the number of different groups to which the sampled cases belong
∑ is the total sample size
ni is the number of cases in the i-th group
yij is the value of the measured variable for the j-th case from the i-th group
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̅ is the mean of all yij
̅ is the mean of the yij for group i.
The test statistic has a F-distribution with DFInter and DFIntra degrees of freedom. Thus the null
hypothesis is rejected if ( )
ANOVA Multiple Comparisons
Difference of Means
̅ ̅
Standard Error of the Difference of Means Estimator
√ ( )
Scheffe’s Method
Confidence Interval for Difference of Means
( ) ̅ ̅ √ ( ) ( )
Source: http://en.wikipedia.org/wiki/Scheff%C3%A9%27s_method
Tukey's range test HSD
Confidence Interval for Difference of Means
( ) ̅ ̅ ( ) √ ( )
Where q is the studentized range distribution.
Source: https://en.wikipedia.org/wiki/Tukey%27s_range_test
Fisher's Method LSD
If overall ANOVA test is not significant, you must not consider any results of Fisher test, significant or
not.
Confidence Interval for Difference of Means
( ) ̅ ̅ ( ⁄ ) √ ( )
Where t is the student distribution.
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Bonferroni's Method
The family-wise significance level (FWER) is α = 1 - Confidence Level. Thus any comparison flagged by
ISSTATS as significant is based on a Bonferroni Correction:
( )
( )
Where k is the number of groups.
Confidence Interval for Difference of Means
( ) ̅ ̅ ( ⁄ ) √ ( )
Where t is the student distribution.
Sidak's Method
The family-wise significance level (FWER) is α = 1 - Confidence Level. So any comparison flagged by
ISSTATS as significant is based on a Sidak Correction:
( ) ( )
( ) ( )
Where k is the number of groups.
Confidence Interval for Difference of Means
( ) ̅ ̅ ( ⁄ ) √ ( )
Where t is the student distribution.
Welch’s Test for equality of means
The test statistic, F*
, is defined as follows:
∑ ( ̅ ̃)
( )
∑
where
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F*
is the result of the test
k is the number of different groups to which the sampled cases belong
ni is the number of cases in the i-th group
∑ ∑
̃ ∑ ̅
( )
The test statistic has approximately a F-distribution with k-1 and ∑
degrees of freedom. Thus
the null hypothesis is rejected if ( )
Brown–Forsythe Test for equality of means
The test statistic, F*
, is defined as follows:
∑ ( ̅ ̅ )
∑ ( )
where
F*
is the result of the test
k is the number of different groups to which the sampled cases belong
ni is the number of cases in the i-th group (sample size of group i)
∑ is the total sample size
̅ ∑ ̅
is the overall mean.
The test statistic has approximately a F-distribution with k-1 and df degrees of freedom. Where df is
obtained with the Satterthwaite (1941) approximation as
∑
with
( )
∑ ( )
Thus the null hypothesis is rejected if ( )
Homoscedasticity Tests
Levene's Test
The test statistic, F, is defined as follows:
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∑ ( ̅ ̅ )
∑ ∑ ( ̅ )
where
F is the result of the test
k is the number of different groups to which the sampled cases belong
∑ is the total sample size
ni is the number of cases in the i-th group
Yij is the value of the measured variable for the j-th case from the i-th group
| ̅ | where ̅ is a mean of i-th group
̅ is the mean of all Zij
̅ is the mean of the Zij for group i.
The test statistic has a F-distribution with k-1 and N-k degrees of freedom. Thus the null hypothesis is
rejected if ( )
Source: http://en.wikipedia.org/wiki/Levene%27s_test
Brown–Forsythe Test for equality of variances
The test statistic, F, is defined as follows:
∑ ( ̅ ̅ )
∑ ∑ ( ̅ )
where
F is the result of the test
k is the number of different groups to which the sampled cases belong
∑ is the total sample size
ni is the number of cases in the i-th group
Yij is the value of the measured variable for the j-th case from the i-th group
| ̃ | where ̃ is a median of i-th group
̅ is the mean of all Zij
̅ is the mean of the Zij for group i.
The test statistic has a F-distribution with k-1 and N-k degrees of freedom. Thus the null hypothesis is
rejected if ( )
Source: http://en.wikipedia.org/wiki/Levene%27s_test
Bartlett's Test
Bartlett's test is used to test the null hypothesis, H0 that all k population variances are equal against the
alternative that at least two are different.
If there are k samples with size ni and sample variances S2
i then Bartlett's test statistic is
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( ) ( ) ∑ ( ) ( )
( )
(∑ ( ) )
where
∑ is the total sample size
∑ ( )
is the pooled estimate for the variance
The test statistic has approximately a chi-squared distribution with k-1 degrees of freedom. Thus the null
hypothesis is rejected if ( ).
Source: http://en.wikipedia.org/wiki/Bartlett%27s_test