DOCSLIB.ORG

Systems Manual 1I Overview

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Systems Manual 1I Overview System Overview General The Statistical Solutions System for Missing Data Analysis incorporates the latest advanced techniques for imputing missing values. Researchers are regularly confronted with the problem of missing data in data sets. According to the type of missing data in your data set, the Statistical Solutions System allows you to choose the most appropriate technique to apply to a subset of your data. In addition to resolving the problem of missing data, the system incorporates a wide range of statistical tests and techniques. This manual discusses the main statistical tests and techniques available as output from the system. It is not a complete reference guide to the system. About this Manual Chapter 1 Data Management Describes how to import a data file, specifying the attributes of a variable, transformations that can be performed on a variable, and defining design and longitudinal variables. Information about the Link Manager and its comprehensive set of tools for screening data is also given. Chapter 2 Descriptive Statistics Discusses Descriptive Statistics such as: Univariate Summary Statistics, Sample Size, Frequency and Proportion. Chapter 3 Regression Introduces Regression, discusses Simple Linear, and Multiple Regression techniques, and describes the available Output Options. Chapter 4 Tables (Frequency Analysis) Introduces Frequency Analysis and describes the Tables, Measures, and Tests output options, which are available to the user when performing an analysis. Chapter 5 t- Tests and Nonparametric Tests Describes Two-group, Paired, and One-Group t-Tests, and the Output Options available for each test type. Systems Manual 1i Overview Chapter 6 Analysis (ANOVA) Introduces the Analysis of Variance (ANOVA) method, describes the One-way ANOVA and Two-way ANOVA techniques, and describes the available Output Options. Chapter 7 Plots An overview of the plots available in the system, such as: Scatterplot, Histogram, Bar Chart and Normal Probability Plot. Chapter 8 Tutorial A tutorial with examples you can work through. Appendices Includes references, error messages and data set information. Index Index contents, subject matter, topics. 2 Systems Manual 1. Data Management SYSTEM PREFERENCES AND READING/SAVING DATA SPECIFYING VARIABLE ATTRIBUTES GROUPING VARIABLES TRANSFORMING VARIABLES DEFINING VARIABLES THE LINK MANAGER Introduction This chapter introduces you to setting preferences for your output options, importing data from other applications, the attributes of a variable, cutting and pasting variables and cases in the datasheet, transformations that can be performed on a variable, and defining design and longitudinal variables. Also there is an explanation of the Link Manager that comprises a powerful set of tools that you can use for screening data. System Preferences You can set preferences for the output options for an analysis by selecting the View menu System Preferences option in the Main window to display the window shown below: Reading/Saving Data To import a file into the system select Open from the pull-down File Menu. The Open window is displayed where the file to be opened can be selected. Systems Manual 3i Chapter 1: Data Management Supported File Types See the Readme.txt for the most up to-date list of supported file formats and also Appendix D: External File Formats. The system can read any of the following file formats: Datasheet Lotus 1-2-3 Worksheet Frequency Table Paradox File Multiple Datasheet Quattro Pro Worksheet BMDP New System - Datasheet SAS for Windows/OS2 BMDP New System - Frequency Table SAS for Unix, SAS JMP BMDP Port File SAS Transport File ASCII - Delimited S-PLUS File dBASE or compatible SPSS Portable File Excel Worksheet and Excel for Office 2000 SPSS Data File FoxPro Stata File Minitab versions 8-12 Statistica version 5 Gauss File SYSTAT File If you enter a non-system file type in the File Name datafield, you will be prompted to specify a system file type: After pressing the OK button, the Import File Format window is displayed: Select a file format and click OK. The system copies the selected file format into the datasheet. 4 Systems Manual Chapter 1: Data Management Import an ASCII File If the selected file type is ASCII the following window is displayed: Import/Export The Import/Export selection buttons allow you to specify whether the file you want to import contains Variable Names in the first record: Data (default) The system reads the first record as variable data or values. Data plus Labels The first record is read as Variable Names and all subsequent records as data. Columns Separated By The Columns separated by selection buttons allow you to choose the character that separates the variables within the record. You can choose among comma, space, or tab. The system defaults to space. Rows Separated By The Rows separated by selections buttons allow you to choose the end of record marker that separates records within a file. You can choose among carriage return <CR>, line feed <LF>, or both <CR><LF>. The system defaults to <CR><LF>. Surround character variable with The Surround character variable with selection buttons allow you to specify characters to be used as “surround” characters for your alphanumeric (character) variables. ASCII text files can have single quote, double quote, or no surround characters. The system defaults to none. Missing Value Symbol The Missing Value Symbol selection buttons allow you to specify the characters to be used in the datasheet to represent missing values. Only one missing value code is allowed when importing ASCII. You can use a space as a character, but only when commas or tabs separate the data. You can also type in up to seven characters using any printing characters. The system defaults to an asterisk. Systems Manual 5i Chapter 1: Data Management Number of Rows Per Case The Number of rows per case datafield allows you to indicate the number of rows per case. For example, if each case takes up three rows of data, enter a 3 in the box. The system defaults to 1 row per case. Each case should have the fixed number of rows as specified. Import Variable Attributes Window The Import Variable Attributes window is displayed when you want to import any file including ASCII files. It displays a list of the variables in the file to be imported with their associated Statistical Solutions System type. You can change their type here before the file is imported into the system. To change an imported variable type: 1. Import a file. 2. Highlight a variable(s) in the Variable information list, and select a new type from the Type datafield. 3. Click on OK when you are satisfied with all your choices. The system imports your file and displays it in a new datasheet. NOTE: Generally the system does not handle date variables. When a date variable is detected, the system forces this variable to represent the number of days since Jan 1, 1900. You may also choose to read date variables as character strings or as continuous variables 6 Systems Manual Chapter 1: Data Management Specifying Variable Attributes In general, you will want to name your own variables rather than using default names Var1, Var2, etc. Most likely, you will also prefer to specify the type of data in the variables. You can name variables and specify variable type, role and format through the Specify Variable Attributes window. To display the Specify Variable Attributes window, double click on the name of a variable in the datasheet. Alternatively, you can choose Variable Attributes from the Variables menu to display the Specify Variable Attributes window. The Specify Variable Window is a tabbed window that lets you specify the attributes specific to each variable in your datasheet. It also lets you set cutpoints, modify categories and copy attributes. The four tabs are: Basic Attributes Specifies basic variable attributes. Set Cutpoints Groups continuous or ordinal variables. Modify Categories Groups nominal or ordinal variables. Copy Attributes Copies attributes from one variable to another variable(s). Specify Basic Attributes You can use the Basic Attributes window to change from the default Variable Name to any name not already assigned to a variable in the same datasheet. Each variable must be assigned a type. The type you assign to a variable affects its availability in certain plots and analyses. A variable can be considered nominal, ordinal, continuous, integer or character. The default is continuous. The terminology used is comparable to the Stevens classification system (see Variable Type later in this section). The tabs on the window change according to the type of variable chosen. Systems Manual 7i Chapter 1: Data Management Role The Role of a variable can be: None, Grouping, Case Frequency, or Case Label. The default is None, which you should interpret as any. Format The Format section of the Basic Attributes window provides fields for specifying the format of the values in the variable when displayed in the datasheet. Alignment The Alignment field lets you choose among Left, Right, Center, and Decimal. Field Width The Field Width of a variable refers to the number of characters that fit into one cell of the datasheet. You may want to specify a field width larger than that required by the data in the variable when you have a long variable name. Scientific Notation You can specify whether or not your variables are to be displayed in Scientific Notation. Decimal Places You can specify the number of Decimal Places that are to be the values for each variable. Decimal Places has a default of 2, with a range from 0 to 8. Show Group Names Use the Show Group Names option to show group names rather than original values for nominal, ordinal, continuous and integer variables. Double Precision Double Precision is applicable to Continuous variables only.
Load more

Recommended publications
  • Exact Statistical Inferences for Functions of Parameters of the Log-Gamma Distribution
    UNLV Theses, Dissertations, Professional Papers, and Capstones 5-1-2015 Exact Statistical Inferences for Functions of Parameters of the Log-Gamma Distribution Joseph F. Mcdonald University of Nevada, Las Vegas Follow this and additional works at: https://digitalscholarship.unlv.edu/thesesdissertations Part of the Multivariate Analysis Commons, and the Probability Commons Repository Citation Mcdonald, Joseph F., "Exact Statistical Inferences for Functions of Parameters of the Log-Gamma Distribution" (2015). UNLV Theses, Dissertations, Professional Papers, and Capstones. 2384. http://dx.doi.org/10.34917/7645961 This Dissertation is protected by copyright and/or related rights. It has been brought to you by Digital Scholarship@UNLV with permission from the rights-holder(s). You are free to use this Dissertation in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/or on the work itself. This Dissertation has been accepted for inclusion in UNLV Theses, Dissertations, Professional Papers, and Capstones by an authorized administrator of Digital Scholarship@UNLV. For more information, please contact [email protected]. EXACT STATISTICAL INFERENCES FOR FUNCTIONS OF PARAMETERS OF THE LOG-GAMMA DISTRIBUTION by Joseph F. McDonald Bachelor of Science in Secondary Education University of Nevada, Las Vegas 1991 Master of Science in Mathematics University of Nevada, Las Vegas 1993 A dissertation submitted in partial fulfillment of the requirements for the Doctor of Philosophy - Mathematical Sciences Department of Mathematical Sciences College of Sciences The Graduate College University of Nevada, Las Vegas May 2015 We recommend the dissertation prepared under our supervision by Joseph F.
    [Show full text]
  • IBM SPSS Exact Tests
    IBM SPSS Exact Tests Cyrus R. Mehta and Nitin R. Patel Note: Before using this information and the product it supports, read the general information under Notices on page 213. This edition applies to IBM® SPSS® Exact Tests 21 and to all subsequent releases and modifications until otherwise indicated in new editions. Microsoft product screenshots reproduced with permission from Microsoft Corporation. Licensed Materials - Property of IBM © Copyright IBM Corp. 1989, 2012. U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Preface Exact Tests™ is a statistical package for analyzing continuous or categorical data by exact methods. The goal in Exact Tests is to enable you to make reliable inferences when your data are small, sparse, heavily tied, or unbalanced and the validity of the corresponding large sample theory is in doubt. This is achieved by computing exact p values for a very wide class of hypothesis tests, including one-, two-, and K- sample tests, tests for unordered and ordered categorical data, and tests for measures of asso- ciation. The statistical methodology underlying these exact tests is well established in the statistical literature and may be regarded as a natural generalization of Fisher’s ex- act test for the single 22× contingency table. It is fully explained in this user manual. The real challenge has been to make this methodology operational through software development. Historically, this has been a difficult task because the computational de- mands imposed by the exact methods are rather severe. We and our colleagues at the Harvard School of Public Health have worked on these computational problems for over a decade and have developed exact and Monte Carlo algorithms to solve them.
    [Show full text]
  • The NPAR1WAY Procedure This Document Is an Individual Chapter from SAS/STAT® 14.1 User’S Guide
    SAS/STAT® 14.1 User’s Guide The NPAR1WAY Procedure This document is an individual chapter from SAS/STAT® 14.1 User’s Guide. The correct bibliographic citation for this manual is as follows: SAS Institute Inc. 2015. SAS/STAT® 14.1 User’s Guide. Cary, NC: SAS Institute Inc. SAS/STAT® 14.1 User’s Guide Copyright © 2015, SAS Institute Inc., Cary, NC, USA All Rights Reserved. Produced in the United States of America. For a hard-copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. For a web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. The scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others’ rights is appreciated. U.S. Government License Rights; Restricted Rights: The Software and its documentation is commercial computer software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication, or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), DFAR 227.7202-3(a), and DFAR 227.7202-4, and, to the extent required under U.S.
    [Show full text]
  • Statistical Inferences for Functions of Parameters of Several Pareto and Exponential Populations with Application in Data Traffic
    UNLV Theses, Dissertations, Professional Papers, and Capstones 2009 Statistical inferences for functions of parameters of several pareto and exponential populations with application in data traffic Sumith Gunasekera University of Nevada Las Vegas Follow this and additional works at: https://digitalscholarship.unlv.edu/thesesdissertations Part of the Applied Mathematics Commons, Applied Statistics Commons, and the Statistical Methodology Commons Repository Citation Gunasekera, Sumith, "Statistical inferences for functions of parameters of several pareto and exponential populations with application in data traffic" (2009). UNLV Theses, Dissertations, Professional Papers, and Capstones. 46. http://dx.doi.org/10.34917/1363595 This Dissertation is protected by copyright and/or related rights. It has been brought to you by Digital Scholarship@UNLV with permission from the rights-holder(s). You are free to use this Dissertation in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s) directly, unless additional rights are indicated by a Creative Commons license in the record and/or on the work itself. This Dissertation has been accepted for inclusion in UNLV Theses, Dissertations, Professional Papers, and Capstones by an authorized administrator of Digital Scholarship@UNLV. For more information, please contact [email protected]. STATISTICAL INFERENCES FOR FUNCTIONS OF PARAMETERS OF SEVERAL PARETO AND EXPONENTIAL POPULATIONS WITH APPLICATIONS IN DATA TRAFFIC by Sumith Gunasekera Bachelor of Science University of Colombo, Sri Lanka 1995 A dissertation submitted in partial fulfillment of the requirements for the Doctor of Philosophy Degree in Mathematical Sciences Department of Mathematical Sciences College of Sciences Graduate College University of Nevada, Las Vegas August 2009 .
    [Show full text]
  • PASW Exact Tests
    Exact Tests ™ Cyrus R. Mehta and Nitin R. Patel For more information about SPSS® software products, please visit our WWW site at http://www.spss.com or contact Marketing Department SPSS Inc. 233 South Wacker Drive, 11th Floor Chicago, IL 60606-6307 Tel: (312) 651-3000 Fax: (312) 651-3668 SPSS is a registered trademark. PASW is a registered trademark of SPSS Inc. The SOFTWARE and documentation are provided with RESTRICTED RIGHTS. Use, duplication, or disclosure by the Government is subject to restrictions as set forth in subdivision (c)(1)(ii) of The Rights in Technical Data and Computer Software clause at 52.227-7013. Contractor/manufacturer is SPSS Inc., 233 South Wacker Drive, 11th Floor, Chicago, IL, 60606-6307. General notice: Other product names mentioned herein are used for identification purposes only and may be trademarks of their respective companies. TableLook is a trademark of SPSS Inc. Windows is a registered trademark of Microsoft Corporation. Printed in the United States of America. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher. Preface Exact Tests is a statistical package for analyzing continuous or categorical data by ex- act methods. The goal in Exact Tests is to enable you to make reliable inferences when your data are small, sparse, heavily tied, or unbalanced and the validity of the corre- sponding large sample theory is in doubt. This is achieved by computing exact p values for a very wide class of hypothesis tests, including one-, two-, and K- sample tests, tests for unordered and ordered categorical data, and tests for measures of association.
    [Show full text]
  • Exact Nonparametric Binary Choice and Ordinal Regression Analysis"
    Exact Nonparametric Binary Choice and Ordinal Regression Analysis Karl H. Schlagy February 15, 2014 Abstract We provide methods for investigating the relationship between an attribute and an ordinal outcome, as in a binary choice model. We do not make additional assumptions on the errors as in the (ordinal) logit or probit models and do not invoke asymptotic theory. These tests are exact as their type I error probability can be bounded above by their nominal level in the data set that is being investigated. Bounds on type II error probabilities are provided. Methods extend to duration and survival analysis with right censoring. Several data applications are given. Keywords: exact, probit, logit, stochastic inequality, latent variable, single index model, ordinal, nonparametric, survival analysis. JEL classi…cation: C14, C20. 1 Introduction Binary choice models in the form of logistic or probit regressions are very popular for understanding how attributes in‡uence outcomes, yet they have grave drawbacks when coming to inference. When …nding signi…cant evidence of a coe¢ cient there may be several reasons why the corresponding null hypothesis has been rejected. One would wish that this reveals a signi…cant evidence of a positive relationship between this attribute and the outcome. However there are also three other reasons that can lead to a rejection of the null hypothesis. (i) Errors may not distributed as postulated by the underlying model. (ii) The formulae used to compute the corresponding p value We wish to thank Francesca Solmi whose work motivated this project, Heiko Rachinger for valuable comments and Robin Ristl for programming the software used to analyze the data.
    [Show full text]
  • Exact Hypothesis Testing Without Assumptions & New and Old
    Exact Hypothesis Testing without Assumptions - New and Old Results not only for Experimental Game Theory1 Karl H. Schlag October 11, 2013 1The author would like to thank Caterina Calsamiglia, Patrick Eozenou and Joel van der Weele for comments on exposition of an earlier version. 1 Contents 1 Introduction 1 2 When are T Tests Exact? 3 3 Two Independent Samples 4 3.1 Preliminaries ............................... 4 3.1.1 Documentation . 8 3.1.2 Accepting the Null Hypothesis . 9 3.1.3 Side Remarks on the P Value . 10 3.1.4 Evaluating Tests . 12 3.1.5 Ex-Post Power Analysis . 15 3.1.6 Con…dence Intervals . 16 3.1.7 Noninferiority Tests and Reversing a Test . 18 3.1.8 Estimation and Hypothesis Testing . 18 3.1.9 Substantial Signi…cance . 19 3.2 Binary-Valued Distributions . 19 3.2.1 One-Sided Hypotheses . 19 3.2.2 Tests of Equality . 24 3.3 Multi-Valued Distributions . 25 3.3.1 Identity of Two Distributions . 25 3.3.2 Stochastic Dominance . 28 3.3.3 Medians .............................. 29 3.3.4 Stochastic Inequalities and Categorical Data . 30 3.3.5 Means ............................... 32 3.3.6 Variances ............................. 36 4 Matched Pairs 36 4.1 Binary-Valued Distributions . 36 4.1.1 Comparing Probabilities . 36 4.1.2 Correlation . 37 4.2 Multi-Valued Distributions . 38 4.2.1 Identity of Two Marginal Distributions . 38 2 4.2.2 Independence of Two Distributions . 39 4.2.3 Stochastic Inequalities and Ordinal Data . 39 4.2.4 Means ............................... 40 4.2.5 Covariance and Correlation .
    [Show full text]
  • PROC FREQ Statement
    SAS/STAT® 13.1 User’s Guide The FREQ Procedure This document is an individual chapter from SAS/STAT® 13.1 User’s Guide. The correct bibliographic citation for the complete manual is as follows: SAS Institute Inc. 2013. SAS/STAT® 13.1 User’s Guide. Cary, NC: SAS Institute Inc. Copyright © 2013, SAS Institute Inc., Cary, NC, USA All rights reserved. Produced in the United States of America. For a hard-copy book: No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, or otherwise, without the prior written permission of the publisher, SAS Institute Inc. For a web download or e-book: Your use of this publication shall be governed by the terms established by the vendor at the time you acquire this publication. The scanning, uploading, and distribution of this book via the Internet or any other means without the permission of the publisher is illegal and punishable by law. Please purchase only authorized electronic editions and do not participate in or encourage electronic piracy of copyrighted materials. Your support of others’ rights is appreciated. U.S. Government License Rights; Restricted Rights: The Software and its documentation is commercial computer software developed at private expense and is provided with RESTRICTED RIGHTS to the United States Government. Use, duplication or disclosure of the Software by the United States Government is subject to the license terms of this Agreement pursuant to, as applicable, FAR 12.212, DFAR 227.7202-1(a), DFAR 227.7202-3(a) and DFAR 227.7202-4 and, to the extent required under U.S.
    [Show full text]
  • On Estimating the Reliability in a Multicomponent System Based on Progressively-Censored Data from Chen Distribution
    ON ESTIMATING THE RELIABILITY IN A MULTICOMPONENT SYSTEM BASED ON PROGRESSIVELY-CENSORED DATA FROM CHEN DISTRIBUTION By Oluwakorede Daniel Ajumobi Sumith Gunasekera Lakmali Weerasena Associate Professor of Statistics Assistant Professor of Mathematics (Committee Chair) (Committee Member) Ossama Saleh Hong Qin Professor of Mathematics Associate Professor of Computer Science (Committee Member) (Committee Member) ON ESTIMATING THE RELIABILITY IN A MULTICOMPONENT SYSTEM BASED ON PROGRESSIVELY-CENSORED DATA FROM CHEN DISTRIBUTION By Oluwakorede Daniel Ajumobi A Thesis Submitted to the Faculty of the University of Tennessee at Chattanooga in Partial Fulfillment of the Requirements of the Degree of Master of Science: Mathematics The University of Tennessee at Chattanooga, Chattanooga, Tennessee May 2019 ii Copyright c 2019 Oluwakorede Daniel Ajumobi All rights reserved iii ABSTRACT This research deals with classical, Bayesian, and generalized estimation of stress-strength reliability parameter, Rs;k = Pr(at least s of (X1;X2;:::;Xk) exceed Y) = Pr(Xk−s+1:k > Y) of an s-out-of-k : G multicomponent system, based on progressively type-II right-censored samples with random removals when stress and strength are two independent Chen random variables. Under squared-error and LINEX loss functions, Bayes estimates are developed by using Lindley’s approximation and Markov Chain Monte Carlo method. Generalized estimates are developed using generalized variable method while classical estimates - the maximum likelihood estimators, their asymptotic distributions, asymptotic confidence intervals, bootstrap-based confidence intervals - are also developed. A simulation study and a real-world data analysis are provided to illustrate the proposed procedures. The size of the test, adjusted and unadjusted power of the test, coverage probability and expected lengths of the confidence intervals, and biases of the estimators are also computed, compared and contrasted.
    [Show full text]
  • IBM SPSS Exact Tests
    IBM SPSS Exact Tests Cyrus R. Mehta and Nitin R. Patel Note: Before using this information and the product it supports, read the general information under Notices on page 213. This edition applies to IBM® SPSS® Exact Tests 22 and to all subsequent releases and modifications until otherwise indicated in new editions. Microsoft product screenshots reproduced with permission from Microsoft Corporation. Licensed Materials - Property of IBM © Copyright IBM Corp. 1989, 2013. U.S. Government Users Restricted Rights - Use, duplication or disclosure restricted by GSA ADP Schedule Contract with IBM Corp. Preface Exact Tests™ is a statistical package for analyzing continuous or categorical data by exact methods. The goal in Exact Tests is to enable you to make reliable inferences when your data are small, sparse, heavily tied, or unbalanced and the validity of the corresponding large sample theory is in doubt. This is achieved by computing exact p values for a very wide class of hypothesis tests, including one-, two-, and K- sample tests, tests for unordered and ordered categorical data, and tests for measures of asso- ciation. The statistical methodology underlying these exact tests is well established in the statistical literature and may be regarded as a natural generalization of Fisher’s ex- act test for the single 22× contingency table. It is fully explained in this user manual. The real challenge has been to make this methodology operational through software development. Historically, this has been a difficult task because the computational de- mands imposed by the exact methods are rather severe. We and our colleagues at the Harvard School of Public Health have worked on these computational problems for over a decade and have developed exact and Monte Carlo algorithms to solve them.
    [Show full text]
  • Sigmaxl Version 7.0 Workbook
    SigmaXL® Version 7.0 Workbook Contact Information: Technical Support: 1-866-475-2124 (Toll Free in North America) or 1-519-579-5877 Sales: 1-888-SigmaXL (888-744-6295) E-mail: [email protected] Web: www.SigmaXL.com Copyright © 2004-2014, SigmaXL Inc. Published September, 2014 Table of Contents SigmaXL® Feature List Summary, What’s New in Version 7.0, Installation Notes, System Requirements and Getting Help ............................................................................................ 1 SigmaXL Version 7.0 Feature List Summary ................................................................................... 3 What’s New in Version 7.0 ............................................................................................................. 7 Installation Notes ......................................................................................................................... 11 Activating SigmaXL ................................................................................................................. 15 Error Messages ...................................................................................................................... 18 Installation Notes for Excel 2007/2010/2013 .............................................................................. 19 Installation Notes for Excel Mac 2011 ......................................................................................... 21 SigmaXL® Defaults and Menu Options ........................................................................................
    [Show full text]