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MWSUG 2008 Papers - Index

46 papers. Source: MWSUG (Midwest SAS Users Group) conference proceedings.

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1 A Comparison between Correlated ROC Curves Using SAS and DBM MRMC: A Case Study in Image Comparison Dachao Liu Receiver Operating Characteristic (ROC) analysis is often used in evaluation of a diagnostic test, related to decision making in cost/benefit analysis or medical practice by assessing the performance of binary classifiers. ROC curves in the diagnosis of multi-reader multi-case setting are correlated. There are ways to compare these correlated ROC curves. In this case study, there were 3 LCD imaging machines or display configurations, and 12 observers. Each observer read 180 cases with 60 being cancer cases and 120 normal benign cases. As a case study, this paper will discuss how the data was processed in SAS and how the comparison of ROC curves was made in both SAS and roc curve, proc import 10
2 A Multivariate Ranking Procedure to Assess Treatment Effects Alan Y Chiang,
Grace Li,
Ying Ding,
Ming-Dauh Wang		 In early phase clinical studies, it is often difficult to assess the effects of a set of biomarker variables and/or clinical endpoints when the individual variables do not appear to have statistically significant effects. To address this situation, we propose a method of U-scores applied to subsets of multivariate data. A SAS macro is developed and we illustrate the usefulness of this approach through simulations, considering various combinations of correlations and underlying distributions, and compare its statistical power with the existing tests: Hotelling's 2T and nonparametric rank-sum tests. Finally we apply this approach in a Phase I clinical study to help assess the treatment effects of an investigational drug on rheumatoid arthritis power analysis, clinical endpoint, clinical trial, correlation, wilcoxon test, macro definition, phase i 7
3 A Picture Is Worth A Thousand Data Points - Increase Understanding by Mapping Data Paul Ciarlariello If you find yourself faced with difficult to interpret data it may be helpful to "paint" the data onto a map. This paper presents information for transforming numeric data into visual data using SAS 9.1 and Microsoft EXCEL and MapPoint. There are also step by step instructions on how to automatically update your map for presentation on the SAS Information Delivery Portal sample, excel 10
4 A Sampler of What's New in Base SAS 9.2 Jason Secosky Coding with SAS is easier than ever with SAS 9.2. This paper highlights the top new features and performance improvements in DATA STEP, PROC SQL, and PROC SORT. Included are writing functions with DATA STEP syntax, improved performance when accessing an external database from PROC SQL, more intuitive and culturally acceptable sorting with PROC SORT, and several "Top 10" SASware Ballot items proc step, proc sql, study day, proc sort, put statement, where statement, proc format, proc fcmp, hash table, do loop, sas function, character variable, sas programmer, macro loop, infile statement, sas log, set statement, sas base, txt, user-defined format, work library, macro definition 15
5 A Toolbox of Technologies: Integrating SAS Applications with Flash Multimedia Incorporating HTML, and JavaScript Tony Goodman,
Terry Miller,
Jeff Agnew		 The Requirements Integration Process Improvement Team (RIPIT) at Wright-Patterson Air Force Base utilized SAS language and procedures, coupled with Adobe Flash multimedia technology, to develop the Forecast Accuracy Comparison Tool or FACT Plus. This tool enabled Air Force inventory managers to reduce the time and cost of forecasting supply and maintenance needs for Air Force spare parts by 99.5%. The tool automated disparate legacy processes and methodologies and incorporated them into a one-stop centralized application. By drawing upon various web programming techniques, a developer can seamlessly integrate a SAS program into an application that functions via the Internet. SAS programmers can utilize modern technologies to retool their applications to provide clients with more accessible, user-friendly web- based solutions demand forecasting, sas base 9
6 Advanced PROC REPORT: Getting Your Tables Connected Using Links Arthur L. Carpenter Gone are the days of strictly paper reports. Increasingly we are being asked to render our tables and reports using a variety of electronic file types that can be browsed and read using our computers. Besides the cost savings associated with the purchase of paper, paperless reports also minimize the costs associated with the printing and distribution of the reports. Another, often overlooked and potentially more important, side benefit of the paperless report is the increased availability of linked graphs, reports and tables. Navigation of the paper report is dependent on the table of contents, a strong index, and visual aids such as captions and footnotes; however in paperless reports we can easily jump from one table to its supporting and dependent tables with the click of a mouse. Easily that is if we have created the necessary links proc report, proc step, output delivery system, macro variable, macro loop, do loop, macro facility, footnote statement, ods pdf, macro definition, proc sql 21
7 Analytical Method Improvement Yields Dramatic Decrease in Variation for a Final Formulation Process Roger Norris sample, main effect 20
8 Blinding Sponsors for Open Label Studies: Challenges and Solutions Quan Jenny Zhou,
Shengyan Hong,
Zhengping Ma		 Although double blinding (blind treating physicians and patients) is the optimal approach to minimize the bias in clinical research, it's not always feasible to conduct double blind studies. For open label studies, it's often desirable to blind the study sponsors to reduce potential bias and increase credibility of trial results. However, open label studies usually create some challenges to blind sponsors. In this paper, we discuss some types of Case Report Form (CRF) data that could unblind sponsors and then propose two methods to mask the data in order to blind sponsors. We share some SAS macros to implement the proposed methods and also provide a real example for illustration open label, treatment arm, macro definition, adverse events 9
9 Chemical Informatics using JMP PowerMV and ChemModLab S. Stanley Young,
Thomas H. Burger,
Michael S. Lajiness,
Wolf-D. Ihlenfeldt		 proc hpforest, data science, outlier, regression analysis, training set 40
10 Classification of Breast Cancer Cells Using JMP Marie Gaudard This paper illustrates some of the features of JMP that support classification and data mining. We will utilize the Wisconsin Breast Cancer Diagnostic Data Set, a set of data used to classify breast lumps as malignant or benign based on the values of thirty potential predictors, obtained by measuring the nuclei of fluid removed using a fine needle aspirate. We begin by illustrating some visualization techniques that help build an understanding of the data set. After partitioning our data into a training set, a validation set, and a test set, we fit four models to the training data. These include a logistic model, a partition model, and two neural net models. We then compare the performance of these four models on the validation data set to choose one. The test set is used to assess the performance of this final model data cleaning, training set, logistic distribution, validation set, dataset, test set, logistic regression, correlation, sample, cross-validation, regression analysis, proc freq, data mining, feature selection, box plot, histogram, outlier, predictor variable, stepwise selection 34
11 Confidence Interval Calculation for Binomial Proportions Keith Dunnigan confidence interval, sample size, sample, confidence level, binomial distribution, sas output, datepart 12
12 Consensus NMF: A unified approach to two-sided testing of micro array data Paul Fogel,
S. Stanley Young		 scree plot, correlation, likelihood function, regression analysis, order by, anova, sample 41
13 Creating Macros for Survival Data in Oncology Study Jagannath Ghosh In this paper, we introduce some system functions and macros to create study specific survival variables for oncology clinical trials. We present five variables which show overall survival time, time to disease progression, duration of response, progression free survival and time to treatment failure fo r one particular study and then we will be using survival analysis based on overall survival and censoring information. Note that the study data will be fake data, however, it is still valuable for a real life data generated from a clinical trial. The purpose of his paper is to show the power and usefulness of SAS in clinical research (basically studies which require death and survival information, such as cancer and HIV). best response, overall survival, duration of response, randomization, survival analysis, clinical trial, pd 8
14 Creating Special Symbols in SAS Graph Shiqun (Stan) Li,
Wei Zhou		 This paper will present several techniques to embed special characters and special symbols into SAS graphs. The special symbols can be Greek letters, mathematical symbols, subscription, superscription, underline, and user designed symbols. The symbols can be created in the titles, footnotes, axis labels, or the graph area of a SAS graph. This presentation is prepared for an intermediate and advanced audience. Key Words: Special Symbol, Special Character, SAS/GRAPH, NOTE-MOVE, Subscription, Superscription, Underline, Customized Font sas output, proc step 6
15 Data Mining and Analysis to Lung Disease Data Guoxin Tang Objective: To examine the relationship between patient outcomes and conditions of the patients undergoing different treatments for lung disease. Method: SAS Enterprise Guide was used to obtain Lung disease data from the NIS (National Inpatient Sample) by using CATX and RXMATCH statements. We first concatenate all 15 columns of diagnosis codes into one text string using the CATX statement. The RXMATCH looks for the initial code of '162' to find all patients with a diagnosis of lung disease. The ICD 9 code of 162 means malignant neoplasm of trachea, bronchus, and lung. Kernel Density Estimation was used to examine the lung disease by Age, Length of Stay and Total Charges, which showed the relationships among these outcomes by using data visualization. Then we use SAS Text Miner to investigate relationships in co-morbid diagnoses. To investigate, we used SAS Text Miner to define clusters of diagnoses. Then we can inspect the results by defining a severity measure using text analysis sample, cluster analysis 9
16 Data Set Investigator - Automated Exception Reporting from an electronic data dictionary with %DSI() Matthew T. Karafa,
Julie Thornton		 Data cleaning and data sleuthing can be the most tedious and time consuming part of any analysis. As part of the data elicitation process we often collect metadata about valid values, and other data rules that can be used to quickly check for such problems. By using a small amount of this metadata about the data set\u2019s variables the provided macro, %DSI(), can produce a data exception report both by rule and by record ID which can be quickly turned back to the client for data correction. %DSI() takes a comma separated data file defining data "rules" with fields including variable name, type, valid values and ranges. These are then applied to the data set using internal macros that report the records and values that violate the given set of rules. These are then organized into a fairly simple, MSWord compliant HTML document, which can be returned to the client for action sample, data dictionary 4
17 Experimental Learning: Use of JMP Journal in Six Sigma Green and Black Belt Training Amurthur Ramamurthy,
Paul Cook		 central tendency, rmse, regression analysis, multiple regression, anova, mse, post hoc test 16
18 Exploring Efficient Ways to Collapse Variables Yubo Gao There are many situations where we would want to find the frequency distributions of discrete results. Usually, more than one method is available in SAS. With today's computer technology, data files under a million records are not a problem. But if the data file is much larger than that, such as those present in major credit card/retailers companies and university/hospitals, efficient methods should be sought in order to reduce resource utilization, such as time expended. Based on a frequency distribution question posted at the SAS-L community, this paper first reviewed the methods suggested by the SAS-L subscribers, and then proposed two other methods. Next, a performance comparison among these methods in terms of CPU time usage was made by solving an expanded example, the comparison showed that the ratios of CPU time usage between the slowest method and the fastest one is 12, and the result may serve as a benchmark when solving similar problems proc step, proc freq, data step, proc means, cpu time, frequency analysis, benchmarking, coalesce function 5
19 Find Out What You're Missing: Programming with SAS Enterprise Guide Chris Hemedinger In this paper, you can read about the productivity gains that you can enjoy when you add SAS Enterprise Guide to your SAS programming toolbox. You will see how to perform old tasks in a new way as well as how to accomplish some tasks that would have been very difficult-if not impo ssible-without the benefit of an integrated tool like SAS Enterprise Guide. Topics in this paper include: Working with projects and the process flow Getting the most from SAS tasks Flexing your power with project prompts Conditionally running portions of your project * Noting a few things that you cannot do sas enterprise guide, macro variable, excel, sas programmer, proc report, sas log, sas programming, dataset, proc step, macro definition 11
20 Getting Started with ODS Statistical Graphics in SAS 9.2 Robert N. Rodriguez ODS Statistical Graphics (or ODS Graphics for short) is major new functionality for creating statistical graphics that is available in a number of SAS software products, including SAS/ST A T , SAS/ETS , SAS/QC , and SAS/GRAPH . With the production release of ODS Graphics in SAS 9.2, over sixty statistical procedures have been modified to use this functionality , and they now produce graphs as automatically as they produce tables. In addition, new procedures in SAS/GRAPH use this functionality to produce plots for exploratory data analysis and for customized statistical displays. SAS/GRAPH is required for ODS Graphics functionality in SAS 9.2. This paper presents the essential information you need to get started with ODS Graphics in SAS 9.2. ODS Graphics is an extension of ODS (the Output Delivery System), which manages procedure output and lets you display it in a variety of destinations, such as HTML and RTF ods graphics, data science 21
21 Global Clinical Data Classification: A Discriminate Analysis Amurthur Ramamurthy,
Gordon Kapke,
Jodi Yoder		 discriminant analysis, data cleaning, proc princomp, prior probability, validation set, sample, laboratory data 16
22 How to Generate Dynamical and Flexible Codes in Clinical Trial Xianming (Steve) Zheng In clinical trial, we typically develop and move TFLs to production before data lock to ensure blinded condition. Consequently, there are many unknown information associated with this process, and we try our best to minimize occasions to recheck programs in production. Therefore, it requires many technical skills to achieve this goal, especially advanced macro skills. It is essential to read existing data as parameters, use those parameters as condition and pass to future codes. I will use two biggest trials in our company as example to demonstrate how to utilize those skills in real studies binary variable, proc step, proc phreg 3
23 Imputation of Categorical Missing Data: A comparison of Multivariate Normal and Multinomial Methods Holmes Finch,
Matt Margraf		 Procedures for the imputation of missing data have been developed for continuous data, using Markov Chain Monte Carlo techniques. This approach has been demonstrated to provide acceptable results for dichotomous data when other variables in the dataset are continuous, rather than categorical, and the resulting imputations are not rounded off so as to fit the scale of the original data. This approach has not been thoroughly studied for situations in which all variables in the dataset are categorical. A categorical analog to the standard multiple imputation method does exist, but it must typically be used with a very small set of variables due to estimation problems. Thus, if multiple imputation methods for continuous data can be demonstrated to perform acceptably for situations in which all variables are categorical, they may provide a useful alternative for many research situations imputation, missing value, estimate, nominal variable, multinomial distribution, multiple imputation, markov chain, likelihood function 12
24 Inverse Prediction Using SAS Software: A Clinical Application Jayawant N. Mandrekar,
Cristine Allmer		 An important application of regression methodology is in the area of prediction. Oftentimes investigat ors are interested in predicting a value of a response vari able (Y) based on the known value of the predictor variable (X). However, sometimes there is a need to predict a value of the predictor variable (X) based on the known value of the response variable (Y). In such situations, it is im proper to simply switch the roles of the response a nd predictor variables to get the desired predictions i.e., regr ess X on Y. A method that accounts for the underlying assumptions while estimating or predicting X from known Y is known as inverse prediction. This approach will be illustrated using the PROC REG, and PROC GPLOT procedures in SAS. The calculations for the 95% confidence limits for a predicted X from a known Y will also be presented. The macro and its application will be demonstrated using data from clinical / laboratory studies regression analysis, confidence interval, proc reg, predictor variable, response variable, scatter plot, sample, proc gplot 7
25 Let Me Look At It! Graphic Presentation of Any Numeric Variable Anastasiya M. Osborne Have you ever been asked to produce a high quality, management-friendly report in record time? Have you ever spent time typing ranges for PROC FORMAT to apply in tables or maps? During Congressional hearings, U.S. Department of Agriculture (USDA) often gets urgent requests to graphically represent politically-sensitive data. This paper presents a SAS macro that was developed to allow flexibility in choosing a dataset, a variable in question, and a number of groups for statistical analysis. The macro then produces the results in an EXCEL spreadsheet, and an ODS output. It also automatically creates a format for the variable that can be used in PROC GMAP to produce an impressive map. The macro reduces programming time by eliminating time-consuming tasks to analyze the variable and manually type ranges for PROC FORMAT proc step, proc format, macro variable, proc gmap, call symput, proc freq, macro facility, excel, data science, macro definition 11
26 Long-Term Value Modeling in the Automobile Industry Jeff Ames,
Cathy Hackett,
Bruce S. Lund		 Businesses often classify their customer base in terms of the customers' predicted long-term value (LTV). LTV may influence marketing strategies, particularly CRM and concern resolution. This paper describes an approach to LTV calculations in the automobile industry. The emphasis of this presentation is on one aspect of LTV which is the choice of "next new-vehicle segment". SAS code related to "next-segment" predictive modeling is outlined. The implementation of this predictive model within a SAS scoring platform is presented regression analysis, logistic distribution, logistic regression, predictor variable, proc logistic, proc step, data step, response variable, macro definition 6
27 Loop-Do-Loop Around Arrays Wendi L. Wright Have you ever noticed your DATA STEP repeats the same code over and over? And then thought … there must be a better way. Sure, you could use a macro, but macros can generate many lines of code. Arrays, on the other hand, can do the same job in only a few lines. Many SAS programmers avoid arrays thinking that they are difficult, but the truth is they are not only easy to use, but make your work easier. Arrays are SAS DATA STEP statements that allow iterative processing of variables and text. We will look at many examples, including 1) input and output of file s using arrays, 2) doing the same calculation on multiple variables, and 3) creating multiple records with one observation. This tutorial will present the basics of using array statements and demonstrate several examples of usage do loop, array, temporary array, data step, array bounds, character variable, if-then-else, conditional do loop, numeric variable, proc means, sas function, missing value, numeric functions 17
28 Mastectomy Versus Lumpectomy In Breast Cancer Treatment Beatrice Ugiliweneza Objective: To extract information and analyze the cost of mastectomy and lumpectomy as breast cancer treatments using SAS. Methods: The data used are from the National Inpatient Sample (NIS). It contains a stratified sample of all hospital patient visits from 37 participating states. First, we extract breast cancer cases among all the data and then focus on those treated by mastectomy and lumpectomy. Then, data analysis techniques are used to examine and compare these two major surgical treatments. We used linear models in SAS/STAT to examine the data, and also PROC GPLOT methods. Results: For the data used, the study shows that the cost of mastectomy treatment is lower than the cost of lumpectomy treatment. Moreover, the analysis shows that mastectomy is more used than lumpectomy. Conclusion: SAS is a good tool for statistical data analysis, data mining and data visualization. Further study will include claims data to investigate longitudinal patient outcomes scatter plot, proc means, proc freq, proc reg, sas enterprise guide, regression analysis, sample, data science, anova, proc step 17
29 New SAS Performance Optimizations to Enhance Your SAS Client and Solution Access to the Database Mike Whitcher The SQL procedure has been used for years as the way many SAS clients and solutions query for their data. Examine the new SQL performance optimizations that have been added to this bellwether procedure, optimizations designed to greatly expand query pass-through capability to databases and shorten your SAS client and solution query response times. Also, see the new SQL enhancements for use with SAS data sets. Whether you use SAS Web Report Studio, SAS Marketing Automation or other SAS clients or solutions, or still submit your SQL queries in batch, you owe it to yourself to see how you can make them run faster proc sql, proc step, put statement, where statement, dataset, record count, proc format, sas function, user-defined format, ansi sql, sas base, implicit sql pass-through, having, business intelligence, hash table, datepart, group by, sysdate9, time., sample, create table, macro variable 15
30 One-Step Change from Baseline Calculations, and Other DOW-Loop Tricks Nancy Brucken Change from baseline is a common measure of safety and/or efficacy in clinical trials. The traditional way of calculating changes from baseline in a vertically-structured dataset requires multiple DATA steps, and thus several passes through the data. This paper demonstrates how change from baseline calculations can be performed with a single pass through the data, through use of the Dorfman-Whitlock DO- (DOW-) Loop. It also looks at how the DOW-Loop can be implemented to transpose multiple variables onto a single record per BY statement in one pass through the input dataset, thus by-passing one limitation of PROC TRANSPOSE change from baseline, set statement, in=, proc transpose, proc step 8
31 P Charts for Improved Analysis Vin Kane sample, dataset, data science, sample size, calendar year, connection string, line chart 16
32 PROC REPORT: Compute Block Basics - Part I Tutorial Arthur L. Carpenter One of the unique features of the REPORT procedure is the Compute Block. Unlike most other SAS procedures, PROC REPORT has the ability to modify values within a column, to insert lines of text into the report, to create columns, and to control the content of a column. Through compute blocks it is possible to use a number of SAS language elements, many of which can otherwise only be used in the DATA STEP. While powerful, the compute block can also be complex and potentially confusing. This tutorial introduces basic compute block concepts, statements, and usages. It discusses a few of the issues that tend to cause folks consternation when first learning how to use the compute block in PROC REPORT. This paper is being presented in conjunction with the Hands-on Workshop PROC REPORT: Compute Block Basics -Part II Practicum (Paper 188-2008) proc report, proc step, class variable, put statement, proc calis 16
33 PROC REPORT: Compute Block Basics - Part II Practicum Arthur L. Carpenter One of the unique features of the REPORT procedure is the Compute Block. Unlike most other SAS procedures, PROC REPORT has the ability to modify values within a column, to insert lines of text into the report, to create columns, and to control the content of a column. Through compute blocks it is possible to use a number of SAS language elements, many of which can otherwise only be used in the DATA STEP. While powerful, the compute block can also be complex and potentially confusing. This tutorial introduces basic compute block concepts, statements, and usages. It discusses a few of the issues that tend to cause folks consternation when first learning how to use the compute block in PROC REPORT. This paper is being presented in conjunction with the Tutorial PROC REPORT: Compute Block Basics -Part I Tutorial (Paper 031-2008). Consult that paper for additional details proc report, proc step, class variable, put statement, binary variable 18
34 SAS Stat Studio: A Programming Environment for High-End Data Analysts Rick Wicklin bootstrap, time series analysis, scatter plot, series plot, proc princomp, sample 19
35 SAS Tips, Tricks and Techniques Kirk Paul Lafler The base-SAS System offers users the power of a comprehensive DATA STEP programming language, an assortment of powerful PROCs, a macro language that extends the capabilities of the SAS System, and user-friendly interfaces including the SAS Display Manager. This presentation highlights numerous SAS tips, tricks and techniques using a collection of proven code examples related to effectively using the SAS Display Manager and its many features; process DATA STEP statements to handle subroutines and code libraries; deliver output in a variety of formats; construct reusable code; troubleshoot and debug code; and an assortment of other topics proc univariate, proc step, options statement, proc sql, dataset, macro facility, macro variable, macro parameter, %macro parameters, sas log, dictionary tables, ods pdf, ods rtf, sas output, if-then-else, ods html, macro definition pdf 14
36 Scheduling College Classes Using Operations Research Techniques Barry E. King,
Terri Friel		 This paper outlines efforts taken at Butler University's College of Business Administration to construct semester class schedules using a mixed integer linear programming problem to develop a schedule of classes and an ASSIGNMENT procedure to assign faculty to the schedule. It also discusses our experiences with the effort and suggestions for improvement linear programming, binary variable 4
37 SIPOC and Recursive Partitioning: Powerful Tool Combination for Transactional Six Sigma Projects Amurthur Ramamurthy liquidity gap, sample 17
38 So, Who's Afraid of Nonlinear Regression? Heinz Plaumann Many technical and business colleagues shy away from maximizing the true value of decision-supporting data due to reluctance to analyze data with the most up-to-date, descriptive and predictive models. Sometimes, this entails the development and fitting of nonlinear models. This paper presents several examples from industry. The first uses SAS-JMP software to fit models derived to support our work in product development improving performance of professional level bowling balls. This was coupled with extensive use of experimental design and led us to strong structure-property relationships, including the "subjective" evaluation of the end product: Is this really a better bowling ball? A second example involves developing understanding in a spray application in agriculture where we applied models to rheological behavior and spray particle size distribution. Final examples involve analysis of market penetration data proc nlin, regression analysis, estimate, correlation 14
39 Survival Methods for Correlated Time-to-Event Data James Bena,
Shannon McIntyre		 The use of product-limit (Kaplan-Meier) estimation and Cox proportional hazards modeling is common when analyzing time- to-event data, especially in the presence of censoring. Presenting results from both methods provides the magnitude of loss within the levels of a given variable, and a relative measure of failure risk between the levels. However, since both above methods assume independence of the observations, correlated measurements require adjustment to avoid underestimation of the variance, and overestimation of the statistical significance. In this paper, we focus on the case of clustered results, where a single observed unit may have several unique observations. The variances of Kaplan-Meier estimates from PROC LIFETEST are adjusted for the clustering using Taylor-series approximation. The standard errors of estimated hazard ratios from Cox proportional hazards models fit using PROC TPHREG are altered using the sandwich estimator, effectively fitting a marginal model proc step, proc calis, hazard ratio, proc phreg, confidence interval, macro call, survival function 3
40 The Difference Between Predictive Modeling and Regression Patricia B. Cerrito Predictive modeling includes regression, both logistic and linear, depending upon the type of outcome variable. However, as the datasets are generally too large for a p-value to have meaning, predictive modeling uses other measures of model fit. Generally, too, there are enough observations so that the data can be partitioned into two or more datasets. The first subset is used to define (or train) the model. The second subset can be used in an iterative process to improve the model. The third subset is used to test the model for accuracy. The definition of "best" model needs to be considered as well. In a regression model, the "best" model is one that satisfies the criteria of uniform minimum variance unbiased estimator. In other words, it is only "best" in the class of unbiased estimators. As soon as the class of estimators is expanded, "best" no longer exists, and we must define the criteria that we will use to determine a "best" fit. There are several criteria to consider regression analysis, sample, proc hpsplit, type ii error, logistic regression, type i error, logistic distribution, predictor variable, response variable, data mining, roc curve, neural network, residual, correlation, likelihood function, data cleaning 19
41 The Effective Billet Heating Method for Ultimate Seamless Tube Size Control Nathan Abboud,
Michael Seifert		 In the competitive seamless tubing market a tight tolerance with a high yield can be achieved by starting with a uniformly heated billet. This paper presents a new method to control furnace temperature in order to produce billets with a uniform temperature distribution. The result is optimal control over tube size at no added cost elapsed time, response variable 5
42 The Over-Reliance on the Central Limit Theorem Patricia B. Cerrito The objective is to demonstrate the theoretical and practical implication of the central limit theorem. The theorem states that as n approaches infinity, the distribution of the sample mean approaches normality with mean equal to the population mean and variance equal to the population variance divided by n. However, as n approaches infinity, the variance of the mean approaches zero. In practice, the population variance is unknown, and so the sample variance is used to estimate the population distribution. In that case, we assume the format of a t-distribution, which requires the assumption that the population is itself normally distributed. In this presentation, we use data visualization to show some problems that can occur when assuming that n is sufficiently large to assume that the sample mean is normally distributed. In particular, we use PROC SURVEYSELECT to sample data from non-normal distributions to compare the distribution of the sample mean to that of the population mean sample, sample size, central limit theorem 6
43 The Plot Thickens from PLOT to GPLOT Wendi L. Wright This paper starts with a look at basic plotting using PROC PLOT. A dataset with the daily number of hits on three websites is used. The presentation continues with examples of how to create more informative and professional looking plots by adding titles, footnotes, and referenc e lines and changing the symbols, legends and axes. This is done in a step by step a pproach with examples of how the plot changes at each step. A look at PROC GPLOT is next using the same Web ex ample. The final plot created using PROC PLOT is compared with the same program run in the PR OC GPLOT procedure. Further improvements are made in the same step by step fashion using various SAS Graph statements. The first change is to the titles and tootnotes. Then, one at a time, changes are made to the symbols (SYMBOL statement), the legend (LEGEND statement), and the axes (AXIS statement) proc step, proc gplot, title statement 22
44 Tips and Tricks for Creating Multi-Sheet Microsoft Excel Workbooks the Easy Way with SAS Vincent DelGobbo Transferring SAS data and analytical results between SAS and Microsoft EXCEL can be difficult, especially when SAS is not installed on a Windows platform. This paper discusses using the new XML support in Base SAS-9 software to create multi-sheet Microsoft EXCEL workbooks (versions 2002 and later). You will learn step-by-step techniques for quickly and easily creating attractive multi-sheet EXCEL workbooks that contain your SAS output, and also tips and tricks for working with the ExcelXP ODS tagset. Most importantly, the techniques that are presented in this paper can be used regardless of the platform on which SAS software is installed. You can even use them on a mainframe! The use of SAS server technology is also discussed. Although the title is similar to previous papers by this author, this paper contains new and revised material not previously presented excel, xls, proc print, by group processing, sas output, sas base, proc step, proc template, title statement, output delivery system, ods xml, macro definition 19
45 Using Direct Standardization SAS Macro for a Valid Comparison in Observational Studies Daojun Mo,
Xia Li,
Alan Zimmermann		 Observational studies are usually imbalanced in the factors associated with the outcome measures. Simply presenting the descriptive results or the P values from an unadjusted between-group comparison could lead to a biased conclusion. Direct standardization is one of the methods for binary data that reveal the valid association between comparison groups. Direct standardization is often implemented in a spreadsheet by copying and pasting the data. This becomes tedious in a study that explores multiple outcome measures. We thus developed a SAS macro that is adaptable to many types of observational studies which consider binary outcome measures. Examples are given to demonstrate the concept of direct standardization, and how to use the macro response variable, phase iii, phase iv, in=, out=, binomial distribution, poisson distribution, clinical trial 11
46 Visualization and the Improvement of Anodized Parts Using JMP Philip J. Ramsey,
Marie A. Gaudard,
Mia L. Stephens		 standard deviation, dataset, histogram, fixed effects, main effect 42

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