The new and highly anticipated SAS^® Output Delivery System (ODS) destination for Microsoft Excel is finally here! Available as a production feature in the third maintenance release of SAS^® 9.4 (TS1M3), this new destination generates native Excel (XLSX) files that are compatible with Microsoft Office 2010 or later. This paper is written for anyone, from entry-level programmers to business analysts, who uses the SAS^® System and Microsoft Excel to create reports. The discussion covers features and benefits of the new Excel destination, differences between the Excel destination and the older ExcelXP tagset, and functionality that exists in the ExcelXP tagset that is not available in the Excel destination. These topics are all illustrated with meaningful examples. The paper also explains how you can bridge the gap that exists as a result of differences in the functionality between the destination and the tagset. In addition, the discussion outlines when it is beneficial for you to use the Excel destination versus the ExcelXP tagset, and vice versa. After reading this paper, you should be able to make an informed decision about which tool best meets your needs.

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Thanks to advances in technologies that make data more readily available, sports analytics is an increasingly popular topic. A majority of sports analyses use advanced statistics and metrics to achieve their goal, whether it be prediction or explanation. Few studies include public opinion data. Last year's highly anticipated NCAA College Football Championship game between Ohio State and Oregon broke ESPN and cable television records with an astounding 33.4 million viewers. Given the popularity of college football, especially now with the inclusion of the new playoff system, people seem to be paying more attention than ever to the game. ESPN provides fans with College Pick'em, which gives them a way to compete with their friends and colleagues on a weekly basis, for free, to see who can correctly pick the winners of college football games. Each week, 10 close matchups are selected, and users must select which team they think will win the game and rank those picks on a scale of 1 (lowest) to 10 (highest), according to their confidence level. For each team, the percentage of users who picked that team and the national average confidence are shown. Ideally, one could use these variables in conjunction with other information to enhance one's own predictions. The analysis described in this session explores the relationship between public opinion data from College Pick'em and the corresponding game outcomes by using visualizations and statistical models implemented by various SAS^® products.

SAS^® Grid Manager, as well as other grid computing technologies, have a set of great capabilities that we, IT professionals, love to have in our systems. This technology increases high availability, allows parallel processing, facilitates increasing demand by scale out, and offers other features that make life better for those managing and using these environments. However, even when business users take advantage of these features, they are more concerned about the business part of the problem. Most of the time business groups hold the budgets and are key stakeholders for any SAS Grid Manager project. Therefore, it is crucial to demonstrate to business users how they will benefit from the new technologies, how the features will improve their daily operations, help them be more efficient and productive, and help them achieve better results. This paper guides you through a process to create a strong and persuasive business plan that translates the technology features from SAS Grid Manager to business benefits.

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As SAS^® programmers, we often develop listings, graphs, and reports that need to be delivered frequently to our customers. We might decide to manually run the program every time we get a request, or we might easily schedule an automatic task to send a report at a specific date and time. Both scenarios have some disadvantages. If the report is manual, we have to find and run the program every time someone request an updated version of the output. It takes some time and it is not the most interesting part of the job. If we schedule an automatic task in Windows, we still sometimes get an email from the customers because they need the report immediately. That means that we have to find and run the program for them. This paper explains how we developed an on-demand report platform using SAS^® Enterprise Guide^®, SAS^® Web Application Server, and stored processes. We had developed many reports for different customer groups, and we were getting more and more emails from them asking for updated versions of their reports. We felt we were not using our time wisely and decided to create an infrastructure where users could easily run their programs through a web interface. The tool that we created enables SAS programmers to easily release on-demand web reports with minimum programming. It has web interfaces developed using stored processes for the administrative tasks, and it also automatically customizes the front end based on the user who connects to the website. One of the challenges of the project was that certain reports had to be available to a specific group of users only.

Ensemble models are a popular class of methods for combining the posterior probabilities of two or more predictive models in order to create a potentially more accurate model. This paper summarizes the theoretical background of recent ensemble techniques and presents examples of real-world applications. Examples of these novel ensemble techniques include weighted combinations (such as stacking or blending) of predicted probabilities in addition to averaging or voting approaches that combine the posterior probabilities by adding one model at a time. Fit statistics across several data sets are compared to highlight the advantages and disadvantages of each method, and process flow diagrams that can be used as ensemble templates for SAS^® Enterprise Miner™ are presented.

As Data Management professionals, you have to comply with new regulations and controls. One such regulation is Basel Committee on Banking Supervision (BCBS) 239. To respond to these new demands, you have to put processes and methods in place to automate metadata collection and analysis, and to provide rigorous documentation around your data flows. You also have to deal with many aspects of data management including data access, data manipulation (ETL and other), data quality, data usage, and data consumption, often from a variety of toolsets that are not necessarily from a single vendor. This paper shows you how to use SAS^® technologies to support data governance requirements, including third party metadata collection and data monitoring. It highlights best practices such as implementing a business glossary and establishing controls for monitoring data. Attend this session to become familiar with the SAS tools used to meet the new requirements and to implement a more managed environment.

Do you create complex reports using PROC REPORT? Are you confused by the COMPUTE BLOCK feature of PROC REPORT? Are you even aware of it? Maybe you already produce reports using PROC REPORT, but suddenly your boss needs you to modify some of the values in one or more of the columns. Maybe your boss needs to see the values of some rows in boldface and others highlighted in a stylish yellow. Perhaps one of the columns in the report needs to display a variety of fashionable formats (some with varying decimal places and some without any decimals). Maybe the customer needs to see a footnote in specific cells of the report. Well, if this sounds familiar then come take a look at the COMPUTE BLOCK of PROC REPORT. This paper shows a few tips and tricks of using the COMPUTE DEFINE block with conditional IF/THEN logic to make your reports stylish and fashionable. The COMPUTE BLOCK allows you to use data DATA step code within PROC REPORT to provide customization and style to your reports. We'll see how the Census Bureau produces a stylish demographic profile for customers of its Special Census program using PROC REPORT with the COMPUTE BLOCK. The paper focuses on how to use the COMPUTE BLOCK to create this stylish Special Census profile. The paper shows quick tips and simple code to handle multiple formats within the same column, make the values in the Total rows boldface, trafficlighting, and how to add footnotes to any cell based on the column or row. The Special Census profile report is an Excel table created with ODS tagsets.ExcelXP that is stylish and fashionable, thanks in part to the COMPUTE BLOCK.

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With millions of users and peak traffic of thousands of requests a second for complex user-specific data, fantasy football offers many data design challenges. Not only is there a high volume of data transfers, but the data is also dynamic and of diverse types. We need to process data originating on the stadium playing field and user devices and make it available to a variety of different services. The system must be nimble and must produce accurate and timely responses. This talk discusses the strategies employed by and lessons learned from one of the primary architects of the National Football League's fantasy football system. We explore general data design considerations with specific examples of high availability, data integrity, system performance, and some other random buzzwords. We review some of the common pitfalls facing large-scale databases and the systems using them. And we cover some of the tips and best practices to take your data-driven applications from fantasy to reality.

If your organization already deploys one or more software solutions via Amazon Web Services (AWS), you know the value of the public cloud. AWS provides a scalable public cloud with a global footprint, allowing users access to enterprise software solutions anywhere at any time. Although SAS^® began long before AWS was even imagined, many loyal organizations driven by SAS are moving their local SAS analytics into the public AWS cloud, alongside other software hosted by AWS. SAS^® Solutions OnDemand has assisted organizations in this transition. In this paper, we describe how we extended our enterprise hosting business to AWS. We describe the open source automation framework from which SAS Soultions onDemand built our automation stack, which simplified the process of migrating a SAS implementation. We'll provide the technical details of our automation and network footprint, a discussion of the technologies we chose along the way, and a list of lessons learned.

Project management is a hot topic across many industries, and there are multiple commercial software applications for managing projects available. The reality, however, is that the majority of project management software is not applicable for daily usage. SAS^® has a solution for this issue that can be used for managing projects graphically in real time. This paper introduces a new paradigm for project management using the SAS^® Graph Template Language (GTL). SAS clients, in real time, can use GTL to visualize resource assignments, task plans, delivery tracking, and project status across multiple project levels for more efficient project management.

Have you ever wondered how to get the most from Web 2.0 technologies in order to visualize SAS^® data? How to make those graphs dynamic, so that users can explore the data in a controlled way, without needing prior knowledge of SAS products or data science? Wonder no more! In this session, you learn how to turn basic sashelp.stocks data into a snazzy HighCharts stock chart in which a user can review any time period, zoom in and out, and export the graph as an image. All of these features with only two DATA steps and one SORT procedure, for 57 lines of SAS code.

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Microsoft Visual Basic Scripting Edition (VBScript) and SAS^® software are each powerful tools in their own right. These two technologies can be combined so that SAS code can call a VBScript program or vice versa. This gives a programmer the ability to automate SAS tasks; traverse the file system; send emails programmatically via Microsoft Outlook or SMTP; manipulate Microsoft Word, Microsoft Excel, and Microsoft PowerPoint files; get web data; and more. This paper presents example code to demonstrate each of these capabilities.

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Considering the fact that SAS^® Grid Manager is becoming more and more popular, it is important to fulfill the user's need for a successful migration to a SAS^® Grid environment. This paper focuses on key requirements and common issues for new SAS Grid users, especially if they are coming from a traditional environment. This paper describes a few common requirements like the need for a current working directory, the change of file system navigation in SAS^® Enterprise Guide^® with user-given location, getting job execution summary email, and so on. The GRIDWORK directory has been introduced in SAS Grid Manager, which is a bit different from the traditional SAS WORK location. This paper explains how you can use the GRIDWORK location in a more user-friendly way. Sometimes users experience data set size differences during grid migration. A few important reasons for data set size difference are demonstrated. We also demonstrate how to create new custom scripts as per business needs and how to incorporate them with SAS Grid Manager engine.

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From stock price histories to hospital stay records, analysis of time series data often requires use of lagged (and occasionally lead) values of one or more analysis variables. For the SAS^® user, the central operational task is typically getting lagged (lead) values for each time point in the data set. While SAS has long provided a LAG function, it has no analogous lead function--an especially significant problem in the case of large data series. This paper reviews the LAG function, in particular the powerful, but non-intuitive implications of its queue-oriented basis. The paper demonstrates efficient ways to generate leads with the same flexibility as the LAG function, but without the common and expensive recourse to data re-sorting. It also shows how to dynamically generate leads and lags through use of the hash object.

For SAS^® Enterprise Guide^® users, sometimes macro variables and their values need to be brought over to the local workspace from the server, especially when multiple data sets or outputs need to be written to separate files in a local drive. Manually retyping the macro variables and their values in the local workspace after they have been created on the server workspace would be time-consuming and error-prone, especially when we have quite a number of macro variables and values to bring over. Instead, this task can be achieved in an efficient manner by using dictionary tables and the CALL SYMPUT routine, as illustrated in more detail below. The same approach can also be used to bring macro variables and their values from the local to the server workspace.

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Business problems have become more stratified and micro-segmentation is driving the need for mass-scale, automated machine learning solutions. Additionally, deployment environments include diverse ecosystems, requiring hundreds of models to be built and deployed quickly via web services to operational systems. The new SAS^® automated modeling tool allows you to build and test hundreds of models across all of the segments in your data, testing a wide variety of machine learning techniques. The tool is completely customizable, allowing you transparent access to all modeling results. This paper shows you how to identify hundreds of champion models using SAS^® Factory Miner, while generating scoring web services using SAS^® Decision Manager. Immediate benefits include efficient model deployments, which allow you to spend more time generating insights that might reveal new opportunities, expose hidden risks, and fuel smarter, well-timed decisions.

You've heard all the talk about SAS^® Visual Analytics--but maybe you are still confused about how the product would work in your SAS^® environment. Many customers have the same points of confusion about what they need to do with their data, how to get data into the product, how SAS Visual Analytics would benefit them, and even should they be considering Hadoop or the cloud. In this paper, we cover the questions we are asked most often about implementation, administration, and usage of SAS Visual Analytics.

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Inspired by Christianna William's paper on transitioning to PROC SQL from the DATA step, this paper aims to help SQL programmers transition to SAS^® by using PROC SQL. SAS adapted the Structured Query Language (SQL) by means of PROC SQL back with SAS^®6. PROC SQL syntax closely resembles SQL. However, there are some SQL features that are not available in SAS. Throughout this paper, we outline common SQL tasks and how they might differ in PROC SQL. We also introduce useful SAS features that are not available in SQL. Topics covered are appropriate for novice SAS users.

SAS^® software provides many DATA step functions that search and extract patterns from a character string, such as SUBSTR, SCAN, INDEX, TRANWRD, etc. Using these functions to perform pattern matching often requires you to use many function calls to match a character position. However, using the Perl regular expression (PRX) functions or routines in the DATA step improves pattern-matching tasks by reducing the number of function calls and making the program easier to maintain. This talk, in addition to discussing the syntax of Perl regular expressions, demonstrates many real-world applications.

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In a data warehousing system, change data capture (CDC) plays an important part not just in making the data warehouse (DWH) aware of the change but also in providing a means of flowing the change to the DWH marts and reporting tables so that we see the current and latest version of the truth. This and slowly changing dimensions (SCD) create a cycle that runs the DWH and provides valuable insights in the history and for the decision-making future. What if the source has no CDC? It would be an ETL nightmare to identify the exact change and report the absolute truth. If these two processes can be combined into a single process where just one single transform does both jobs of identifying the change and applying the change to the DWH, then we can save significant processing times and value resources of the system. Hence, I came up with a hybrid SCD with CDC approach for this. My paper focuses on sources that DO NOT have CDC in their sources and need to perform SCD Type 2 on such records without worrying about data duplications and increased processing times.

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With advances in technology, the world of sports is now offering rich data sets that are of interest to statisticians. This talk concerns some research problems in various sports that are based on large data sets. In baseball, PITCHf/x data is used to help quantify the quality of pitches. From this, questions about pitcher evaluation and effectiveness are addressed. In cricket, match commentaries are parsed to yield ball-by-ball data in order to assist in building a match simulator. The simulator can then be used to investigate optimal lineups, player evaluation, and the assessment of fielding.

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Our company Veikkaus is a state-owned gambling and lottery company in Finland that has a national legalized monopoly for gambling. All the profit we make goes back to Finnish society (for art, sports, science, and culture), and this is done by our government. In addition to the government's requirements of profit, the state (Finland) also requires us to handle the adverse social aspects of gaming, such as problem gambling. The challenge in our business is to balance between these two factors. For the purposes of problem gambling, we have used SAS^® tools to create a responsible gaming tool, called VasA, based on a logistic regression model. The name VasA is derived from the Finnish words for 'Responsible Customership.' The model identifies problem gamblers from our customer database using the data from identified gaming, money transfers, web behavior, and customer data. The variables that were used in the model are based on the theory behind the problem gambling. Our actions for problem gambling include, for example, different CRM and personalization of a customer's website in our web service. There were several companies who provided responsible gambling tools as such for us to buy, but we wanted to create our own for two reasons. Firstly, we wanted it to include our whole customer database, meaning all our customers and not just those customers who wanted to take part in it. These other tools normally include only customers who want to take part. The other reason was that we saved a ridiculous amount of money by doing it by ourselves compared to having to buy one. During this process, SAS played a big role, from gathering the data to the construction of the tool, and from modeling to creating the VasA variables, then on to the database, and finally to the analyses and reporting.

Mobile devices are an integral part of a business professional's life. These mobile devices are getting increasingly powerful in terms of processor speeds and memory capabilities. Business users can benefit from a more analytical visualization of the data along with their business context. The new SAS^® Mobile BI contains many enhancements that facilitate the use of SAS^® Analytics in the newest version of SAS^® Visual Analytics. This paper demonstrates how to use the new analytical visualization that has been added to SAS Mobile BI from SAS Visual Analytics, for a richer and more insightful experience for business professionals on the go.

Revolution Analytics reports more than two million R users worldwide. SAS^® has the capability to use R code, but users have discovered a slight learning curve to performing certain basic functions such as getting data from the web. R is a functional programming language while SAS is a procedural programming language. These differences create difficulties when first making the switch from programming in R to programming in SAS. However, SAS/IML^® software enables integration between the two languages by enabling users to write R code directly into SAS/IML. This paper details the process of using the SAS/IML command Submit /R and the R package XML to get data from the web into SAS/IML. The project uses public basketball data for each of the 30 NBA teams over the past 35 years, taken directly from Basketball-Reference.com. The data was retrieved from 66 individual web pages, cleaned using R functions, and compiled into a final data set composed of 48 variables and 895 records. The seamless compatibility between SAS and R provide an opportunity to use R code in SAS for robust modeling. The resulting analysis provides a clear and concise approach for those interested in pursuing sports analytics.

For marketers who are responsible for identifying the best customer to target in a campaign, it is often daunting to determine which media channel, offer, or campaign program is the one the customer is more apt to respond to, and therefore, is more likely to increase revenue. This presentation examines the components of designing campaigns to identify promotable segments of customers and to target the optimal customers using SAS^® Marketing Automation integrated with SAS^® Marketing Optimization.

Specifying colors based on group value is a quite popular practice in visualizing data, but it is not so easy to do, especially when there are multiple group values. This paper explores three different methods to dynamically assign colors to plots based on their group values. They are combining EVAL and IFN functions in the plot statements; bringing the DISCRETEATTRMAP block into the plot statements; and using the macro from the SAS^® sample 40255.

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Someone has aptly said, Las Vegas looks the way one would imagine heaven must look at night. What if you know the secret to run a plethora of various businesses in the entertainment capital of the world? Nothing better, right? Well, we have what you want, all the necessary ingredients for you to precisely know what business to target in a particular locality of Las Vegas. Yelp, a community portal, wants to help people finding great local businesses. They cover almost everything from dentists and hair stylists through mechanics and restaurants. Yelp's users, Yelpers, write reviews and give ratings for all types of businesses. Yelp then uses this data to make recommendations to the Yelpers about which institutions best fit their individual needs. We have the yelp academic data set comprising 1.6 million reviews and 500K tips by 366K in 61K businesses across several cities. We combine current Yelp data from all the various data sets for Las Vegas to create an interactive map that provides an overview of how a business runs in a locality and how the ratings and reviews tickers a business. We answer the following questions: Where is the most appropriate neighborhood to start a new business (such as cafes, bars, and so on)? Which category of business has the greatest total count of reviews that is the most talked about (trending) business in Las Vegas? How does a business' working hours affect the customer reviews and the corresponding rating of the business? Our findings present research for further understanding of perceptions of various users, while giving reviews and ratings for the growth of a business by encompassing a variety of topics in data mining and data visualization.

Sensitive data requires elevated security requirements and the flexibility to apply logic that subsets data based on user privileges. Following the instructions in SAS^® Visual Analytics: Administration Guide gives you the ability to apply row-level permission conditions. After you have set the permissions, you have to prove through audits who has access and row-level security. This paper provides you with the ability to easily apply, validate, report, and audit all tables that have row-level permissions, along with the groups, users, and conditions that will be applied. Take the hours of maintenance and lack of visibility out of row-level secure data and build confidence in the data and analytics that are provided to the enterprise.

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For SAS^® users, PROC TABULATE and PROC REPORT (and its compute blocks) are probably among the most common procedures for calculating and displaying data. It is, however, pretty difficult to calculate and display changes from one column to another using data from other rows with just these two procedures. Compute blocks in PROC REPORT can calculate additional columns, but it would be challenging to pick up values from other rows as inputs. This presentation shows how PROC TABULATE can work with the lag(n) function to calculate rates of change from one period of time to another. This offers the flexibility of feeding into calculations the data retrieved from other rows of the report. PROC REPORT is then used to produce the desired output. The same approach can also be used in a variety of scenarios to produce customized reports.

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Stephen Curry, James Harden, and LeBron James are considered to be three of the most gifted professional basketball players in the National Basketball Association (NBA). Each year the Kia Most Valuable Player (MVP) award is given to the best player in the league. Stephen Curry currently holds this title, followed by James Harden and LeBron James, the first two runners-up. The decision for MVP was made by a panel of judges comprised of 129 sportswriters and broadcasters, along with fans who were able to cast their votes through NBA.com. Did the judges make the correct decision? Is there statistical evidence that indicates that Stephen Curry is indeed deserving of this prestigious title over James Harden and LeBron James? Is there a significant difference between the two runners-up? These are some of the questions that are addressed through this project. Using data collected from NBA.com for the 2014-2015 season, a variety of parametric and nonparametric k-sample methods were used to test 20 quantitative variables. In an effort to determine which of the three players is the most deserving of the MVP title, post-hoc comparisons were also conducted on the variables that were shown to be significant. The time-dependent variables were standardized, because there was a significant difference in the number of minutes each athlete played. These variables were then tested and compared with those that had not been standardized. This led to significantly different outcomes, indicating that the results of the tests could be misleading if the time variable is not taken into consideration. Using the standardized variables, the results of the analyses indicate that there is a statistically significant difference in the overall performances of the three athletes, with Stephen Curry outplaying the other two players. However, the difference between James Harden and LeBron James is not so clear.

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The increasing popularity and affordability of wearable devices, together with their ability to provide granular physical activity data down to the minute, have enabled researchers to conduct advanced studies on the effects of physical activity on health and disease. This provides statistical programmers the challenge of processing data and translating it into analyzable measures. One such measure is the number of time-specific bouts of moderate to vigorous physical activity (MVPA) (similar to exercise), which is needed to determine whether the participant meets current physical activity guidelines (for example, 150 minutes of MVPA per week performed in bouts of at least 20 minutes). In this paper, we illustrate how we used SAS^® arrays to calculate the number of 20-minute bouts of MVPA per day. We provide working code on how we processed Fitbit Flex data from 63 healthy volunteers whose physical activities were monitored daily for a period of 12 months.

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Cycling is one of the fastest growing recreational activities and sports in India. Many non-government organizations (NGOs) support this emission-free mode of transportation that helps protect the environment from air pollution hazards. Lots of cyclist groups in metropolitan areas organize numerous ride events and build social networks. Although India was a bit late for joining the Internet, the social networking sites are getting popular in every Indian state and are expected to grow after the announcement of Digital India Project. Many networking groups and cycling blogs share tons of information and resources across the globe. However, these blogs posts are difficult to categorize according to their relevance, making it difficult to access required information quickly on the blogs. This paper provides ways to categorize the content of these blog posts and classify them in meaningful categories for easy identification. The initial data set is created from scraping the online cycling blog posts (for example, Cyclists.in, velocrushindia.com, and so on) using Python. The initial data set consists of 1,446 blog posts with titles and blog content since 2008. Approximately 25% of the blog posts are manually categorized into six different categories, such as Ride stories, Events, Nutrition, Bicycle hacks, Bicycle Buy and Sell, and Others, by three independent raters to generate a training data set. The common blog-post categories are identified, and the text classification model is built to identify the blog-post classification and generate the text rules to classify the blogs based on those categories using SAS^® Text Miner. To improve the look and feel of the social networking blog, a tool developed in JavaScript automatically classifies the blog posts into six categories and provides appropriate suggestions to the blog user.

Average yards per reception, as well as number of touchdowns, are commonly used to rank National Football League (NFL) players. However, scoring touchdowns lowers the player's average since it stops the play and therefore prevents the player from gaining more yardage. If yardages are tabulated in a life table, then yardage from a touchdown play is denoted as a right-censored observation. This paper discusses the application of the SAS/STAT^® Kaplan-Meier product-limit estimator to adjust these averages. Using 15 seasons of NFL receiving data, the relationship between touchdown rates and average yards per reception is compared, before and after adjustment. The modification of adjustments when a player incurred a 2-point safety during the season is also discussed.

For many organizations, the answer to whether to manage their data and analytics in a public or private cloud is going to be both. Both can be the answer for many different reasons: common sense logic not to replace a system that already works just to incorporate something new; legal or corporate regulations that require some data, but not all data, to remain in place; and even a desire to provide local employees with a traditional data center experience while providing remote or international employees with cloud-based analytics easily managed through software deployed via Amazon Web Services (AWS). In this paper, we discuss some of the unique technical challenges of managing a hybrid environment, including how to monitor system performance simultaneously for two different systems that might not share the same infrastructure or even provide comparable system monitoring tools; how to manage authorization when access and permissions might be driven by two different security technologies that make implementation of a singular protocol problematic; and how to ensure overall automation of two platforms that might be independently automated, but not originally designed to work together. In this paper, we share lessons learned from a decade of experience implementing hybrid cloud environments.

Do you write reports that sometimes have missing categories across all class variables? Some programmers write all sorts of additional DATA step code in order to show the zeros for the missing rows or columns. Did you ever wonder whether there is an easier way to accomplish this? PROC MEANS and PROC TABULATE, in conjunction with PROC FORMAT, can handle this situation with a couple of powerful options. With PROC TABULATE, we can use the PRELOADFMT and PRINTMISS options in conjunction with a user-defined format in PROC FORMAT to accomplish this task. With PROC SUMMARY, we can use the COMPLETETYPES option to get all the rows with zeros. This paper uses examples from Census Bureau tabulations to illustrate the use of these procedures and options to preserve missing rows or columns.

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