Data visualization is a strong tool for understanding the nature and distribution of collected data. A histogram is one such data visualization tool that can be used to assess normality of the data. Hence, plotting the correct histograms is important since the decision regarding additional analytical methods (parametric or nonparametric) is based on whether the data follows normal distribution. In SAS(r), different procedures, such as SGPLOT, SGPANEL, or UNIVARIATE, can be used to generate histograms. However, histograms plotted in SAS using the SGPLOT or SGPANEL procedures show an anomaly when the largest value in a set of data coincides with one of the tick points on the X axis of the histogram. This paper discusses this anomaly and suggests a remedy for solving it. This paper also suggests that the UNIVARIATE procedure can be used to validate the histograms produced using the SGPLOT and SGPANEL procedures. Furthermore, an alternative method for calculating the value to be specified for the BINSTART option in the SGPLOT and SGPANEL procedures that does not alter the histogram produced by the usual method is also suggested. All of the procedures described above were performed using SAS(r) 9.3.

Health-care organizations are faced with the challenge of analyzing high volumes of population-level health data in order to gain insights into ways to reduce cost while improving health-care quality and efficiency. This paper explains how to develop statistical models using SAS^® Real World Evidence to analyze cost and understand the key factors associated with health-care costs. Using SAS Real World Evidence, a population cohort based on a research question is created. Statistical modeling is performed on this cohort using the powerful Add-in Builder available in SAS Real World Evidence. The results from statistical models are then analyzed in order to understand characteristics associated with cost.

The DATA step has been at the core of SAS^® applications and solutions since the inception of SAS. The DATA step continues its legacy, offering its capabilities in SAS^® Cloud Analytic Services (CAS) in SAS^® Viya^® . CAS provides parallel processing using multiple threads across multiple machines. The DATA step leverages this parallel processing by dividing data between threads and executing simultaneously on the data assigned to each thread. The LAG function in the DATA steps RETAIN statement and the automatic variable _N_ require special attention when running DATA step programs in CAS. This paper describes each of these features, discusses how you can continue to use them with CAS, and compares performance with that in SAS^® 9.4.

This friendly and picture-oriented paper is about some new procedures for modeling using penalized variable selection and some procedures for building models that are a richer description of your data than you can get from ordinary least squares (OLS). The four procedures that we cover are REG, GLMSELECT, QUANTREG, and QUANTSELECT. The paper explains theory and gives examples of SAS^® code and output for four procedures. The new penalized methods that are discussed in this paper help, but only help, find a parsimonious model. They help us by using algorithms that are more stable, continuous, and computationally efficient than stepwise methods.

Although information for identifying high-potential customers is a key piece in a Customer Value Management strategy, it is hardly available in real-world databases. This paper describes an analytical framework developed to find the next best offer for each of the over five million customers in the mobile prepaid business of Telefonica Chile. This framework is based on the analytical life cycle methodology proposed by SAS^® best practices, which promotes business understanding as a crucial control point in the use of advanced analytics in the real world. A supervised learning approach using artificial neural networks in SAS^® Enterprise Miner(tm) is used to predict purchase behavior in a window of time based on the state of a set of variables. Then the best offer for each customer is assigned, based on the scoring propensity for every single product. The customer-product data set is used as input for SAS^® Real-Time Decision Manager to accelerate the consumption of the customers available balance every time a customer tops up or uses his handset. Finally, as the next step in this process, the generation of predictive models for other business objectives is being planned. In this sense, counting on SAS^® Viya^® benefits through SAS^® Visual Statistics and SAS^® Visual Data Mining and Machine Learning, we can improve the experience of use in a production industrial environment with more flexibility and higher performance of the analysis involved.

What are customer price and credit optimization? The author has faced this challenging question numerous times with various analytical and business professionals. On the surface, the question itself can appear straightforward to analytics and business management facing the complex decisions around customer value management. However, in reality, variations in the understanding of the customer price/credit optimization concept are quite vast and have led to a high degree of confusion around the optimization concept within the financial industry. The main objective of this presentation is to define the concept of customer pricing/credit optimization properly. The experimental design plays the key role in the definition of the optimal solution, and there is no a way around that. Eventually, any candidate for an optimal pricing/credit solution must be taken through a proper validation based on a proper experimental design. We illustrate cases with examples of experimentally designed test campaigns on real lending portfolios. Examples of pricing and credit optimization solutions are discussed in detail. This presentation is a follow-up to the authors previous presentation at SAS Global Forum 2016 about customer credit and pricing optimization. The critical tool for the authors optimization solutions is the effect/uplift modeling. The effect modeling tool, as well as other necessary tools for optimal solutions, have been developed using SAS/STAT^® software.

Lets face itthe media has given sharks a bad rap, portraying them as villains and creating a culture that fears the fin. Sources like the International Shark Attack File, the Global Shark Attack File, and OCEARCH provide us with staggering amounts of data every day. This data gives us the ability to map shark attack occurrences all over the world, to determine the activity that brought on the attack, whether the attack was provoked, and, ultimately whether it was a fatal attack. We can use the data to tell data stories about this animal species that fascinates so many of us. So, the prevailing question is, can we reduce fear over shark attacks using data? We explore this possibility using SAS^® Visual Analytics 8.1 on SAS^® Viya^®.

Phonetic search can be used across various areas if businesses would like to detect any potential risk to their customers based on various incidents, suspicious events, or negative news hits in the media. However, one might not receive exact or complete information, which means that searching with proper spelling and getting a correct result is a challenge. We implemented a process that is capable of searching based on pronunciation with spelling distance rather than using exact spelling. Basically, this process is based on two SAS^® algorithms: one is a phonetic search with a customized Soundex algorithm, and the other is a fuzzy match using the Levenshtein algorithm, associated with scores to improve accuracy. This solution empowered searching utility with higher efficiency in less time because the total complexity has been encapsulated at an abstraction layer in the program. As a result, users need to follow only the simplest of steps to obtain the end result.

SAS has delivered SAS^® Viya^® , the new open architecture powered by Cloud Analytic Services. It is a change in the fundamental methodology of installing SAS^® . Moving away from SAS depots, installation processes, and migration of user metadata is discussed in this presentation, as well as maintaining dual environments for SAS Viya and SAS^® 9.4. SAS Viya installations and migrations from small to large and from on-premises to the cloud are also discussed.

SAS^® Visual Analytics geo maps support country and state geographies out-of-the-box. It can be programmed to plot counties, cities, and ZIP code tabulation areas as well. However, we are often asked if we could plot an organizations custom regions on the map. It might be the sales territories or any nonstandard geographic regions of interest, defined in terms of standard geographic areas: ZIP code, county, or state. This paper outlines how you can plot such custom regions on a SAS Visual Analytics geo map.

The CUSTOM statement that was added to the POWER procedure in SAS/STAT^® 14.2 extends the scope of supported data analyses to include generalized linear models and other extensions of existing capabilities. It works in concert with an exemplary data set and the SAS/STAT procedure that you plan to use for the eventual data analysis. This paper explains the method and demonstrates it for a variety of data analyses, including Poisson regression, logistic regression, and zero-inflated models. It also discusses how you can use CUSTOM statement options to refine the method for sample size inflation/deflation or for extra covariates.

Crime poses a grave threat to the serenity of a city. This threat gets more acute with the increase in population and size of a city. The core objective of this paper is to predict the potential number of crime incidents for a city by using historical crime data and to provide insights for predictive first-responder planning and law enforcement. This investigation examines the historical crime data for the city of Los Angeles, California for the time period of January 1, 2010 to July 21, 2017 and ongoing. The data set that was used has been publicly made available by the Los Angeles Police Department. It consists of over 1.4 million observations and 26 variables. Primary exploratory data analysis provided basic insights such as the district where crime is most prevalent (District 363), the most commonly used weapon (strong arm - hand fist) and the most common crime occurrence time (noon). Trend analysis revealed that the crime rate had significantly increased after 2014 and that Battery - Simple Assault was the most common crime. SAS^® Enterprise Guide^® and R were used for data cleaning, exploration, and data modeling. Work is ongoing to develop statistically valid models to predict crimes for Battery (Simple Assault) using various models such as ARIMA, ETS, and Prophet. In addition, forecasting methodologies such as simple exponential smoothing models, Holts smoothing model, additive and multiplicative models, and Winters model will also be explored for accuracy.

The game of baseball can be explained as a Markov process, as each state is independent of all states except the one immediately prior. In this research, probabilities are calculated from historical data on every state transition from 2011 to 2016 for Major League Baseball games, and are then grouped to account for homefield advantage and offensive player ability. Simulation is applied to mimic games or entire seasons of baseball teams. For a specific game, the results give the probability of a win and expected runs for each team. For a season, the results of the simulation give a teams overall winning percentage. Predictions can be used by managers, players, and fans for varying interests.

The last 30 years have seen extraordinary development of new tools for the prediction of numerical and binary responses. Examples include the LASSO method and elastic net for regularization in regression and variable selection, quantile regression for heteroscedastic data, and machine learning predictive methods such as classification and regression trees (CART), multivariate adaptive regression splines (MARS), random forests, gradient boosting machines (GBM), and support vector machines (SVM). All these methods are implemented in SAS^® , giving the user an amazing toolkit of predictive methods. In fact, the set of available methods is so rich it begs the question, When should I use one or a subset of these methods instead of the other methods? In this talk I hope to provide a partial answer to this question through the application of several of these methods in the analysis of several real data sets with numerical and binary response variables.

Preparing students to the take the Base SAS^® Certification exam is challenging in a formal classroom setting. The diverse backgrounds of the students combined with the unique features of SAS^® requires careful planning and insightful examples. The course is part of the core required in the Master of Science in Applied Statistics (MSAS) program at Kennesaw State University. The execution of the course has changed over time due to the experiences of the students and feedback from industry. The topics discussed in this presentation include an overview of the population that takes the class at Kennesaw State University, the technology available to the students, the current organization of the course, the materials provided by KSU, the materials and support provided by SAS, as well as the pitfalls that were encountered in the past that informed some of the current design decisions. Examples of the materials are incorporated into the presentation, as well as challenges faced by the students. The exam can be proctored as part of the class, which also presents an interesting dynamic. The experience, in general, is very similar to a coach preparing an athlete to compete. Much of the mental preparation is on the studentwe can only create an environment that exposes the individual to the challenges that they will face.

Predictive models are now commonplace, with many applications in the areas of credit risk, customer relationship management (CRM), and marketing. They are used to find the most likely outcome resulting from specific actions taken by organizations in their interaction with their clients. These models have proven to be effective in improving the efficiency and performance of businesses over the last 20 years. The challenge these organizations face now is how to optimize the deployment of predictions made by these models when there is more than one possible model to follow. This presentation reviews the use of mathematical optimization techniques and how they can be used as a second layer on top of predictive models in order to find the best action. In the presentation, the basics of mathematical optimization are reviewed using simple examples, and these simple cases are extended to cover real problems encountered in marketing and credit risk. Then, a case study is presented to show how optimization was used by a North American bank to find the best collections channel. For this specific problem, several predictive models were developed to predict the likelihood of successful collections using a specific collections channel (such as telephone, SMS text message, email, letter, and so on). The predictions from these models were then used in a mathematical optimization formulation to find the best channel per account.

The SORT procedure has been an integral part of SAS^® since its creation. The sort-in-place paradigm made the most of the limited resources at the time, and almost every SAS program had at least one PROC SORT in it. The biggest options at the time were to use something other than the IBM procedure SYNCSORT as the sorting algorithm, or whether you were sorting ASCII data versus EBCDIC data. These days, PROC SORT has fallen out of favor; after all, PROC SQL enables merging without using PROC SORT first, while the performance advantages of HASH sorting cannot be overstated. This leads to the question: Is the SORT procedure still relevant to any other than the SAS novice or the terminally stubborn who refuse to HASH? The answer is a surprisingly clear yes. PROC SORT has been enhanced to accommodate twenty-first century needs, and this paper discusses those enhancements.

In clinical trials, a data definition table (DDT) is a document that lists a variety of information about each case report form (CRF), such as form number, variable name, variable label or description, data type, length, and codes. When imported into SAS^®, it can be used to accomplish a variety of tasks. This paper provides a workflow for creating a format library and test data for CRFs by using SAS and the DDT in Microsoft Excel. Both creating a format library and test data are necessary pieces to gather at the beginning of a study to eliminate the need to re-create formats in each of your SAS programs (reduce code replication) and to start programming when real study data is not available yet (quicker delivery reporting time), respectively. Other uses for the DDT include creating the SAS code for the codebook, annotated CRFs, and analysis file documentation.

This presentation goes through the process of creating a macro to find interpolated medians in Base SAS^® . While medians are a great way to summarize the center of skewed data, but when collected data comes from an ordinal scale or is drawn from a very small range in possible values, they are not ideal. Two medians can be exactly the same but come from data that is weighted very differently. For that reason, the median does not always accurately represent the center and shape of the data as accurately as it could. Interpolated medians, on the other hand, can better represent whether the weight of the data is above or below the true median. Therefore, they not only tell you something about the center of the median, but also describe the shape of the data. This presentation explains the origins, calculations, and uses of interpolated medians. I then show an example of where the interpolated median succeeds where the regular median falls short by showing two sets of data with the same possible range of values but very different samples. I show that they have the same median and show how the interpolated median becomes much more descriptive and a better measure in this situation. Then I go through the code I used to develop the %MACRO to calculate interpolated medians, along with the tests I ran to validate it against known data sets and known interpolated medians to ensure that the %MACRO functions correctly and is an easy-to-use way to calculate interpolated medians in SAS^® .

The Great Lakes are the largest group of freshwater lakes on the planet and contain 21% of the worlds fresh water. Data on the daily temperatures of the individual Great Lakes is stored on the internet in a collection of text files maintained by the National Oceanic and Atmospheric Administration within the U.S. Department of Commerce and is updated daily. SAS^® was used to read this data from the internet and create attractive, reproducible, and informative reports. In this paper, we illustrate how SAS can be used to read data from the internet, how to wrangle and combine the data to get it ready for analysis, how to work with Julian dates, and how to use the Output Delivery System to make reproducible reports. We also explore how to make attractive visualizations using the SGPLOT procedure, and how to use polygon files to make an animated map using the GMAP procedure. Furthermore, we show how SAS/STAT^® software can be used to explore relationships and build predictive models between lake temperatures, land temperatures, and snowfall amounts in a city to the west of Lake Michigan. These data and examples work very well for illustrating statistical computing techniques to students in a classroom or for tools for self-study.

Image processing is not new and has been here for a while. But now, with the advent of the Internet of Things, this has become one of the most promising and exciting applications of real-time streaming analytics technologies. Historically focused on character or face recognition, the image-processing world has evolved to include enhanced predictive and prescriptive analytics applications using real-time streams of rich image and video content. From predictive maintenance in manufacturing to health care or security, there are countless examples of image analytics domain applications. In this session, we explore how to implement image analysis with SAS^® Event Stream Processing and how to deploy image-processing analytic models created with SAS^® Viya^® .

The SAS^® middle tier includes the middle-tier infrastructure software such as SAS^® Web Server, SAS^® Web Application Server, Java Message Service broker, and the system management software such as SAS^® Environment Manager, all of which are critical to all SAS products with a web frontend and all SAS middle-tier services. Since the last SAS^® Global Forum, we have made a lot of updates and significant improvements to the middle tier that are driven by customer requirements and input from consultants and SAS technical support. We believe it is extremely important to provide a detailed review of the updates so that SAS administrators can better manage their SAS middle-tier environment and satisfy their corporate IT requirements with improved confidence and fun. This paper first reviews the key components of the SAS middle tier, and then discuss updates made in recent SAS^® 9.4 releases (mainly SAS^® 9.4M4 and SAS 9.4M5). We specifically discuss the following topics: changes to SAS Web Server, SAS Web Application Server, and Java Message Service broker; changes to SAS^® Private Java Runtime Environment; a new process for updating your horizontal cluster node the right way; SSL library updates to address your concerns about OpenSSL vulnerability; how to preserve your manual SSL configurations; improvements to hardening your environment to enforce TLSv1.2 protocol; and changes to system management and SAS^® Environment Manager.

As financial institutions market themselves to increase their market share against their competitors, they understandably focus on gaining new customers. However, they must also retain (and further engage) their existing customers. Otherwise, the new customers they gain can easily be offset by existing customers who leave. Happily, predictive analytics can be used to forecast which customers have the highest chance of leaving so that we can effectively target our retention marketing toward those customers. This can make it much easier and less expensive to keep (and further cultivate) existing customers than to enlist new ones. A previous paper showed a simple but fairly comprehensive approach to forecasting customer retention. However, this approach fails in many situations with more complicated data. This paper shows a much more robust approach using elements of machine learning. We provide a detailed overview of this approach and compare it with the simpler (parametric) approach. We then look at the results of the two approaches. With this technique, we can identify those customers who have the highest chance of leaving and the highest lifetime value. We then make suggestions to improve the model for better accuracy. Finally, we provide suggestions to extend this approach to cultivate existing customers and thus increase their lifetime value. Code snippets are shown that function for any version of SAS^® but they require SAS/STAT^® software.

The Cobb County Fire Departments (CCFD) 8-minute emergency response time doubles the National Fire Protection Associations (NFPA) 4-minute standard, measured at the 90th percentile of all emergencies. This project aims to reduce CCFDs response time by focusing on the travel times of their various emergency vehicles. Currently, there are 29 fire stations and 272 fire zones within Cobb County, with each fire station being responsible for a pre-defined set of fire zones. We investigate whether fire zones and stations can be realigned to reduce travel times by analyzing historical response time data from September 2015 to August 2016. CCFD historical data reveal which fire station actually responded to each incident, as well as the related travel time. Google Maps is then used to check the response times from neighboring fire stations to determine whether a different fire station could have responded more quickly to the same incident. The comparison between historical and Google travel times reveals the location and frequency of disagreement between the historical and Google recommended fire stations. Fire zones can then be reassigned to different fire stations to reduce future traveling times. Results vary for each fire station, but they show that there is room for improvement in the way that CCFD currently responds to emergencies. Python is used to connect to the Google Maps Distance Matrix API, and SAS^® is used for resulting analyses.

Stock market conditions, government policy changes, or even weather patterns can be regarded as stochastic processes that are driven by unobserved regimes. A powerful tool to explore these behavioral patterns is the regime-switching model (RSM) that is offered in the HMM procedure and the associated action in SAS^® Econometrics software. This model, which is widely used in finance, economics, science, and engineering, has two characteristics: it allows different parameter values for different regimes, and it models the transition probabilities between regimes. These characteristics enable it to fully capture the structural changes in the time series. This paper uses two examples to illustrate how you can use RSMs to better understand the regime patterns in your data and improve your economic analysis. The first example demonstrates how regime-switching autoregression (RS-AR) models help you characterize the volatility and dynamics of stock returns. The second example examines the relationship and movement between the Japanese yen and the Thai baht by using regime-switching regression (RS-REG) models.

Analysts who do statistical modeling, data mining, and machine learning often ask the question, I have hundreds of variableseven thousands. Which should I include in my regression model? This paper describes SAS^® Viya(tm) procedures for building linear and logistic regression models, generalized linear models, quantile regression models, generalized additive models, and proportional hazards regression models. The paper explains how these procedures capitalize on the in-memory environment of SAS Viya, and it compares their syntax, features, and output with those of high-performance regression modeling procedures in SAS/STAT^® software.

Millions of people spend their weekdays in an office. Occasionally they hang a new picture, or add a new piece of furniture. Eventually their office gets so full they need to reorganize it to find items quicker and work more efficiently. After 15 years, that time has come for the SAS^® Add-In for Microsoft Office. This paper reviews the new user interface for the SAS Add-In for Microsoft Office. A redesigned ribbon enables quicker access to the most common operations. Where there used to be several task panes that each served a specific purpose, the task panes are now blended together into one home page that enables users to easily discover, execute, and manage SAS^® content. Come see these enhancements and many more in the new version of the SAS Add-In for Microsoft Office.

SAS^® Viya^® is like a Swiss Army knife when it comes to allowing all types of clients to talk to itJava, Python, Lua, R, and, of course, SAS^® . In addition to using these language-based APIs, SAS also publishes APIs that conform to REST best practices. The well-designed APIs in SAS Viya make it relatively easy to build complex web and non-web applications with SAS Viya as the engine of your application. The applications can be solely based on SAS Viya capabilities or users can do a mashup of the advanced SAS Viya analytics with their other products and capabilities. restAF is a light-weight ui-framework-agnostic JavaScript library designed to be easy-to-use for app developers. restAF takes advantage of the consistency of the REST APIs to reduce the responses from the server into a standard form, and also manages the data on the client side. This reduction enables developers to interact with the server in a repeating pattern regardless of which service the application is using. In this presentation, we explain the concepts behind restAF and demonstrate building applications that exploit SAS^® Cloud Analytic Services, compute server, SAS^® Visual Analytics, and other key capabilities of SAS Viya in a very consistent manner with minimal coding. restAF (along with all the examples and detailed documentation) are available as an open-source project. You can download it from GitHub with the usual open-source licensing policies.

Maxtera, a member of the CDS group, received the SAS^® Channel Partner award in 2016. With its innovative initiatives in the areas of data analysis and analytical intelligence, Maxtera provides its clients with information that generates knowledge, which contributes to better decisions and new actions in their business area. Several public institutions in Brazil at the federal, state, and municipal levels, in partnership with CDS/Maxtera, use the SAS^® tools to identify signs of irregularities (fraud) in their businesses in the areas of revenue recovery, benefit payments, government procurement, education, public safety, among many other business areas. In less than two years, more than 100 million dollars have already been recovered or prevented from migrating to various types of people and criminal organizations. This experience has aroused the interest of many public administrations, and CDS/Maxtera has used SAS solutions and tools to contribute to the success of these projects, applying methodologies and procedures for data analysis and analytical intelligence. This e-poster presents the financial results achieved in the projects, methodologies, and procedures used by CDS/Maxtera clients with the application of SAS tools. Our goal is to contribute our experience and learning with other SAS partners present at SAS^® Global Forum 2018.

Affinity analysis is referred to as Market Basket Analysis in retail and e-commerce outlets application. It determines how often items are purchased together and the best possible groupings of products that are regularly bought by customers in order to understand their behavior and preferences. This analysis enables you to design an effective marketing campaign and is appropriate for several kinds of decisions, such as cross-selling products, bundle of products, and product placement, and can provide guidance for website and loyalty program design. A product bundling strategy can help you either to sell two or more products or services together, or to optimize marketing content such as product catalogs, and social media and internet advertising. The output of the Market Basket node in SAS^® Enterprise Miner(tm) generates vast numbers of rules that enable us to analyze discovered patterns that are often used as business guidelines for marketing strategies. However, this is not enough to identify the exact decreasing sequential co-occurrence of the set of items that embodies the bundle. To overcome this limitation, a new approach, implemented as a new SAS Enterprise Miner node, is proposed to identify a bundle. This approach simplifies the selection of items and adds richer information to marketing initiatives.

Multicollinearity is the phenomenon in which two or more identified predictor variables in a multiple regression model are highly correlated. The presence of this phenomenon can have a negative impact on the analysis as a whole and can severely limit the conclusions of the research study. This paper reviews and provides examples of the different ways in which multicollinearity can affect a research project, how to detect multicollinearity, and how one can reduce it through ridge regression applications. This paper is intended for any level of SAS^® user.

With the new SAS^® Visual Analytics and SAS^® Visual Data Mining and Machine Learning capabilities on SAS^® Viya^®, institutions can more quickly and easily understand drivers of customer risk in a visually appealing way. Analysts can identify new opportunities for stakeholders to identify potential gains or losses by understanding drivers of risk and detecting new risk factors as they emerge over time. Using a combination of supervised and unsupervised machine learning methods, businesses can further understand and improve their own definition of customer risk as an organization. In this talk, Zencos shares its expertise in identifying high-risk attributes, understanding the relative importance of each driver, and recognizing key combinations of factors associated with risky behavior through an anti-money laundering case study.

Home telematics is a new and growing field in the insurance industry. Whereas traditional rating variables, such as amount of insurance and type of home construction, describe a home by its structure, telematics variables describe how the home is used. Telematics variables are derived from sensors placed in the home, such as thermometers, motion detectors, and smoke detectors. The data from these sensors are summarized in many different ways in order to provide potential model variables. For insurance companies, the ultimate goal is to use these data to predict future insurance losses for a group of homeowners policies. However, such high-frequency, high-dimensional data are usually full of outliers, so the data require preprocessing. Two objectives of this preprocessing are to determine which homes behave as outliers and to reduce the dimensionality of the predictor variables. The RPCA procedure in SAS^® Visual Data Mining and Machine Learning software uses robust principal component analysis to accomplish these objectives. Principal component analysis (PCA) is a common approach to reducing the dimensionality of a matrix. The robust part of this analysis involves splitting the original data matrix into a low-rank matrix and a sparse matrix before performing PCA. The low-rank matrix contains the normal portion of the variables, and the sparse matrix contains the outlier portion of the variables. You can use both these outputs from PROC RPCA to get a better understanding of the data and to prepare the data for modeling.

Tuning of models has become a very important topic. Machine learning models, especially neural nets, have many hyperparameters that might impact the outcome of the modeling process dramatically. Today, some machine learning products, such as SAS^® Visual Data Mining and Machine Learning, include autotuning features. This paper discusses (1) different tuning criteria of the robust model, (2) possible approaches toward optimal tuning such as full factorial, screening, response surface methodology, Taguchi L designs, and stochastic design of experiments, and (3) their practical implementation in SAS^® Enterprise Miner(tm). Similar to the Nelder-Mead technique, optimal tuning can include an iterative design of experiments, extending the factors levels that reach their limits at interim optimal points. Applying the Taguchi inner array for model hyperparameters and the outer array for validation partition setups and randomizations creates the possibility of using dual response surface methodology to find a robust optimal solution that acknowledges variance across outer arrays. Robust experiment design could allow for substantial subsampling of the training and testing data sets to accelerate the tuning process while controlling both signal and noise aspects. In addition to achieving model optimization, robust tuning provides insights on how model structure and hyperparameters influence the model performance. The latest enables learning by tuning.

As the retail world has shifted to an omnichannel environment with ever-growing competition, have you ever found yourself overwhelmed by how best to maximize revenue while at the same time improve the operational efficiencies? In this session, you learn how Levi Strauss Co. is modernizing and complementing the Supply and Sourcing Management reporting process by incorporating predictive analytics and decision-making insights to inform and make accurate actions throughout their global supply chain, with a vision of expanding into other areas in the future including the Levi Strauss Co. Global End to End Planning Process and with supplier partners.

Chronic diseases such as diabetes and hypertension are very widely prevalent in the world. They are responsible for a significant number of deaths each year and treatment for such chronic diseases accounts for high health care costs. Research has shown that these diseases can be proactively managed and prevented while lowering the health care cost. Using SAS^® Analytics solutions, we mined a data sample of close to one million patient profiles to identify who can benefit from preventive care and lifestyle changes. Later in the analysis we correlate to associate those patient profiles with their socio-economic data to shed light on a prevention management strategy.

SAS^® programming can feel fraught with danger. Each time a program is executed, there is a risk that an error in the source data or code will lead to undetected erroneous results. Run-time validation is a defensive programming technique designed to decrease that risk by adding code to detect and report errors as a program runs. This paper presents three utility macros for run-time validation of SAS jobs: %DupCk detects duplicate key values in a data set; %Assert detects values in a data set that are invalid or unexpected; and %CheckRecordCounts detects the accidental deletion of records. By increasing the detection rates of common errors, these utilities increase the programmers confidence in their results. Principles of run-time validation are discussed, as are principles of macro design encountered during the development of the macros.

The Challenge: We have long programs that accomplish a number of different objectives. We often want to run only parts of the programs while preserving the entire programs for documentation or future use. Some of the reasons for selectively running parts of a program are: 1) Part of it has run already, and the program timed out or encountered an unexpected error. It takes a long time to run, so we don't want to re-run the parts that ran successfully; 2) We don't want to re-create data sets that were already created. This can take a considerable amount of time and resources, and can also occupy additional space while the data sets are being created; 3) We need only some of the results from the program currently, but we want to preserve the entire program; 4) We want to test new scenarios that require only subsets of the program.

SAS^® custo+D324mers now have the opportunity to move workloads to the cloud, benefiting from lower operating expenses (OPEX) and maintenance costs, reduced risk, and, most importantly, keeping analytics close to the data. This can be accomplished without sacrificing performance, predictability, security, and the overall user experience. After extensive joint development and testing with SAS, Oracle can provide a seamless cloud platform for SAS^® Viya^® and SAS^® 9.4 leveraging Oracle Cloud Infrastructure. This cloud platform delivers scalable analytics performance, delivering speeds equal to or better than an on-premises solution. In this session, we discuss the parameters of the joint testing, why Oracle is able to provide a superior cloud platform for analytics, and how a SAS customer is leveraging the Oracle cloud platform and the benefits that they are experiencing.

SAS^® Theme Designer enables customers to create their own custom application and report themes and tailor the visual look of the applications they run and the reports they generate. Users can specify colors, fonts, and images, and the visual changes are presented within an embedded preview application or within an actual application (when editing a theme within application context). Users can customize their applications and reports to create a unique organizational theme, or they can customize their applications and reports based on an individual business requirement. This visual customization provides for the customers desire for colors, general branding, and logo integration. This paper explores the process of using SAS Theme Designer in conjunction with other applications such as SAS^® Visual Analytics. This paper also highlights the process of creating and modifying application themes, and previewing application color, font, and image changes within applications such as SAS Visual Analytics.

SAS® middle-tier performance needs to be optimal to provide users with the best possible experience as well as to support growth in number of users. Optimal performance requires tuning. However, due to differences in the SAS web applications that are deployed as well as differences in the mix of applications in use, the tuning that is optimal for one deployment might not be optimal for another deployment. This session covers the tools that you can use to understand what's going on in a SAS middle tier, as well as the techniques you can use to tune the SAS middle tier once you understand what's going on.

The latest release of SAS/STAT^® software has something for everyone. The new CAUSALMED procedure performs causal mediation analysis for observational data, enabling you to obtain unbiased estimates of the direct causal effect. You can now fit the compartment models of pharmacokinetic analysis with the NLMIXED and MCMC procedures. In addition, variance estimation by the bootstrap method is available in the survey data analysis procedures, and the PHREG procedure provides cause-specific proportional hazards analysis for competing-risks data. Several other procedures have been enhanced as well. Learn about the latest methods available in SAS/STAT software that can modernize your statistical practice.

Do you maintain a Microsoft Windows server providing your organizations SAS^® 9.4 environment? Are you struggling to get Kerberos authentication out to Apache Hadoop working correctly for your SAS^® Enterprise Guide^® or SAS^® Studio users? This paper outlines the key steps for enabling Kerberos authentication between your users, your SAS 9.4 deployment on Microsoft Windows server, and your Hadoop data sources. Learn about the configuration changes you need to make, and also learn about some effective trouble-shooting techniques to get your environment working and your users happy.

SAS^® Analytics Innovation Lab was established at La Trobe University Business School in 2015 as part of the SAS^® academic program in Australia and New Zealand. The research lab has developed new cutting-edge technology in machine learning and text analytics, with applications in healthcare, social media analytics, security, federated search, Internet of Things (IoT) stream analytics, and video captioning. The SAS Analytics Innovation Lab is part of the new Research Centre for Data Analytics and Cognition, where the key focus is to carry out academic research targeting innovative solutions to business and real-world problems. The collaboration with SAS and use of SAS tools have provided the researchers with an industry-known platform and technologies that can be used as a Trojan horse to introduce new research to industry. Several examples of how SAS technologies and new research from the SAS Analytics Innovation Lab have provided innovative practical solutions are presented.

This e-poster provides a discussion of why should you use a Docker container to run SAS^® Analytics (or any other software application). Topics include a business case, a value proposition, an architectural review, and building and deploying the container. There is also a demo of using SAS Analytics in the container.

Python is often one of +D331the first languages that any programmer studies. In 2014, Python was named as the most popular introductory teaching language for US universities, and in 2017 it was named third in the list of popular programming languages taught within the UK. In April 2017, SAS introduced the SASPy project, a module that can be installed on top of the Jupyter Notebook to enable the Jupyter Notebook to connect to SAS^® 9.4 or SAS^® Viya^® workspace sessions. This SAS^® workspace session connectivity enables any authenticated users to utilize many of the procedures available within the SAS environment, the majority of which are also available by using the Python programming language. This enables users to have the freedom to code in a language of their choice with the view of achieving the same results. SASPy also includes a method that can convert any Python coding that is submitted into the SAS coding equivalent. This method enables SAS coders and Python coders to work side by side, in addition to aiding those Python coders in their learning of the SAS programming language.

The ability to translate medical claims data into actionable insights is an important step in deriving knowledge useful to make inferences about the healthcare system. A single patient can have numerous diagnosis codes per encounter. Developing methods to efficiently process this information is necessary in order to provide meaningful health statistics to providers that help them to better understand the needs of their populations with multiple conditions. SAS^® array statements and the macro facility help to streamline this process in an efficient way. This paper discusses, through examples, how Base SAS^® techniques such as RETAIN, FIRST, LAST, DO loops, and the LAG function can work together with SAS arrays and macros to transform a claims data file into population-level summaries.

SAS^® Cloud Analytic Services (CAS) provides the analytics that power SAS^® Viya^® . Just as SAS^® provides a wide range of capabilities through its extensive array of procedures, so CAS services are supplied by a wide (and growing) variety of action sets. And just as PROCs each define their own grammar and output tables, so CAS actions define their own programming interface in the form of input parameters and results. But the differences between conventional PROCs and CAS actions are also illuminating. Action execution occurs in parallel on the CAS cluster with reference to a server-side environment (a session, caslibs, tables). And when writing scripts or applications to invoke CAS actions, SAS and other popular industry programming languages are provided an equal footingwith each programming language client customized for its own ecosystem. This paper is a complete introduction to CAS actions and explains how actions execute; introduces the programming concepts common to all actions and to all client languages; and provides a brief tour of the programming interfaces for SAS, Java, Python, R, and REST. A complete understanding of CAS actions will be beneficial to IT as they seek to implement, manage, and operate SAS Viya. And it is essential for SAS Viya programmers, such as data scientists, who want to solve specific problems by invoking CAS analytics directly.

A SAS^® environment can be very complex under the covers, with many configuration options that can affect everything from performance through to security. Having automations in place that ensure that only authorized settings are in effect (for both SAS and the operating system) can lighten the load of a SAS administrator as well as that of IT. Ansible can take care of ensuring that your SAS prerequisites are met and continue to be met, while also making sure that only tested and authorized configuration changes are implemented.

The banking sector experiences increased demands related to risk assessment because of the Basel Capital Requirements. Credit modeling and scoring is an important component of estimating the capital requirement, and banks face various challenges and needs related to this modeling. SAS^® Credit Scoring for Banking is an integrated solution that enables detailed analysis and improved prediction of credit risk, with these challenges and needs in mind. This session is based on experiences gained from implementing SAS Credit Scoring for Banking for a series of banks. We discuss the solution architecture in the context of challenges and needs related to credit modeling. The architecture overview highlights the SAS^® software included in SAS Credit Scoring for Banking and the stages at which the different software comes into play. We consider the benefits of SAS Credit Scoring for Banking and see how the solution has the flexibility of modification to include additional data sources and existing models. The session ends with examples of model monitoring and reporting in the solution.

SAS^® applications are used in wide-ranging lines of business in enterprise organizations today. SAS architecture provides interfaces that enable their use in a range of different use cases. In the past, these applications were built on different SAS components with various user interfaces and varying underlying operating systems with different operational procedures. With the trend toward IT consolidation, SAS components and SAS applications that were developed in the last decades are in the scope of the ongoing consolidation processes around the globe. Some of these applications were developed based on technologies that would be called legacy today. Nevertheless, for businesses in which they are still in use, they play vital roles in their business processes. These applications need an application infrastructure that both fits the application and is sustainable for the future in terms of regulation and security. This paper gives you insights into the technical and governmental challenges that occur during a consolidation process. It focuses on building up a platform, how to deal with change management, and describes the different steps you have to take in order to govern application consolidation.

The latest version of SAS^® Viya^® brings with it a wealth of new capabilities, and administrators are by no means left out of the party. The version of SAS^® Environment Manager that accompanies SAS Viya 3.3 significantly ratchets up what a SAS^® administrator can accomplish with this next generation HTML5-based web application. See new features in its top-level dashboard, including an at-a-glance widget that provides the health of all SAS Viya machines, services, and service instances in a hierarchical heat map. Witness the new Logged Issues widget highlighting recent errors with details to help diagnose and head-off escalating problems. Learn about all the new SAS Environment Manager menu options, including viewers for the following: Licensed Products showing expiration dates and grace periods for all deployed products Logsconsolidating logging from SAS services with advanced filtering and summarization Machinesgraphing CPU and memory consumption, and listing specific metric checks and supported services Schedulingmonitoring and editing scheduled jobs Finally, discover additional features like an advanced data explorer for importing data sources, as well as the ability to create and manage user-defined formats. In general, gain an appreciation for all the new blades SAS has added to its multi-purpose Swiss Army knife for administration. Gain an understanding of advanced features and sharpen your skills in monitoring and managing a SAS Viya 3.3 environment.

Brazils Social Security monthly budget is about US$12 billion, paying benefits to 34 million people. In Brazil, the Federal Court of Accounts is responsible for auditing these expenses. In the last few years, the Tribunal de Contas da Unio (TCU) has found several improper payments, and has acted alongside the Social Security Agency to reduce such losses. In this paper, we describe how SAS^® Fraud Framework was used to estimate and monitor the evolution of the percentage of improper payments of social benefits granted. The TCU team conducted 685 interviews with experts from different areas (auditing or control). Based on the analysis of this data, they generated triangular probability distribution per respondent, applied Markov chain Monte Carlo (MCMC) simulations and fitted probability distributions (Kernel and Beta), aiming to estimate the incidence of irregularities. The reliability level of the instrument was verified by cluster analysis and by the use of retest items in the interviews. The results showed that 20% to 38% is the expected percentage of improper payments in Brazilian Social Security, according to the perception of experts regarding the incidence of all possible types of irregularities. Analyzing by type of benefit or region, the Northern Region and the benefits to the poor elderly showed the highest averages (29% and 27%). These procedures resulted in the estimation of approximately $18 million improper monthly payments.

SAS^® Grid Computing is a shared, centrally managed analytics computing environment that features workload balancing and management, high availability, and fast processing. A SAS^® grid environment helps you incrementally scale your computing infrastructure over time as the number of users and the size of data grow. It also provides rolling maintenance and upgrades without any disruption to your users. We have built a quickstart with Amazon Web Services (AWS) and SAS, which launches the infrastructure for the SAS grid. This quickstart is for IT infrastructure architects, administrators, and DevOps professionals who are planning to implement or extend their SAS workloads on the AWS Cloud. It deploys the infrastructure for implementing SAS Grid Computing and related SAS components on Amazon Elastic Compute Cloud (Amazon EC2) instances and uses security groups, a virtual private cloud (VPC), subnets, and AWS Elastic Load Balancing to provide security and availability. A SAS grid environment in the cloud provides the elasticity and agility to scale your resources as needed. The quickstart automatically builds and configures the required infrastructure for SAS Grid Computing application installation, thereby reducing the dependency on your IT team. The effort required to plan, design, and implement the infrastructure is eliminated.

SAS customers appreciate SAS^® Grid Manager for the many benefits it provides to their SAS^® environment. Enhancing the availability of their critical services is certainly one of the most popular benefits. This paper presents the best practices for implementing highly available services and leveraging the new failover orchestration capabilities provided by the latest release of SAS Grid Manager.

Mediation analysis is a statistical technique for investigating the extent to which a mediating variable transmits the effect of an independent variable to a dependent variable. Because it is used in many fields, there have been rapid developments in statistical mediation. The most cutting-edge statistical mediation analysis focuses on the causal interpretation of mediated effects. Causal inference is particularly challenging in mediation analysis because of the difficulty of randomizing subjects to levels of the mediator. The focus of this paper is on updating three existing SAS^® macros (%TWOWAVEMED, %TWOWAVEMONTECARLO, and %TWOWAVEPOSTPOWER, presented at SAS^® Global Forum 2017) in two significant ways. First, the macros are updated to incorporate new cutting-edge methods for estimating longitudinal mediated effects from the Potential Outcomes Framework for causal inference. The two new methods are inverse-propensity weighting, an application of propensity scores, and sequential G-estimation. The causal inference methods are revolutionary because they frame the estimation of mediated effects in terms of differences in potential outcomes, which align more naturally with how researchers think about causal inference. Second, the macros are updated to estimate mediated effects across three waves of data. The combination of these new causal inference methods and three waves of data enables researchers to test how causal mediated effects develop and maintain over time.

The SAS^® ODS Excel destination statement option OPTIONS has many suboptions. One of those suboptions is START_AT, which enables you to place your output anywhere on the open output Microsoft Excel worksheet. I will show you how to put your starting output data element into any row and column in the open worksheet.

SAS^® Studio is an important new interface for SAS^®, designed for both traditional SAS programmers and for point-and-click users. For SAS programmers, SAS Studio offers many useful features not found in the traditional Display Manager, including integrated syntax help, one-touch code formatting, and the ability to drag-and-drop variable and data set names into your code. SAS Studio runs in a web browser. You write programs in SAS Studio, submit the programs to a SAS server, and the results are returned to your SAS Studio session. SAS Studio is included in the license for Base SAS^®, is the interface for SAS^® University Edition, and is the default interface for SAS^® OnDemand for Academics. Both SAS University Edition and SAS OnDemand for Academics are free-of-charge for non-commercial use. With SAS Studio becoming so widely available, this is a good time to learn about it.

SAS^® Visual Analytics 8.2 extends the unified SAS^® user experience to data exploration and reporting, adds new features to report content, and introduces exciting new report objects. The user experience design presents a familiar and consistent user interface for navigation at the report, page, and object levels. New features enrich report content such as improvements in handling data, enhancements in graphs and tables, and more geographic capabilities. New report objects in the release include the Key Value object to provide infographic-like treatment, the parallel coordinates plot for more analytical visualization, and Data-Driven Content to manipulate externally created content within the point-and-click interface of the report.

There is a limit to employee skills and capacity to efficiently analyze volumes of textual unstructured data in a manner that provides actionable business insight. SAS^® provides several tools that provide the capacity to explore text: SAS^® Text Miner, SAS^® Sentiment Analysis, and SAS^® Visual Analytics. SAS^® Visual Analytics word clouds expand analytical capacity by making text analytics and sentiment analysis easier to use, thereby putting these powerful tools in the hands of a much broader audience. Using a point-and-click interface, SAS^® Visual Analytics word clouds can explore larger volumes of unstructured data, identify patterns and create usable reports to help define policies, and take actions that improve business operations.

SAS^® Visual Forecasting, based on SAS^® Viya^® , is the next generation SAS^® product for forecasting. It provides a new resilient, distributed, scripting environment for cloud computing that provides time series analysis, automatic forecast model generation, automatic variable and event selection, and automatic model selection. SAS Visual Forecasting features a new graphical interface that is centered on the use of pipelines, a new microservices-based architecture, and a new fast, scalable, and elastic in-memory server environment based on SAS^® Cloud Analytic Services (CAS). It provides end-to-end capabilities to explore and prepare data, apply various modeling strategies, compare forecasts, override statistical forecasts, and visualize results. The workflow framework for model generation and forecasting is shared with SAS^® Visual Data Mining and Machine Learning and SAS^® Visual Text Analytics. Forecast analysts and data scientists can also access the power of SAS Visual Forecasting though a flexible and powerful programming environment.

Health care payers constantly work in the world of big data. Because of the insights that big data can provide, payers are increasingly required to use it to improve outcomes for their patients, lower costs, and prevent fraud, waste, and abuse (FWA) in their networks. This enormous task requires contributors from many areas of the organization, such as strategically minded executives, clinicians, auditors, and data scientists. Together, SAS^® Visual Investigator and SAS^® Visual Analytics provide a toolset that enables users to bridge the gaps between the individuals in these different roles. This paper examines how advanced analytics, coupled with visualizations, make complex analytics readily available to subject matter experts across the health care industry.

The SAS^® service layer has been completely re-architected from the ground up in SAS^® Viya^® . The monolithic web application server used in SAS^® 9 has been replaced by modular microservices, which act as building blocks to help form a loosely coupled, scalable, and secure platform. This presentation focuses on the various technologies used within SAS Viya, from service registration to an event delivery architecture model that enables services to communicate with one another. Learn about a few of the key microservices included within SAS Viya and the benefits they provide.

The introduction of SAS^® Viya^® opened SAS^® and its industry-leading machine learning algorithms to the open-source community. With the ability to connect open-source interfaces such as Lua, Python, and R, as well as REST APIs to SAS^® Cloud Analytic Services (CAS), this new engine becomes a powerful tool to include in any analytical toolbox. Using CAS actions, users can easily surface data and analytical model results to a WebApp through REST API connectivity. This capability is one key aspect that SAS Viya offers to provide real-time analytics. REST APIs provide a way to access SAS Viya over HTTP, and CAS capabilities are more efficient in communicating between front-end and back-end applications. Zencos will showcase leveraging the capabilities of the CAS Server and connecting to REST APIs to surface data for real-time decision making using a case study.

You have SAS^® Viya^® installed and running for your business. Everyone loves it so much, they are using it more than you initially anticipated. Fortunately, you had the forethought to architect for high availability. Now the process of adding additional nodes is relatively simple, and you don't have to lose sleep as you keep your growing user community happy. This paper guides you through the process of creating high availability for the SAS Viya services, microservices, and web applications that support it.

Any architecture that is chosen by a SAS^® customer to run their SAS applications has the following requirements: a good understanding of all layers and components of the SAS infrastructure an administrator to configure and manage the infrastructure the ability to meet SAS requirements not to just run the software but to also enable it to perform well This paper discusses important performance considerations for SAS^®9 (both SAS^® Foundation and SAS^® Grid) and SAS^® Viya^®. We give guidance for how to configure the cloud infrastructure to get the best performance with SAS.

The degree of customization required for different kinds of reports and analyses used in presentations, documents, and spreadsheets varies from one organization or department to another. Often the results needed can be achieved by using a point-and-click tool. If that is not possible, then a coded approach is required. The amount of syntax required increases depending on the SAS^® procedure that you choose. The PRINT, TABULATE, and REPORT procedures offer successively greater customization capabilities. However, to achieve complete flexibility, a new tool from SAS called the Report Writing Interface (RWI) is required. In the Output Delivery System RWI, which is new in SAS^® 9.4, you write code in a DATA step, and then combine it with any other kind of DATA step statement to construct complex report structures currently not achievable with other tools. In addition, RWI enables you to incorporate style customization anywhere. The syntax uses a common dot notation that programmers from multiple languages are familiar with. This seminar examines capabilities, examples, advantages, and disadvantages of this latest reporting methodology.

Scalability. Fault tolerance. Load balancing. High performance. High Availability. These are all phrases that commonly show up during analytics infrastructure conversations. However, maybe you are still confused about how all this relates to SAS^® Grid Manager and what it would mean for your organization. When considering SAS Grid Manager, many customers have similar points of confusion: How will their SAS^® workflow change? How do they connect to the grid? How should they manage it, and what do they need to do in order to successfully implement it? In this paper, we cover questions we are asked most often about implementation, administration, and usage of SAS Grid Manager.

Linters are programs that carry out static source code analysis, detecting certain bugs, coding rule deviations, and other issues. Linters also detect redundant or error-prone constructs that are nevertheless, strictly speaking, legal. Potential performance optimizations can also be checked by linters. This paper covers creating a modular linter for the SAS^® language consisting of a parser module, an analysis module that includes a list of rules, and a reporting module that displays issues found. It is possible to include and exclude rules, as well as develop your own rules, all of which makes the linter very flexible for any team with its own list of requirements regarding the source code and programming standards. The parser for SAS language grammar is based on the ANTLR Java parser. The tool is written in Java and SAS, which is why it can be integrated into any SAS environment.

Many organizations need to process large numbers of time series for analysis, decomposition, forecasting, monitoring, and data mining. The TSMODEL procedure provides a resilient, distributed, optimized generic time series analysis scripting environment for cloud computing. It comes equipped with capabilities such as automatic forecast model generation, automatic variable and event selection, and automatic model selection. It also provides advanced support for time series analysis (in the time domain or in the frequency domain), time series decomposition, time series modeling, signal analysis and anomaly detection (for IoT), and temporal data mining. This paper describes the scripting language that supports cloud-based time series analysis. Examples that use SAS^® Visual Forecasting software demonstrate the use of this scripting language.

As statisticians and programmers, SAS^® is part of our daily life. Through assessing patterns, data quality, programming data sets, analysis displays, or developing simulations, we need to determine the best ways to conduct our daily work, enabling us to see the forest for the trees. This paper provides guidance on quality defensive programming, efficient coding, as well as good programming concepts. Programming no nos are also discussed. The concepts discussed will enable us to navigate through the trees; that is, seeing the trees for the forest. We might have been programming in SAS for weeks, months, years, or decades. Regardless, we should continue to expand our skills and continue learning and updating our techniques. With this paper, we provide reminders for paths lost in the past, as well as new tips to help us clear the brush from the trail. This paper is part deux of Defensive Coding by Example (2015), quenching our thirst for adventure in the great SAS hinterland.

With more than 310 million users worldwide, Twitter is an important source for data generation for social media analytics. Each day, Netflix and its competitors within the video on-demand space have thousands of tweets where users share their opinions. By analyzing the content of the tweets, companies can learn more about their customers and their likes and dislikes. It can help make business profitable by tracking how well social media campaigns are paying off and how many leads turn prospects into customers. Analyzing and making sense of this vast amount of unstructured data will help Netflix make better-informed decisions to maintain their competitive edge. In this study, we analyze tweets for Netflix and its competitors such as Amazon Prime Video, Hulu, and HBO Now. We demonstrate the use of multiple SAS^® tools to analyze large numbers of tweets and generate quick summaries, identify different categories of tweets, and classify reviews. Over 32,000 tweets were captured over five days, and SAS^® Enterprise Miner(tm) was used to identify commonly used terms and to categorize similar tweets into groups. SAS Enterprise Miner was used to analyze customer sentiment by classifying reviews into positive, neutral, and negative, based on its content. The sentiment analysis feature in SAS^® Visual Analytics gives a quick overview through word clouds for each text topic, helping us to understand customer opinions and use the information to improve the business.

The video-sharing website YouTube encourages interaction between its users via the provision of a user comments facility. This was originally envisaged as a way for viewers to provide their feedback about the videos, but now it is used for other communicative purposes like sharing ideas, paying tributes, social networking, and answering queries. This study seeks to examine and categorize the types of comments made by YouTube users on popular Hollywood movie trailers to understand how the sentiments of these users can impact first-day revenue. This examination can also show the trend of box office earnings based on the sentiments after the movie is released for the running week. We used the SAS^® Enterprise Miner(tm) rule builder model, which gave us an accuracy of 88%, and scored the YouTube data to get sentiments. Also, SAS^® Text Miner was applied to generate topic clusters and concept links for the comments to give us an idea of what the users liked or disliked in the movie. The results can help distributers and movie makers to determine the response rate for the movie by understanding the comments on the trailers. Once the movie is released, the-next day earnings can be predicted by looking at the present-day sentiments.

Using Agile methods to deliver applications is a commonplace approach these days. But when you try to apply Agile techniques to delivering data, analytics, and visualizations, a whole set of new challenges arise that affect whether you are able to deliver a production-ready solution every 24 weeks. This session takes you through seven repeatable Agile techniques that deliver completed visualizations every three weeks. Shane covers the WHY, the HOW, and the WHAT for each of these steps. At the end of the session, you will have a set of artifacts that you can start to leverage in your next project. These steps have been discovered, defined, and refined by Shane over the last four years, based on a number of AgileBI customer projects in New Zealand (the land of hobbits and kiwifruit). The seven Agile methods covered in this session are: Defining information products to set the scope Modelstorming business events to identify the data requirements Applying Agile data modeling techniques to structure data quickly Wireframing to gather visualization requirements Using Acceptance Test-Driven Development to ensure that you build it right Delivering three visualization iterations in three weeks Developing the T-shaped skills required to build an AgileBI team.

Stockholm County Council (SCC) was into nudging long before it won professor Richard Thaler a Nobel Prize. SCC needed to find out if it was possible to nudge its population to go to the emergency department (ED) at a less variable rate, as this would improve the SCCs resource planning, shorten ED queues exponentially, and improve health outcomes at a lower cost for the entire population. Paradoxically, the demand for emergency healthcare does not vary randomly over time even though accidents and diseases (to a high extent) do. This is an effect of individual and social traits of the potential ED patientsthat is, the population of a region. In the EDs, days after holidays or Mondays are usually busy, while Christmas Eve and Friday lunch are not. While this pattern is stable throughout weeks, months, and years, the extent of busyness and calmness is often not known. If the public received information about estimated patient load at the EDs across the county, they would have the opportunity of attending an ED with lower load, which naturally is in their interest. By using SAS^®, we use an ensemble of a wide array of models to forecast expected ED visits for the next 72 hours in several hospitals in Stockholm, Sweden. The models are analyzed, evaluated, and compared. The forecasts are useful for resource allocation in the EDs and to nudge future ED visitors to go to the right ED at the right time.

DS2s latest packages, PCRXFIND and PCRXREPLACE, wrap the functionality of previous regular expression functions into sleek new packages. These just-in-time compiled regular expressions (or RegEx) can be used in multi-threaded environments to maximize throughput, while the object-oriented APIs simplify your connection to the powerful world of RegEx. Practical examples showcase using the new packages to execute RegEx on large data sets and include tips and techniques to get the most out of RegEx in distributed environments. We take a look at analyzing and filtering data sets using the new packages, as well as using the fundamentals of text analytics to make processing large jobs faster. To top things off, we do the work with whichever smiling emoji best suits you, since the new packages are ready to handle all of your social media and international text-handling needs.

While a Pearson correlation coefficient can be a quick and easy measure to compare two measurement methods or examine repeatability, it is not the most appropriate nor does it give you insight into bias. Performing linear regression or tests for differences between the means is also not the best approach for determining whether two methods are comparable or a measurement is repeatable. Examining the difference between the measurements might not offer insight into the accuracy of the methods, and a Pearson correlation coefficient is not a measure of agreement but a measure of association. Altman and Bland (1983) suggested a graphical method and two statistical tests to examine repeatability of measurement or whether two measurement methods produced similar results. The graphical method, called a Bland-Altman plot, is a plot of the difference versus the average of two different measures with y-reference lines at two standard deviations (SD) or three SD limits of the difference. A Bland-Altman plot allows for assessment of the magnitude of disagreement, both error and bias. The statistical tests suggested by Altman and Bland are a test for zero bias and a test of independence of the bias (difference between the methods) and magnitude (average of the methods) of the measure. A systolic blood pressure example is used to show how to perform the statistical tests and create the Bland-Altman plot using SAS/STAT^® PROC TTEST, Base SAS^® PROC CORR, and ODS Statistical Graphics SGPLOT.

Nearly all data is associated with some date information. In many industries, a SAS^® programmer comes across various date formats in data that can be of numeric or character type. An industry like pharmaceuticals requires that dates be shown in ISO 8601 formats due to industry standards. Many SAS programmers commonly use a limited number of date formats (like YYMMDD10. or DATE9.) with workarounds using scans or substring SAS functions to process date-related data. Another challenge for a programmer can be the source data, which can include either date/time or only date information. How should a programmer convert these dates to the target format? There are many existing date formats (like E8601 series, B8601 series, and so on) and informants available to convert the source

Kamleshkumar Patel, Jigar Patel, Dilip Patel, Vaishali Patel, Rang Technologies