The Kolmogorov-Smirnov (K-S) test is one of the most useful and general nonparametric methods for comparing two samples. It is sensitive to all types of differences between two populations (shift, scale, shape, and so on). In this paper, we will present a thorough investigation into the K-S test including, derivation of the formal test procedure, practical demonstration of the test, large sample approximation of the test, and ease of use in SAS^® using the NPAR1WAY procedure.

In our previous work, we often needed to perform large numbers of repetitive and data-driven post-campaign analyses to evaluate the performance of marketing campaigns in terms of customer response. These routine tasks were usually carried out manually by using Microsoft Excel, which was tedious, time-consuming, and error-prone. In order to improve the work efficiency and analysis accuracy, we managed to automate the analysis process with SAS^® programming and replace the manual Excel work. Through the use of SAS macro programs and other advanced skills, we successfully automated the complicated data-driven analyses with high efficiency and accuracy. This paper presents and illustrates the creative analytical ideas and programming skills for developing the automatic analysis process, which can be extended to apply in a variety of business intelligence and analytics fields.

With the growth in size and complexity of organizations investing in SAS^® platform technologies, the size and complexity of ETL subsystems and data integration (DI) jobs is growing at a rapid rate. Developers are pushed to come up with new and innovative ways to improve process efficiency in their DI jobs to meet increasingly demanding service level agreements (SLAs). The ability to conditionally execute or switch paths in a DI job is an extremely useful technique for improving process efficiency. How can a SAS^® Data Integration developer design a job to best suit conditional execution? This paper discusses a technique for providing a parameterized dynamic execution custom transformation that can be easily incorporated into SAS^® Data Integration Studio jobs to provide process path switching capabilities. The aim of any data integration task is to ensure that all sources of business data are integrated as efficiently as possible. It is concerned with the repurposing of data via transformation, should be a value-adding process, and also should be the product of collaboration. Modularization of common or repeatable processes is a fundamental part of the collaboration process in DI design and development. Switch path a custom transformation built to conditionally execute branches or nodes in SAS Data Integration Studio provides a reusable module for solving the conditional execution limitations of standard SAS Data Integration Studio transformations and jobs. Switch Path logic in SAS Data Integration Studio can serve many purposes in day-to-day business needs for a SAS data integration developer as it is completely reusable

Given a time series data set, you can use automatic time series modeling software to select an appropriate time series model. You can use various statistics to judge how well each candidate model fits the data (in-sample). Likewise, you can use various statistics to select an appropriate model from a list of candidate models (in-sample or out-of-sample or both). Finally, you can use rolling simulations to evaluate ex-ante forecast performance over several forecast origins. This paper demonstrates how you can use SAS^® Forecast Server Procedures and SAS^® Forecast Studiosoftware to perform the statistical analyses that are related to rolling simulations.

Many organizations need to forecast large numbers of time series that are organized in a hierarchical fashion. Good forecasting practices recommend that several hierarchies be used and that each hierarchy contain a homogeneous set of time series with similar statistical properties. Modeling and forecasting homogeneous time series hierarchies provide better out-of-sample forecast performance. Because an organization might have many time series hierarchies, it is often desirable to model and forecast these hierarchical time series in parallel for computational efficiency. Additionally, it is often desirable to aggregate forecasts from several nonhomogeneous time series hierarchies for report generation. This paper demonstrates these techniques for forecasting time series hierarchies in parallel and for aggregating the forecasts by using SAS^® Forecast Server and SAS^® Grid Manager.

Portfolio segmentation is key in all forecasting projects. Not all products are equally predictable. Nestl uses animal names for its segmentation, and the animal behavior translates well into how the planners should plan these products. Mad Bulls are those products that are tough to predict, if we don't know what is causing their unpredictability. The Horses are easier to deal with. Modern time series based statistical forecasting methods can tame Mad Bulls, as they allow to add explanatory variables into the models. Nestl now complements its Demand Planning solution based on SAP with predictive analytics technology provided by SAS^®, to overcome these issues in an industry that is highly promotion-driven. In this talk, we will provide an overview of the relationship Nestl is building with SAS, and provide concrete examples of how modern statistical forecasting methods available in SAS^® Demand-Driven Planning and Optimization help us to increase forecasting performance, and therefore to provide high service to our customers with optimized stock, the primary goal of Nestl 's supply chains.

The role of the Data Scientist is the viral job description of the decade. And like LOLcats, there are many types of Data Scientists. What is this new role? Who is hiring them? What do they do? What skills are required to do their job? What does this mean for the SAS^® programmer and the statistician? Are they obsolete? And finally, if I am a SAS user, how can I become a Data Scientist? Come learn about this job of the future and what you can do to be part of it.

Retail price setting is influenced by two distinct factors: the regular price and the promotion price. Together, these factors determine the list price for a specific item at a specific time. These data are often reported only as a singular list price. Separating this one price into two distinct prices is critical for accurate price elasticity modeling in retail. These elasticities are then used to make sales forecasts, manage inventory, and evaluate promotions. This paper describes a new time-series feature extraction utility within SAS^® Forecast Server that allows for automated separation of promotional and regular prices.

SAS^® High-Performance Analytics is a significant step forward in the area of high-speed, analytic processing in a scalable clustered environment. However, Big Data problems generally come with data from lots of data sources, at varying levels of maturity. Teradata s innovative Unified Data Architecture (UDA) represents a significant improvement in the way that large companies can think about Enterprise Data Management, including the Teradata Database, Hortonworks Hadoop, and Aster Data Discovery platform in a seamless integrated platform. Together, the two platforms provide business users, analysts, and data scientists with the ideally suited data management platforms, targeted specifically to their analytic needs, based upon analytic use cases, managed in a single integrated enterprise data management environment. The paper will focus on how several companies today are using Teradata s Integrated Hardware and Software UDA Platform to manage a single enterprise analytic environment, fight the ongoing proliferation of analytic data marts, and speed their operational analytic processes.

The use, limits, and misuse of statistical models in different industries are propelling new techniques and best practices in forecasting. Until recently, many factors such as data collection and storage constraints, poor data synchronization capabilities, technology limitations, and limited internal analytical expertise have made it impossible to forecast intermittent demand. In addition, integrating consumer demand data (that is, point-of-sale [POS]/syndicated scanner data from ACNielsen/ Information Resources Inc. [IRI]/Intercontinental Marketing Services [IMS]) to shipment forecasts was a challenge. This presentation gives practical how-to advice on intermittent forecasting and outlines a framework, using multi-tiered causal analysis (MTCA), that links demand to supply. The framework uses a process of nesting causal models together by using data and analytics.

PROC TABULATE is a powerful tool for creating tabular summary reports. Its advantages, over PROC REPORT, are that it requires less code, allows for more convenient table construction, and uses syntax that makes it easier to modify a table s structure. However, its inability to compute the sum, difference, product, and ratio of column sums has hindered its use in many circumstances. This paper illustrates and discusses some creative approaches and methods for overcoming these limitations, enabling users to produce needed reports and still enjoy the simplicity and convenience of PROC TABULATE. These methods and skills can have prominent applications in a variety of business intelligence and analytics fields.

In healthcare, we often express our analytics results as being adjusted . For example, you might have read a study in which the authors reported the data as age-adjusted or risk-adjusted. The concept of adjustment is widely used in program evaluation, comparing quality indicators across providers and systems, forecasting incidence rates, and in cost-effectiveness research. In order to make reasonable comparisons across time, place, or population, we need to account for small sample sizes and case-mix variation in other words, we need to level the playing field and account for differences in health status and for uniqueness in a given population. If you are new to healthcare. What it really means to adjust the data in order to make comparisons might not be obvious. In this paper, we explore the methods by which we control for potentially confounding variables in our data. We do so through a series of examples from the healthcare literature in both primary care and health insurance. In this survey of methods, we discuss the concepts of rates and how they can be adjusted for demographic strata (such as age, gender, and race), as well as health risk factors such as case mix.

This workshop provides hands-on experience using SAS^® Forecast Server. Workshop participants will do the following: create a project with a hierarchy generate multiple forecasts automatically evaluate the accuracy of the forecasts build a custom model

Volatility estimation plays an important role in the elds of statistics and nance. Many different techniques address the problem of estimating volatility of nancial assets. Autoregressive conditional heteroscedasticity (ARCH) models and the related generalized ARCH models are popular models for volatility. This talk will introduce the need for volatility modeling as well as introduce the framework of ARCH and GARCH models. A brief discussion about the structure of ARCH and GARCH models will then be compared to other volatility modeling techniques.

Expensive physical capital must be regularly maintained for optimal efficiency and long-term insurance against damage. The maintenance process usually consists of constantly monitoring high-frequency sensor data and performing corrective maintenance when the expected values do not match the actual values. An economic system can also be thought of as a system that requires constant monitoring and occasional maintenance in the form of monetary or fiscal policy. This paper shows how to use the SSM procedure in SAS/ETS^® to make forecasts of expected values by using high-frequency multivariate time series. The paper also demonstrates the functionality of the new SASEFRED interface engine in SAS/ETS.