SCSUG 2019 Papers - Index

21 papers. Source: SCSUG (South-Central SAS Users Group) conference proceedings.

go 2018

#	Title ×	Authors ×	Abstract ×	Keywords ×	Extras	Pages
1	20 in 20: Quick Tips for SAS Enterprise Guide Users	Kelly Gray	There are many time-saving and headache saving tips and tricks you can use to make working in SAS Enterprise Guide a breeze. Did you know that you can change your layout so that you can see your code and your results at the same time? You will learn 20 tips and tricks for working in SAS Enterprise Guide in 20 minutes. One tip per minute, and out of the twenty you are guaranteed to find at least one nugget that will make your life easier	sas enterprise guide, macro variable	source	14
2	A Simple Method of Creating Custom Excel Reports	Jimmy A. DeFoor	This paper will show the reader how to create formatted `EXCEL`-based reports without using SAS Styles. It will use the technique of creating a report-structured spreadsheet that imports data from a data-structured spreadsheet. The SAS coding methods shown in this paper will include: 1) Using the Types statement in a `PROC SUMMARY` to create line totals and report totals. 2) Creating user formats to provide descriptions for each report line, including minus ('-') signs that indicate the line should be subtracted from a prior line. 3) Building a dummy data set with every possible line in the report so that the report would always have a fixed number of columns and rows. 4) Establishing a macro that would create the same report structure for the three different summaries of the source data. 5) Exporting reports to different worksheets in the same spreadsheet using `PROC EXPORT` with the sheet option. 6) Cleaning up existing spreadsheets and copying spreadsheets using the Call System option	excel, proc step, proc means, macro variable		16
3	Building a Neural Network Scoring Model with JMP and Microsoft SQL Server to predict Aggregate Motor Vehicle Driver Alcohol Impairment	David Whitchurch	According to a predictive model used by the National Highway Traffic Safety Administration, fatalities in crashes where at least one motor vehicle driver was alcohol-impaired (BAC >= 0.08%) accounted for 28% of all motor vehicle crash fatalities in 2017. While there has been a decline in this percentage since 2013, there is still much work to be done in reducing the number of fatalities in motor vehicle crashes involving one or more impaired drivers. Since many drivers in motor vehicle crashes are not tested for blood alcohol content, predictive models are used to estimate the number who are under the influence of alcohol at the time of a crash. Louisiana currently uses an "alcohol-involved" model, which predicts whether a driver involved in a motor vehicle crash had BAC of 0.02% or greater. While this model is useful for highway safety education and awareness efforts, a separate model is needed for predicting driver alcohol impairment (BAC of 0.08% or greater)	neural network, network optimization, data cleaning, sql server, proc sql, bootstrap, validation set, data warehouse, type ii error, type i error, binary variable, feature selection, confusion matrix, training set	source	10
4	Can Life Style Change Prevent Diabetes?	Mounica Mandapati	Food defines our life. Healthy food enhances human life and enables people to avoid some preventable chronic diseases. Diabetes being one among them. According to CDC, diabetes rates have exponentially increased by a staggering 650% during the past 50 years. Diabetes is usually blamed on people's genetics but not on their food environment and lifestyle. So, the main objective of this study is to find out the important food environment factors and lifestyle factors responsible for causing diabetes and predict the risks of the increase in diabetes population. For the purpose of this study, the data has been collected from the United States Department of Agriculture. At present, the data set contains 275 variables and 3,144 observations. To explain, predict and visualize the data SAS Enterprise Miner will be used. According to the results, there is an increase of diabetes rates in 85% counties with a maximum increase of 81% of the population from Roosevelt County, New Mexico	correlation, proc hpsplit, response variable, data mining, excel	source	11
5	Customizing ODS Graphics to Publish Visualizations	Paul Montagna	The procedures provided in the `ODS` graphics package, such as SGPANEL, `SGPLOT`, and SGSCATTER make creating outstanding visualizations much easier than the original `GPLOT` procedure. They also provide great flexibility to create complex graphics with ease. However, it is a common desire to use something other than the default settings, sometimes we desire to tweak a plot, and often we need versions that are of publishable quality (i.e., a specific resolution, size, or restricted colors). All these requirements call for customization. This presentation describes several ways to customize `ODS` graphics plots including: using `ODS` options, altering styles using the %MODSTYLE macro, using `PROC FORMAT`, and some little know OPTION statements with the graphics procedures	proc step, proc sgplot		9
6	Do you know when your data is lying to you? The HOW of Regression Analysis with Quantitative and Qualitative Variables	Steven C. Myers	A qualitative indicator such as a binary variable, D, may describe a population difference, such as 'male' and 'female,' or 'before' and 'after' some event. This workshop will walk you through the regression analysis of whether an outcome variable, Y, is influenced by a qualitative binary event, D. Using only 14 years of data on Y, you will learn that what seems a very simple actually takes 8 different regressions and many Wald statistical tests to reveal that the best conclusion requires complexity of model specification and a strategy of statistical inference. The take away is no matter how sophisticated the technique and how good the data, there is no substitute for thinking your way through a problem. Blindly following technique alone is a bad practice that leads you to make huge mistakes. You will learn the value of articulating a problem, preparing data, exploring the data and the importance of the data generating process	regression analysis, proc reg, sample, proc step, null hypothesis, binary variable, estimate	source	20
7	Essential SAS Coding Techniques for Gaining Efficiency	Kirk Paul Lafler, Clarence W. Jackson	The Base-SAS software provides users with many choices for accessing, manipulating, analyzing, and processing data and results. Partly due to the power offered by the SAS software and the size of data sources, many application developers, programmers and end-users are in need of guidelines for more efficient use. This presentation highlights essential SAS coding techniques for helping to gain efficiency. Attendees learn DATA and `PROC STEP` language statements and options to help conserve CPU, I/O, data storage, and memory resources while accomplishing tasks involving processing, sorting, grouping, joining (merging), and summarizing data	proc step, proc sql, options statement, cpu time, dataset, where statement, dictionary tables	source	11
8	Explore your data before you rush to analysis, you will thank me later: explorations in cross section data	Steven C. Myers	Economists, business leaders and analysts spend a great deal of time analyzing structur ed cross sectional data. This paper is an introduction to exploratory data analysis for economic and business data analytic students in an introductory course in economics to teach data handling and SAS programming and features SAS PROCS MEANS, UNIVARIATE, `SGPLOT`, FREQ, TABULATE, CORR, TTEST and REG. A data set on rents paid is used to illustrate the solution of the problem: do female students pay higher rents than male students? It is essential to learn all you can about your data before rushing to analysis, yet analysts typically rush to more advanced and fancier techniques by ignoring or giving only cursory concern to the underlying data. In this paper we show how to ground the analysis in a firm understanding of the data generating process and suggest many ways to learn about the under lying data	proc step, regression analysis, correlation, sample, outlier, standard deviation, proc reg, null hypothesis, proc corr, proc means, histogram, box plot, binary variable, proc freq, proc sgplot, proc ttest, cross-sectional data, proc tabulate, partial correlation, format statement, proc univariate, degrees of freedom, normal distribution, central tendency, data science, response variable, panel data, estimate, sample size	source	28
9	Exploring the MSGLEVEL= SAS System Option and PROC SQL _METHOD Option	Kirk Paul Lafler	The `SQL` Procedure contains powerful options for users to take advantage of. This paper explores the fully supported _METHOD option as an applications development and tuning tool to provide an understanding of the processing hierarchy (or execution plan) of any SQL query	proc sql, proc step, sas log, options statement, where statement, error message, data merging	source	5
10	How to Land Work as a SAS Professional	Charu Shankar	"By 2018, the United States alone could face a shortage of 140,000 to 190,000 people with deep analytical skills as well as 1.5 million managers and analysts with the know-how to use the analysis of big data to make effective decisions" (McKinsey Global Institute report, May 2011). This situation represents a 50-60% gap between the supply and demand of people with deep analytical talent. Thus, it must come as no surprise that the question the author is asked most in the SAS classroom is how to find SAS work. The author is inundated daily with resumes, requests to connect on LinkedIn, and offers of coffee, tea, or dinner. SAS is a highly valued skill, and everyone wants to know what steps to take to land work. Time magazine rated SAS as the Number 1 career skill in the data world. SAS is also the #1 skill for landing a bigger paycheck according to this study	sas programming, sample	source	16
11	Introduction to Data-Driven Programming Using SAS for Beginners	Kirk Paul Lafler	Data-driven programming, or data oriented programming (DOP), is a specific programming paradigm where the data, and/or data structures, control the flow of a program and not the program logic. Often, data-driven programming approaches are applied in organizations with structured and unstructured data for filtering, aggregating, transforming and calling other programs. This paper and presentation explores several data-driven programming techniques that are available to SAS users	dictionary tables, proc step, proc sql, excel, proc contents, call execute, ods excel, sas library, sas log, output delivery system, macro facility, macro variable, dataset, select, character variable, proc format, proc print, options statement, user-defined format, proc freq	source	23
12	Making Your SAS Results More Meaningful with Color	Kirk Paul Lafler	Color can help make your SAS results more meaningful. Instead of producing boring and ineffective results, users are able to enhance the appearance of their output, documents, reports, tables, charts, statistics, and spreadsheets to highlight and draw attention to important data elements, details, and issues, including using color in headings, subheadings, footers, minimum and maximum values, ranges, outliers, special conditions, and other elements. Color can be added to text, foreground, background, rows, columns, cells, summaries, totals, and traffic lighting scenarios. Topics include using color to results, documents, reports, tables, charts and spreadsheets can be enhanced with color, effectively add color to PDF, RTF, HTML, and `EXCEL` spreadsheet results using `PROC PRINT`, `PROC FREQ`, `PROC REPORT`, `PROC TABULATE`, and `PROC SGPLOT` and Output Delivery System (`ODS`) with style	bar chart, proc sgplot, proc step, histogram, proc sgpanel, excel, scatter plot, vertical bar chart, box plot, bubble plot, proc freq, series plot, proc report, data science, class variable, correlation, outlier	source	20
13	Pattern Analysis and Predictive Policing Using Socio-Economic Factors	Trishla Mishra	According to a recent survey, crime rate in USA has gone down by 2% in the country. Police Department in some of the largest cities such as Los Angeles have started using crime -prediction techniques which is partly the reason why crime rates have declined. Currently, these departments use information related to crime such as day/time of occurrence, premise etc. for prediction. This paper aims at inducing socio -economic factors such as total population, median income, median age, unemployment etc. to check t heir influence and predict crimes based on the same. To do so, LosAngeles crime data was collected from catalog.data.gov. It was merged with the socio - economic data obtained from American FactFinder. Tab leau Public 18.3 was used to explore the dataset. With initial exploration, it was found that the most frequently occurring crime is battery - simple assault. Also, 77th street turned out to be most unsafe location	proc hpforest, proc freq, proc hpsplit, association rules, data mining, logistic regression, sample, apriori, logistic distribution, regression analysis	source	12
14	Running Parts of a Program while Preserving the Entire Program	Stephen B. Sloan	The Challenge: We have long SAS programs that accomplish a number of different objectives. We often only want to run 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: 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. We don't want to recreate 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. We only need some of the results from the program currently, but we want to preserve the entire program. We want to test new scenarios that only require subsets of the program	proc step, run, macro variable, sas enterprise guide	source	4
15	SAS Macro Programming Essentials for New Users	Kirk Paul Lafler	The SAS Macro Language is a powerful tool for extending the capabilities of the SAS System. This hands-on workshop teaches essential macro coding concepts, techniques, tips and tricks to help beginning users learn the basics of how the Macro language works. Using a collection of proven Macro Language coding techniques, attendees learn how to write and process macro statements and parameters; replace text strings with macro (symbolic) variables; generate SAS code using macro techniques; manipulate macro variable values with macro functions; create and use global and local macro variables; construct simple arithmetic and logical expressions; interface the macro language with the `SQL` procedure; store and reuse macros; troubleshoot and debug macros; and develop efficient and portable macro language code	macro variable, macro facility, proc step, autocall macro, macro definition, macro parameter, %macro parameters, macro function, proc sql, dictionary tables, options statement, macro loop, &variable	source	10
16	Strategizing P2P Investments using Socio-Economic Factors	Priyabrata Thatoi	A traditional banking counterpart, Lending Club is an online market place for borrowers and lenders and has become a major player in the peer -to-peer lending business with an average rate of return between 3% and 8% per year. Lenders, before committing their money, carefully investigate a multitude of associated risks such as borrower defaults, liquidity risk, poor loan diversification, etc. But for a steady return, lenders are advised to diversify their portfolio by investing in different loans with differ ent rate of interest. Also, several studies have been conducted with the help of sophisticated machine learning algorithms and traditional credit risk modeling or credit scoring to understand the potential indicators to loan default, which is a major cause of poor return. Most of these modeling techniques utilize collected borrower's personal, professional and credit information	data mining, interest rate, default rate, default	source	12
17	Top SAS Architecture Pitfalls: Lessons Learned the Hard Way	Margaret A. Crevar	Are you a SAS infrastructure architect? Do you want to know what has worked for other SAS customers and what things should be avoided? Attend this discussion to learn from others' experiences with SAS. We also encourage you to share your experiences with us	sas viya, raw data	source	3
18	Twenty Ways to Run Your SAS Program Faster and Use Less Space	Stephen B. Sloan	When running SAS programs that use large amounts of data or have complicated algorithms, we often are frustrated by the amount of time it takes for the programs to run and by the large amount of space required for the program to run to completion. Even experienced SAS programmers sometimes run into this situation, perhaps through the need to produce results quickly, through a change in the data source, through inheriting someone else's programs, or for some other reason. This paper outlines twenty techniques that can reduce the time and space required for a program without requiring an extended period of time for the modifications	proc step, dataset, numeric variable, character variable, proc sort, proc means, by group processing, proc append	source	6
19	Uplift Modeling in Retail Marketing using SAS	Aashish Arjan Bugalia, Agastya Komarraju, Miriam McGaugh	Direct marketing campaigns are often targeted to randomly selected customers which results in huge costs for the company. Also, such campaigns result in customer frustration and makes them less likely to react to further communication s in future. Companies involved in direct marketing campaigns often use a random response model to target customers for the campaigns . An alternative approach can be through uplift modeling - precise targeting of the beneficial customers resulting in greater return on invested money and resources by the company . Based on the marketing and finance literature, this article looks at high prediction accuracy for the probability of purchase based on a sample of customers, to whom a pilot campaign has been sent. Uplift modeling analyzes the causal effect of an action such as a marketing camp aign on a given individual by considering difference in response rate between a treated group and a randomized control group	neural network, data mining, network optimization, roc curve, proc hpsplit, champion model, sample	source	15
20	Using SAS PROC SQL to Support a Build Combinations Tool to Support Modularity	Stephen B. Sloan	With SAS `PROC SQL` we can use a combination of a manufacturing Bill of Materials and a sales specification document to calculate the total number of configurations of a product that are potentially available for sale. This will allow the organization to increase modularity and reduce cost while focusing on the most profitable configurations with maximum efficiency. Since some options might require or preclude other options, the result is more complex than a straight multiplication of the numbers of available options. Through judicious use of `PROC SQL`, we can maintain accuracy while reducing the time, space, and complexity involved in the calculations	proc sql, proc step, excel	source	7
21	Working with Customer Contact Data	Jon Purvis	Customer contact info is critical to success, but as received often contains inaccuracies and may be in different formats. Contact data also often does not have all the fields we would like to have; e.g., the county or metropolitan statistical area is not recorded. Before running any analysis or ETL processes, significant manipulation of the data may need to occur. SAS DATAFLUX is designed for this purpose, but many do not have access to it. In this paper I describe numerous SAS B ASE `DATA STEP` coding techniques that I use to explore, clean, and format messy contact data. In addition, code to create some commonly missing fields is provided. Data Structure Documentation of the data does not always exist, or may be out of date. And just because a field was supposed to be used in a certain way, does not mean it was used as intended. Every new project should start by looking at the data structures and examining the data so that you know what you have	proc step, proc freq, proc sql, outlier, proc means	source	9

Back to Conf go 2018

This site serves as a convenient hosting location and does not work as a publisher. All copyrights belong to their respective owners.

The information on this site is provided for general reference purposes only. Whilst every effort is made to ensure that the site is up to date and accurate, no warrant, nor any responsibility or liability for, the accuracy or completeness of the content or for any loss which may arise from reliance on information contained in this site is provided. Links to other sites are provided for your convenience but no responsibility or liability for the content of those sites or of any external site which links to this site is accepted. References to any specific commercial product, process, service, or corporation are also for information only, and do not constitute endorsement, recommendation or favoring.

SAS is a registered trademark of SAS Institute Inc. SAS, Statistical Analysis System, and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.