I began 2015 by compiling a list of popular articles from my blog in 2014. Although this "People's Choice" list contains many interesting articles, some of my favorites did not make the list. Today I present the "Editor's Choice" list of articles that deserve a second look. I've highlighted one
The Lancaster Centre for Forecasting is seeking Master Student Projects in Forecasting, Data Mining, or Analytics for summer of 2015. Projects normally run from mid-May to mid-August, with reports issued a few weeks after. These projects are a cost efficient way for a company to carry out analytical work by
Last week, I spoke to Suneel Grover, senior solutions architect for digital intelligence at SAS, about how better data and integration can drive improvements in web analytics. This week I wanted to see how this could be applied for a hospitality company. We decided to tackle a frequently debated topic in
In my previous post I explained that even if your organization does not have anyone with data steward as their official job title, data stewardship plays a crucial role in data governance and data quality. Let’s assume that this has inspired you to formally make data steward an official job title. How
I generally don’t use this blog to air my personal experiences, but recent events have reminded me of a few things that I think would benefit our Analytic Hospitality Executives and their organizations to also be reminded of. This past week, I took my fifth trip to Asia this year.
I've written about how to generate a sample from a multivariate normal (MVN) distribution in SAS by using the RANDNORMAL function in SAS/IML software. Last week a SAS/IML programmer showed me a program that simulated MVN data and computed the resulting covariance matrix for each simulated sample. The purpose of
The 2014 INFORMS Annual Conference in San Francisco was quite a success. Record attendance, diverse program, great city, lovely weather: who can ask for more? SAS and, in particular, SAS/OR was well-represented with a number of talks in all areas of operations research. Here is a somewhat arbitrary selection, please click
My colleagues at the SAS & R blog recently posted an example of how to program a permutation test in SAS and R. Their SAS implementation used Base SAS and was "relatively cumbersome" (their words) when compared with the R code. In today's post I implement the permutation test in
Last week I had the opportunity to attend the INFORMS Annual Meeting in San Francisco. For those of you not familiar with this organization or conference, the Institute for Operations Research and the Management Sciences (INFORMS) is the largest society in the world for professionals in the field of operations
The INFORMS 2014 Annual Meeting will be held In San Francisco from November 9-12; conference hotels are the Hilton San Francisco and the Parc 55 Wyndham. More than 35 SAS staff will participate, and SAS will have three adjacent booths representing SAS/OR (and all of Advanced Analytics), JMP, and the SAS
Volatility. It’s a business reality for energy market participants and it’s been a wild ride for the oil marketing business over the past few weeks. How has your energy risk data helped you navigate the recent increase in volatility and precipitous price drop? This month, we are launching a recurring
Welcome to the blog “Operations Research with SAS: Optimize, Simulate, Understand.” For those of you without an operations research background, a brief explanation: operations research (OR) is the study of the operations of systems, with a focus on making them function more efficiently and more effectively. The overall goal of
The tail of a probability distribution is an important notion in probability and statistics, but did you know that there is not a rigorous definition for the "tail"? The term is primarily used intuitively to mean the part of a distribution that is far from the distribution's peak or center.
It is a mild summer evening in July at Lake Neusiedl here in Austria. The participants of the traditional YES Cup Regatta are sitting with beer and barbecue chops on the terrace of our clubhouse. The mood is relaxed, and everyone wants to tell their story after two eventful races.
The SAS/IML language is used for many kinds of computations, but three important numerical tasks are integration, optimization, and root finding. Recently a SAS customer asked for help with a problem that involved all three tasks. The customer had an objective function that was defined in terms of an integral.
Es ist ein lauer Sommerabend im Juli am Neusiedler See. Die Teilnehmer der traditionellen YES-Cup Regatta sitzen bei Bier und Grillkotelette auf der Terrasse unseres Clubhauses. Die Stimmung ist ausgelassen, jeder hat nach zwei ereignisreichen Wettfahrten seine Geschichte zu erzählen. Eine Unterhaltung am Ende unseres Tisches zieht meine Aufmerksamkeit auf
This blog is a continuation of an earlier blog entitled “To grid or not to grid?” In that blog, one of the reasons to say “yes to SAS Grid” is to see if you can gain some performance improvements from modifying your existing SAS processes by converting them to a
I want to use SAS’ recent announcement of our Cost and Profitability Management solution as an opportunity to highlight an often overlooked but valuable application of activity-based costing: business process reengineering. But first, just a brief description of Cost and Profitability Management’s new breakthrough capability: In-memory model calculation. SAS’ decision
My last blog post showed how to simulate data for a logistic regression model with two continuous variables. To keep the discussion simple, I simulated a single sample with N observations. However, to obtain the sampling distribution of statistics, you need to generate many samples from the same logistic model.
In my book Simulating Data with SAS, I show how to use the SAS DATA step to simulate data from a logistic regression model. Recently there have been discussions on the SAS/IML Support Community about simulating logistic data by using the SAS/IML language. This article describes how to efficiently simulate
A colleague asked me an interesting question: I have a journal article that includes sample quantiles for a variable. Given a new data value, I want to approximate its quantile. I also want to simulate data from the distribution of the published data. Is that possible? This situation is common.
I've pointed out in the past that in the SAS/IML language matrices are passed to modules "by reference." This means that large matrices are not copied in and out of modules but are updated "in place." As a result, the SAS/IML language can be very efficient when it computes with
In 1990, the North Carolina General Assembly created the Sentencing and Policy Advisory Commission to evaluate sentencing laws and policies and recommend any modifications necessary to achieve policy goals. As part of the mandate, the General Assembly required the Sentencing Commission to develop a correctional population simulation model. The model
I love working with SAS Technical Support because I get to see real problems that SAS customers face as they use SAS/IML software. The other day I advised a customer how to improve the efficiency of a computation that involved multiplying large matrices. In this article I describe an important
Analytics gives us not just the ability but the imperative to separate our planning activities into two distinct segments – detailed planning that leads to budgets in support of execution, and high-level, analytic-enabled business/scenario planning. My critique of Control Towers in this blog last time led me not only to
The volume is being turned up on the Control Tower approach to running a business; I have recently been introduced to logistics control towers, supply chain control towers and operations control towers just for starters. I’m sure there must be at least a half dozen more out there – pick
Once again I'll be at SAS Global Forum this year. The 2014 location is Washington, D. C., so I am looking forward to greeting many friends in the government and consulting sectors. I always enjoy talking with SAS customers about statistics, simulations, matrix computations, and the SAS/IML product, so here's
I had the opportunity to moderate a roundtable discussion on risk management at the International Institute for Analytics’ (IIA) winter symposium in Orlando earlier this month. I set the stage for the session with a brief overview of my favorite risk approach, “Competing on Value”, by Mack Hannan and Peter
Randomly choosing a subset of elements is a fundamental operation in statistics and probability. Simple random sampling with replacement is used in bootstrap methods (where the technique is called resampling), permutation tests and simulation. Last week I showed how to use the SAMPLE function in SAS/IML software to sample with
I began 2014 by compiling a list of 13 popular articles from my blog in 2013. Although this "People's Choice" list contains many articles that I am proud of, it did not include all of my favorites, so I decided to compile an "Editor's Choice" list. The blog posts on
