Search Results: simulation (468)

Data Management
Jim Harris 0
Data steward is a tough role to play

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

Rick Wicklin 0
Resampling and permutation tests in SAS

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

Rick Wicklin 0
Fat-tailed and long-tailed distributions

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.

Leo Sadovy 0
Activity-Based Business Process Reengineering

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

Rick Wicklin 0
Simulating data for a logistic regression 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

Rick Wicklin 0
Never multiply with a large diagonal matrix

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
Leo Sadovy 0
Agility and the Analytic Sandbox

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

Rick Wicklin 0
Where's Rick at SAS Global Forum 2014?

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

Rick Wicklin 0
Sample with replacement in SAS

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

Learn SAS
Rick Wicklin 0
How to vectorize time series computations

Vector languages such as SAS/IML, MATLAB, and R are powerful because they enable you to use high-level matrix operations (matrix multiplication, dot products, etc) rather than loops that perform scalar operations. In general, vectorized programs are more efficient (and therefore run faster) than programs that contain loops. For an example

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