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As he watched me unpack yet another Amazon delivery, my husband decided to do an intervention. Scattered around me on the floor were solar panels, water purification tablets, tarps, hunting knives and enough batteries to power New York City for many weeks. It was 2019 and hurricane season was upon
SAS' Leonid Batkhan reveals how to change lengths for all character variables in a data set and all data sets in a data library to facilitate data migration to Unicode encoding environment.
A previous article about standardizing data in groups shows how to simulate data from two groups. One sample (with n1=20 observations) is simulated from an N(15, 5) distribution whereas a second (with n2=30 observations) is simulated from an N(16, 5) distribution. The sample means of the two groups are close
Hidden Markov Models Introduction Statistical models of hidden Markov modeling (HMM) have become increasingly popular in the last several years. The models are very rich in mathematical structures and can form the theoretical basis of many real applications. In the classical continuous/discrete Markov process, each state corresponds to an observed
The most fundamental concept that students learning introductory SAS programming must master is how SAS handles data. This might seem like an obvious statement, but it is often overlooked by students in their rush to produce code that works. I often tell my class to step back for a moment
Katherine Taylor explains how and why new data improves risk models.
A common operation in statistical data analysis is to center and scale a numerical variable. This operation is conceptually easy: you subtract the mean of the variable and divide by the variable's standard deviation. Recently, I wanted to perform a slight variation of the usual standardization: Perform a different standardization
Identification of Concepts and Topics based on out-of-the-box rules. It is common practice to develop a business or industry specific taxonomy.
An embedding model is a way to reduce the dimensionality of input data, such as images. Consider this to be a type of data preparation applied to image analysis. When an embedding model is used, input images are converted into low-dimensional vectors that can be more easily used by other computer vision tasks. The key to good embedding is to train the model so that similar images are converted to similar vectors.
In the early days of the COVID-19 pandemic, the issue of tax administration was low on the agenda for most. Beyond the obvious public health concerns, most business and government leaders were focused on how best to keep businesses afloat. But for those in federal, state and local governments responsible
Data collected during the manufacturing process is used to try to identify the cause of discrete problems after the event.
Have you ever seen the "brain teaser" for children that shows a 4 x 4 grid and asks "how many squares of any size are in this grid?" To solve this problem, the reader must recognize that there are sixteen 1 x 1 squares, nine 2 x 2 squares, four 3 x 3 squares, and one 4 x 4 square.
Las soluciones analíticas son muy importantes justo en el momento que vivimos. Tanto en la lucha directa contra la proliferación del virus como en la planificación operativa de los gobiernos y las instituciones de salud, es el instrumento que permite a las empresas enfrentar la crisis económica que surgirá como
In this guest blogpost, Work/Life welcomes Jamie Pack, College Planning Consultant with Advantage College Planning, to help parents and students sort through the issue of declaring a major…or not…during the college application process. Is there any value in applying to college without a declared major? Yes! In a time where
Interview mit Wolfgang Grupp, TRIGEMA.