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With busy week nights, some foods don’t make it to the table until the weekend simply due to lack of prep time. But, it’s a shame to narrow some of our favorite foods to only a few nights of the week. So, a quick solution when you’re in time crunch
In a previous article, I showed two ways to define a log-likelihood function in SAS. This article shows two ways to compute maximum likelihood estimates (MLEs) in SAS: the nonlinear optimization subroutines in SAS/IML and the NLMIXED procedure in SAS/STAT. To illustrate these methods, I will use the same data
In SAS Viya 3.2, the Self-Service Import provides a mechanism for a user to import (copy) data into the SAS Cloud Analytic Services (CAS) environment. The data is copied as a .sashdat file into the selected CAS Library location when it is imported. Self-Service Import data can only be imported into
지난 3월 31일 막을 올린 ‘2017 KBO 프로야구 정규시즌’이 어느새 중반부를 넘어섰습니다. 개막 두 달 만인 5월 30일 최소 경기 수 기준 역대 다섯 번째로 누적 관중 300만명을 넘어서며 식지 않는 인기를 과시하고 있는데요! 이처럼 수많은 관중들의 마음을 얻기 위해 그라운드 위 선수들의 치열한 경기 외에도 야구장 곳곳에서 다양한 시도들이
‘세계 야생 동물의 날(World Wildlife Day)’에 대해 들어보셨나요? 지난 2013년, 국제 자연 보호 연맹(IUCN) 산하 ‘멸종위기에 처한 야생동식물종의 국제거래에 관한 협약(CITES: Convention on International Trade in Endangered Species of Wild Flora and Fauna)’의 170여개 체약국은 3월 3일을 멸종 위기에 처한 야생 동물·식물에 대한 관심과 대책을 촉구하는 기념일로 지정했습니다. 이에 따라
Customizing the Kaplan-Meier plot in assorted ways is so popular that we devote an entire chapter to it in the SAS/STAT documentation.
Maximum likelihood estimation (MLE) is a powerful statistical technique that uses optimization techniques to fit parametric models. The technique finds the parameters that are "most likely" to have produced the observed data. SAS provides many tools for nonlinear optimization, so often the hardest part of maximum likelihood is writing down
Registration is now open for the SAS Analytics Experience 2017, being held September 18-20, in Washington, DC. (The Analytics Experience moves to Amsterdam, October 16-18 -- details on that event to follow.) For anyone interested in FVA analysis, Chip Wells and I will be delivering a half-day pre-conference training session
If you were a fan of the original Star Trek television series, you probably remember lots of little details about the show. And you might even feel sorry for the people who don't get the clever references you make to things from the show. If you're that person, then you'll
5月23日に開催されたSAS Forum Japan 2017の「SAS Viyaディープダイブ」セッションでは、SASのAIに搭載されている画像処理機能が入門レベルとして紹介されました。 セッション内では、皆様にとってもお馴染みの「浅草雷門」の写真を使った画像マッチングのデモも紹介しました。雷門を正面から撮った写真の中から、「雷門の提灯」の部分を切り出し、これをテンプレート画像として使用し、この「雷門の提灯」が写っている写真だけを画像マッチングによって見つけ出すというデモです。 さあ、ちゃんと「雷門の提灯」が写っている写真だけを見つけ出すことができたのでしょうか？ 以下は、Jupyter Notebookを使用し、PythonからSAS の画像処理機能を活用してマッチングを実行した結果です。（コードの一部抜粋） 【ライブラリのインポート】 In [16]: # import libraries import swat import matplotlib.pyplot as plt import os import json import numpy as np 【テンプレート画像「雷門の提灯」のロード】 In [24]: # load an image to cas r = conn.image.loadImages(casout={"caslib":"casuser", 'name':tmp_file_data[0], 'replace':True}, path=tmp_file_path) tmpTable = conn.CASTable(tmp_file_data[0]) 【この画像にマッチングさせます】 【マッチング対象画像のロード】
Ahora más que nunca, las personas quieren explorar los datos de su organización y utilizar la analítica, incluso si no poseen habilidades analíticas avanzadas. Al mismo tiempo, el personal de TI tiene más dificultades para satisfacer las constantes demandas de acceso y preparación de datos, además de gestionar las solicitudes
On a recent trip I met a long time user and early adopter of ODS Graphics who started using GTL with SAS 9.1.3, even before it was released as production with SAS 9.2. This user has presented many papers at SGF on GTL and some hands-on sessions on ODS Graphics Designer.
“Dejar de pensar en el pasado y empezar a crear el futuro es la base de toda transformación”. - Jim Goodnight No es casualidad que este mensaje que alimenta el espíritu de transformación y digitalización de los negocios en la actualidad sea también el de la analítica, esa ciencia que SAS
Utilities can save as much as $10 million a year for every one percent improvement in forecast accuracy by optimizing asset utilization and trading strategies. Changing energy markets and an influx of data from the smart grid are providing more opportunities to reap value from energy forecasting. Improving energy forecasts