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In the SAS/IML language, you can only concatenate vectors that have conforming dimensions. For example, to horizontally concatenate two vectors X and Y, the symbols X and Y must have the same number of rows. If not, the statement Z = X || Y will produce an error: ERROR: Matrices

Advertencia a Millennials: Su ingreso a este blog está totalmente prohibido. Sepa que estamos vigilando celosamente el cumplimiento de esta prohibición. En caso de que lo encontremos metiendo sus narices por aquí, lo ataremos a una silla, lo obligaremos a escuchar todas nuestras largas historias y tendrá una de las

机上で男女が世界地図を広げて、何かの打ち合わせをしています。 航空会社A社の企画担当が、競合他社の路線を分析しているのか? それとも、これから二人で行く海外旅行のプランを立てているのか? いえいえ、これはSAS Visual Analyticsで作成されたダッシュボードの画面です。 このダッシュボードの構成は以下の通りです。 ・使用しているデータ:OpenFlightsで公開されている世界の空港の利用状況のデータ ・真ん中の世界地図:「ネットワークダイアグラム」オブジェクトを使用し、背景に地図を表示して、路線を描画 バブルの大きさは、利用頻度を表しています。 ・左側の棒グラフ:利用頻度の高いトップ10の航空会社 ・右下と左下の数値:「キーの値」オブジェクトを使用し、注目すべき指標をクローズアップしています。例えば右下の値は、利用頻度が最も高い空港名とその数を示しています。 ・人の手や机、カメラ、パソコン等は背景に使用している画像です。 今、このダッシュボードでは、左側の棒グラフ上で「Air China」が選択され、ネットワークダイアグラムと2つの指標はAir Chinaに自動的に絞り込まれた内容が表示されています。 でも、まだ、これは張りぼて? と思っていませんか。 以下は、左側の棒グラフ上で「US Airways」を選択した状態です。 ネットワークダイアグラムや2つの指標の内容が変わっているのがわかりますね。 ご覧の通り、インタラクティブなダッシュボードです。 みなさんも、こんなクールなダッシュボードで戦略を練ってみれば、新たなアイデアが湧いてくるかもしれませんね。 このブログは、SAS CommunityサイトのVisual Analytics ギャラリーに公開されている内容に基づいています。

If you're good at games like Wheel of Fortune, Scrabble, or Words with Friends, you've probably figured out that certain letters appear more often than others. But do you have a cool way to figure out which letters appear most & least frequently? How about using a computer to plot

인공지능(AI), 사물인터넷(IoT) 등 다양한 디지털 기술이 융합되면서 기업의 마케팅 전략도 혁신을 거듭하고 있습니다. 특히 고객이 특정 순간과 장소에서 기대하는 바를 충족시키는 ‘개인화된 경험’이 핵심 마케팅 역량으로 떠올랐는데요. 이를 위해서는 고객 데이터를 실시간으로 분석해 개별 고객의 특성과 원하는 바를 보다 잘 이해하고, 언제 어디서나 고객의 소비 여정과 함께하는 실시간 마케팅(Real-time marketing) 전략을 구현해야

Note: The following concludes an eight-part serialization of selected content from Steve Morlidge's The Little (Illustrated) Book of Operational Forecasting. Good forecasts don’t always ‘look right’ Many forecasters believe that they can tell how good a forecast is by ‘eyeballing’ it. Good forecasts just ‘look right’ or so they would

The true test of when a technology has become mainstream is when it is being widely used across the whole business, from customer-facing functions to backroom work. Analytics has been customer-focused for some time, and has also been widely used for individual performance, for example, by sports teams. The advent

How can SAS Grid Manager and SAS Viya work together to process massive volumes of data? Get answers to common questions about how the two interact in this blog post.

A SAS programmer recently asked me how to compute a kernel regression in SAS. He had read my blog posts "What is loess regression" and "Loess regression in SAS/IML" and was trying to implement a kernel regression in SAS/IML as part of a larger analysis. This article explains how to

Note: Following is an eight-part serialization of selected content from Steve Morlidge's The Little (Illustrated) Book of Operational Forecasting. The measurement challenge So here is the forecasters dilemma: There will always have forecast error. The challenge is to work out the cause of the error and to take the appropriate

Using relative file paths in your SAS programs? Use the new DLGCDIR function to manage your SAS working directory -- even in SAS Enterprise Guide or SAS Studio -- to ensure your programs are working the way they ought to.

We'll take a short break from the Steve Morlidge book serialization, to announce that SAS Research & Development has again provided $10,000 in funding for the SAS/IIF grant program. Both academics and industry practitioners are encouraged to apply and conduct original research for improving the practice of forecasting. SAS/IIF Grants

The Work/Life Center is here to provide support through life transitions. With college semesters in full swing, we're aware that some of you may be facing the transition of "empty nesting". "Empy nesting" occurs after kids leave the home. This transition can occur any time of the year and for

Los seres humanos aprendemos todo el tiempo. Desde el momento en que nacemos, tomamos información de nuestro entorno, la asimilamos y la utilizamos a nuestro favor para sobrevivir y prosperar. Por ejemplo, aprendemos a leer uniendo letras y palabras, formando oraciones y entendiendo un texto. Gracias a las conexiones neuronales

This blog post highlights more SAS Global Forum papers chosen by SAS Press authors.