In ordinary least squares regression, there is an explicit formula for the confidence limit of the predicted mean. That is, for any observed value of the explanatory variables, you can create a 95% confidence interval (CI) for the predicted response. This formula assumes that the model is correctly specified and
Uncategorized
SAS Model Manager and the sasctl packages aim to create a seamless ModelOps and MLOps process for Python and R models. Python and R models are not second-class citizens within SAS Model Manager. SAS, Python, and R models can be easily managed using our no-code/low-code interface. This is an interface that can be extended to support a variety of use cases.
La tecnología está cambiando la sociedad a un ritmo cada vez más rápido. Su impacto está presente en todos los ámbitos de la vida y su influencia ha afectado desde la forma de trabajar, hasta la forma de comunicarse, divertirse o informarse. Con una sociedad digitalizada y conectada, la administración
데이터 문해력과 SAS Viya 플랫폼 ‘데이터 문해력과 시민 데이터 사이언티스트(이하 CDS)의 필요 역량’이라는 지난번 블로그 포스팅에 이어, 이번에는 데이터 문해력을 기반으로 CDS를 지원하는 기반 플랫폼 ‘SAS Viya’(쌔스 바이야)의 주요 특징들을 살펴보겠습니다. SAS Viya는 인 메모리, MPP 환경과 Cloud Native 등 최신 기술을 기반으로, 분석의 생애 주기인 데이터 접근, 전 처리,
A SAS programmer wanted to use PROC SGPLOT in SAS to visualize a regression model. The programmer wanted to visualize confidence limits for the predicted mean at certain values of the explanatory variable. This article shows two options for adding confidence limits to a scatter plot. You can use a
The acceptance-rejection method (sometimes called rejection sampling) is a method that enables you to generate a random sample from an arbitrary distribution by using only the probability density function (PDF). This is in contrast to the inverse CDF method, which uses the cumulative distribution function (CDF) to generate a random
Don McMillan - America's top nerdy comedian -- is a featured entertainer at SAS Explore, Sept 11-14 in Las Vegas.
I love the world of science because it’s measurable and can be replicated. I’m also a strong believer in natural remedies because they have been documented for centuries and grow on our beautiful Mother Earth...you just have to know what you’re looking for. While I certainly appreciate pharmaceuticals and believe
SoDA를 이용해 쉽게 배우는 데이터 과학 #2 지난 포스팅에서는 SoDA(SAS Ondemand for Academics)의 장점과 필요성을 중심으로 SoDA가 어떤 제품인지 소개해 드렸습니다. 이번 포스팅에서는 ‘서비스 가입 방법’을 알아보겠습니다. 먼저 SoDA가 서비스 되는 브라우저 환경을 알아보고, 이어서 서비스 가입의 각 단계를 살펴보겠습니다. * 편집자 주 : 이번 글은 SAS코리아 컨설팅 본부 한노아
There are dozens of common probability distributions for a continuous univariate random variable. Familiar examples include the normal, exponential, uniform, gamma, and beta distributions. Where did these distributions come from? Well, some mathematician needed a model for a stochastic process and wrote down the equation for the distribution, typically by
Let X be any rectangular matrix. What is the trace of the crossproducts matrix, X'*X? Interestingly, you do not need to form the crossproducts matrix to compute the answer! It turns out that tr(X'*X) equals the sum of the squared elements of X. Theorem: For any matrix, X, the trace
이전에 포스팅했던 시민 데이터 사이언티스트(Citizen Data Scientist, 이하 CDS)에 대한 블로그에서 CDS가 갖춰야 할 기본 역량이 Data Literacy(한글로는 데이터 문해력으로 번역/이하 데이터 문해력)라고 소개했었습니다. 참고로 ‘문해력’이란 단어는 글을 읽고 이해하는 능력을 의미하는데, 데이터를 다룰 때도 마찬가지로 데이터를 읽고 이해하는 능력이 중요하기 때문에 이러한 용어를 사용해도 큰 무리는 없으리라 판단됩니다. 이번
In a previous article, I discussed the Wilcoxon signed rank test, which is a nonparametric test for the location of the median. The Wikipedia article about the signed rank test mentions a variation of the test due to Pratt (1959). Whereas the standard Wilcoxon test excludes values that equal μ0
If you are like me, you spend most of your day sitting. The average adult in the US spends 8-12 hours of their day sitting. Whether you are sitting because your work requires it or you have reduced mobility due to an injury or condition, you can improve overall fitness
Wilcoxon's signed rank test is a popular nonparametric alternative to a paired t test. In a paired t test, you analyze measurements for subjects before and after some treatment or intervention. You analyze the difference in the measurements for each subject, and test whether the mean difference is significantly different
A previous article discusses standardized coefficients in linear regression models and shows how to compute standardized regression coefficients in SAS by using the STB option on the MODEL statement in PROC REG. It also discusses how to interpret a standardized regression coefficient. Recently, a SAS user wanted to know how
A previous article shows an example of a Markov chain model and computes the probability that the system ends up in a terminal state (called an absorbing state). As explained previously, you can often compute exact probabilities for questions about Markov chains. Nevertheless, it can be useful to know how
Mother Nature gives us everything we need. I have heard that statement more than once, especially as a naturopath. We just need to pay attention. When I needed a little calm in my life decades ago, I remember taking walks near my house and seeing passion flowers growing in the
A previous article shows how to model the probabilities in a discrete-time Markov chain by using a Markov transition matrix. A Markov chain is a discrete-time stochastic process for which the current state of the system determines the probability of the next state. In this process, the probabilities for transitioning
A close look at the first of five DevOps ideals in Gene Kim's book, “The Unicorn Project."
I was recently asked to be on the jury for the Asia-Pacific track of the 2023 SAS Hackathon. As many of my colleagues have said before, judging the hackathon is a great experience. We were exposed to lots of innovative ideas about how to apply AI and analytics to a wide
The 2023 International Congress of Actuaries (ICA2023) brought together industry leaders and actuaries worldwide to explore the challenges and opportunities for actuaries. Over the years, SAS has emerged as a variance and regression analysis software, revolutionising the actuarial field. Since then, it has evolved into SAS Viya 4.0, a cloud-native
Given a set of N points in k-dimensional space, can you find the location that minimizes the sum of the distances to the points? The location that minimizes the distances is called the geometric median of the points. For univariate data, the "points" are merely a set of numbers $${p_1,
This July I'm refreshing the previous Strengthening Your Relationship email series from 2021. The email series offers quick ideas and information within three categories: research and insight, questions for closeness, and weekend activity ideas. As I revisit this content I was reminded of the weekly meeting practice I wrote about
While writing an article about labeling a polygon by using the centroid, I almost made a false claim about the centroid. I almost claimed that that the centroid is the point in a polygon that minimizes the sum of the distances to the vertices. It is not. The point that
A colleague asked how to compute the barycentric coordinates of a point inside a triangle. Given a triangle in the plane with vertices p1, p2, and p3, every point in the triangle can be represented as a convex combination of the vertices: c1*p1 + c2*p2 + c3*p3, where c1,c2,c3 ≥
SAS' Phuong Ngo demonstrates an automated shift-left CI/CD security workflow.
Part of the power of the SAS ODS system is the ability to visualize data by using ODS templates. An ODS template describes how to render data as a table or as a graph. A lot of papers and documentation have been written about how to define a custom template
データサイエンスの使いどころ・・・攻めと守りの圧倒的な違い 以前のブログで、データ活用における攻めと守りについてお話しました。今回は小売業を例に多くのデータ活用プロジェクトが陥りやすい罠と、真の目的達成のための方法についてご紹介します。 小売業の目的はもちろん他の業種企業と変わらず、収益の最大化です。昨今データ分析を武器として売り上げの最大化、コストの削減、業務プロセスの生産性向上を目指す企業が増えてきています。時には、データサイエンティストが、データサイエンスを駆使してプロジェクトを実行しているケースもあるでしょう。 ここで、今一度現在取り組んでいる、またはこれから取り組もうとしているデータサイエンスやAI活用のプロジェクトがどんな利益を自社にもたらすのかを改めて考えてみましょう。昨今、需要予測についての相談が非常に多いので、ここでは需要予測について考えてみます。 弊社にご相談いただくケースの中で、少なくない企業が、需要予測をこのブログで言うところの「守りの意思決定」としてとらえています。多くのケースで、過去の実績をベースに将来の需要を予測することで、在庫過多や欠品を減らそうというプロジェクトに投資をしていたり、しようとしています。言い換えると、過去の実績を学習データとして、将来を予測するモデルを構築し、ひとつの将来の需要予測を作成し、それを在庫を加味したうえで、発注につなげています。 手段が目的化することで見失う可能性のある本来の目的とは 非常に典型的なAI活用、データサイエンス活用かと思いますが、実は、「AIで予測」、「機械学習で予測」といった言葉で最新のデータ活用をしているかのような錯覚に陥っているケースが見受けられます。数十年前から行われており、昨今でも同様に行われている、機械学習を用いた典型的な需要予測は、「守り」です。すなわち、どんなに多くの種類のデータを使うかどうかにかかわらず、過去の傾向が未来も続くという前提のもとに予測モデルを作成している場合には、あらかじめ定義した前提・業務プロセスの制約の下で、機会損失を最小化するために予測精度をあげているにすぎません。 つまり、そのような前提での需要予測は、小売業の収益向上という観点では、期待効果が限定的であるということです。では、最終的な収益の最大化を実現するには、何をすべきでしょうか? 収益を向上させるためにはもちろんより多くの商品を売ることにほかなりません。より多くの商品を売るためには当然、顧客の購買心理における購買機会に対して販売を最大化する必要があります。あるいは、顧客の購買心理そのものを潜在的なものから顕在化したものにすることも必要でしょう。つまり、販売機会を最大限に活用するということは、店舗中心ではなく、顧客中心に考えるということです。 小売業における攻めのデータ活用の1つは、品ぞろえの最適化 このように、顧客中心に考えることで初めて最適な品揃えの仮説検証のサイクルが可能となります。過去のデータは、単に過去の企業活動の結果であり、世の中の「真理~ここでは顧客の本当の購買思考」を表しているわけではありません。真理への到達は、仮説検証ベースの実験によってのみ可能になります。わかりやすく言うと皆さんよくご存じのABテストです。このような実験により、品ぞろえを最適化することで、販売機会を最大化することが可能なります。そのプロセスと並行して、オペレーショナルな需要予測を実践していくことが重要となります。 需要予測と品ぞろえ最適化の進化 昨今、AIブーム、データサイエンティストブーム、人手不足や働き方改革といったトレンドの中で、従来データ活用に投資してこなかった小売業においても投資が進んでいます。しかし多くのケースでこれまで述べてきたような守りのデータ活用にとどまっていたり、古くから行われている方法や手法にとどまっているケースが見受けられます。歴史から学ぶことで、無用なPOCや効率の悪い投資を避けることができます。今、自社で行っていることがこの歴史の中でどこに位置しているかを考えてみることで、投資の効率性の向上に是非役立てていただければと思います。 小売業におけるデータ活用のROI最大化にむけたフレームワーク SASでは長年、小売業や消費財メーカのお客様とともにお客様のビジネスの課題解決に取り組んできました。その過程で、小売業・消費財メーカー企業内の個々の業務プロセスを個別最適するのではなく、それら個々の業務プロセスを統合した、エンタープライズな意思決定フレームワークが重要であるとの結論に至っています。AIやデータサイエンスという手段を活用し、データドリブンな意思決定のための投資対効果を最大化するための羅針盤としてご活用いただければと思います。
While writing an article about Toeplitz matrices, I saw an interesting fact about the eigenvalues of tridiagonal Toeplitz matrices on Nick Higham's blog. Recall that a Toeplitz matrix is a banded matrix that is constant along each diagonal. A tridiagonal Toeplitz matrix is zero except for the main diagonal, the