Limpieza y Automatización de Datos (vídeo en español)
Automatiza tu trabajo y mejora tu productividad con JMP
Automatiza tu trabajo y mejora tu productividad con JMP
This blog was authored by Shuying Han. Shuying Han developed the UpSet Plot add-in as part of the Biostatistics Undergraduate Summer Internship (BUSI) program at the University of North Carolina at Chapel Hill.
Introduction
Nowadays, statistical studies and clinical trials aim to uncover and communicate the stories within data, transforming raw numbers into meaningful insights. Data visualiza...
We are thrilled to invite you to take part in the pilot of the JMP Wish List Prioritization Survey, set to launch on July 3! Your feedback is invaluable as it will play a pivotal role in shaping the future of JMP by highlighting the ideas that hold the most significance to you.
Survey overview
Within this survey, you will be provided with 100 "coins" to allocate to your preferred ideas. You have...
Xueting Wang wrote this blog post. Xueting Wang developed this add-in as part of the Biostatistics Undergraduate Summer Internship (BUSI) program at the University of North Carolina at Chapel Hill. Introduction What data visualization do you use when you want to view the evidence from different studies and interpret the overall findings of a systematic review? The forest plot is a good choice...
Get familiar with the Python integrated development environment (IDE) in JMP 18 and learn how to: Locate the Python IDE.Run a simple example.Install Python packages.Run JSL script from Python.Send a Python variable to JSL.Create a JMP data table from Python.
Learn about how JMP users from notable organizations explore data and overcome obstacles. See and read presentations from over 55 JMP Discovery Summits.
機械学習の分野では、変数が多いデータを扱うことが多く、そのようなデータを扱うと多重共線性が発生することがあります。その際、多重共線性を回避する手法であるLassoをはじめとする正則化回帰が用いられることがあります。 JMP Proでは「一般化回帰」という手法としてこの正則化回帰を利用でき、パラメータ推定値(回帰係数)を収縮や変数選択によって、多重共線性を影響を抑えた安定したモデル推定が可能になります。 以下の図は、Lassoによって回帰係数が収縮されたときの様子を示しています。レポート「元の説明変数に対する推定値」を見ると、効果の小さい説明変数は0(ゼロ)に収縮されており、Lassoが変数選択を行っていることが確認できます。 では、多重共線性が存在するデータについて、Lasso回帰はどのように有効なのでしょうか。本記事では、応答(Y)との真の関係をあらかじめ定めた仮想データを用い...
This blog post is intended to help you understand and troubleshoot the OneDrive and SharePoint Data Connectors authentication process.
Data integrity is essential for accurate predictive modeling, regulatory compliance, and business efficiency. I spoke with Chandramouli Ramnarayanan, Global Technical Enablement Engineer at JMP, about the biggest data challenges companies face and the best strategies for improving data quality. From statistical monitoring to design of experiments (DOE) and anomaly detection, Chandra shares expert ...
みなさんは、次のようなグラフをご覧になったことはありますでしょうか?近年、さまざまな分野で活用されているグラフです。 このグラフをはじめて見る方も、何となくその意味を感じ取ることができるのではないでしょうか。 このグラフは、「サンキーダイアググラム(Sanky Diagram)」と呼ばれています。 上記のグラフは色の好みを尋ねたアンケートの結果例です。「一番好きな色」、「性別」、「年齢」という設問を横軸に配置しており、各軸における間隔の太さは、回答数の割合に比例しています。 グラフからは「50代はBlueを好む割合が高い」、「40代の女性はPurpleを好む割合が高い」などの傾向が一目でわかります。 本記事では、まずサンキーダイアグラムの概要を説明し、その後、2つの分析例をもとに、JMPでサンキーダイアグラムを描く方法を紹介します。記事の最後には、操作方法を示したビデオも...
Is it possible to optimize a plasma-enhanced chemical vapor deposition (PECVD) with just 25 test wafers? Absolutely! In semiconductor manufacturing, constraints such as a 25-wafer run on a tool are common, but they don’t have to limit success. Leveraging process data to inform experiment design is a winning strategy for getting effective results with limited resources.
Procter & Gamble veteran Cy Wegman on the importance of a data-driven culture, key leadership attributes, and more.
對食品研發來說,每一次實驗都可能浪費原料與時間。透過有系統的實驗設計與數據分析,這家公司做到了「用最少的試驗,得到最多的資訊」。
JMP 18 has a new way to integrate with Python. The JMP 18 installation comes with an independent Python environment designed to be used with JMP. In addition, JMP now has a native Python editor and Python packages specific to JMP. This JMP Python environment has enhanced connectivity and interaction with JMP, which means using Python with JMP has never been easier. In this series of blog posts, I ...
手機螢幕的耐用性對消費者和製造商至關重要。本研究透過跌落測試與統計分析,評估兩種鋁矽酸鹽玻璃螢幕的耐久性,探討是否達到97%成功率標準,並比較其性能差異。
JMP 18 introduces a lot of new capabilities, including revamped Python support, which allows users to directly access, modify, and create JMP data tables from Python. This is accomplished through the jmp.DataTable Python object. Keep reading to learn how to create a pandas.DataFrame from a JMP data table, as well as the reverse, a JMP data table from a pandas.DataFrame live and in-memory.
Esta presentación introduce las nuevas funcionalidades de JMP 18 en español, incluso: Detección de picosConectores de datos configurablesMejoras la importación de datos en servidores PIPreajustes de plataformaMejoras en le perfilador de predicción, incluso visualizar los intervalos de predicción, las interacciones superpuestas, y la habilidad de ver las observaciones individuales.Mejoras en la i...
The jackknife technique is very simple, yet very powerful, relying on calculations using the “leave one out” technique.Figure 3: Simple plot of X1 vs X2, including best-fit line (from ordinary least squares)
drawing programlook in the scripting index, under MouseBox. The sample script for SetClick is a tiny sketchpad.15-puzzlewhile you are looking at MouseBox, check out the SetDropTrack script. Classic game.mazeAlong with the other samples that ship with JMP, there is a set of 3D samples. Look in Samples/Scripts/Scene3D. Check out the RatMaze JSL sample. (Read the 2nd paragraph of the comments be...