Unlock the Power of Prediction Profiler Learn how to interpret and use Prediction ProfilerUnderstand factor effects and interactionsSee the relationship between the data table and the profilerModel prediction & optimization and assess the impact of factor variability
See how to analyze sequential measurement data when the measurements you want to analyze are not single points, but a range of points presented as curves. Examples include chemical spectra, sensors, and batch or streaming data. Learn how to effectively use the entire data set rather than selected points in the curve to provide immediately interpretable results, including graphs commonly used to un...
Duration: 90 minutes
Registration: FREE
Hosted via Zoom
This complimentary online workshop is designed for intermediate to advanced users with at least one year of JMP experience who would like to expand their skills in predictive modeling. Attendees learn the basics of decision tree models, also known as partition models or classification and regression trees.
If you have any questions about this e...
Duration: 90 minutes
Registration: FREE
Hosted via Zoom
This complimentary online workshop is designed for intermediate to advanced users with at least one year of JMP experience who would like to expand their skills in predictive modeling. Attendees learn the basics of neural network models.
If you have any questions about this event, please contact Kirsten Crannell.
Register now and start making d...
Video using JMP Pro 19 was posted in September 2026. Predictive modeling involves selecting the right model, tuning it for the best performance, and scoring new data. Because no single algorithm works best for every problem, it’s important to screen, compare, and refine multiple models before selecting one for deployment. See how to implement a comprehensive predictive modeling workflow using JMP ...
Duration: 90 minutes
Registration: FREE
Hosted via Zoom
This complimentary online workshop is designed for intermediate to advanced users with at least one year of JMP experience who would like to expand their skills in predictive modeling. Attendees learn the basics of neural network models.
If you have any questions about this event, please contact Kirsten Crannell.
Register now and start making d...
Video using JMP 19 was posted in April 2026. Model validation, a method of determining if a predictive model is generalizable to new data, is critical when using models to help make decisions. Data can be partitioned into sets before modeling to avoid overfitting. Part of the original data is used to estimate parameters and the rest of the data is used to tune or evaluate the parameters. JMP Pro ...
2026年3月5日に実施したセミナーのオンデマンドバージョンです。セミナーの内容を編集し、トピックごとに動画を視聴できます。 ■セミナー概要 JMP の「プロファイル」は、統計モデルにおける説明変数（因子）と目的変数（応答）の関係を視覚化し、直感的にその関連性を把握できる便利な機能です。 プロファイルには、「予測プロファイル」「曲面プロファイル」「等高線プロファイル」などがあり、応答曲面モデルやニューラルネットワークなどの比較的複雑なモデルについても、さまざまな角度から可視化・考察することができます。 本セミナーでは、これらのプロファイル機能に焦点を当て、プロファイルで何ができるのか、どのような場面で便利なのか、実際の活用例を交えながら分かりやすくご紹介します。 ■目次 応答曲面とプロファイルJMPの「プロファイル」でできること「予測プロファイル」について観測データでの活用例実験計画で取得...
Learn how to analyze sequential measurement data where measurements you want to analyze as factors are not single points, but a range of points presented as curves. Examples include chemical spectra, sensors, batch or streaming data. Learn how to efficiently use the full range of data rather than selected points in the curve to provide immediately interpretable results, including graphs typically ...
Découvrez comment analyser des données de mesures séquentielles lorsque ces mesures prises en tant que facteurs ne sont pas des points uniques, mais une gamme de points présentés sous forme de courbes. Il peut s'agir, par exemple, de spectres chimiques, de capteurs, de données par lots ou en flux. Apprenez à utiliser efficacement l'ensemble des données plutôt que des points sélectionnés dans la co...
Erfahren Sie, wie Sie sequenzielle Messdaten analysieren, bei denen die als Faktoren zu analysierenden Messungen keine einzelnen Punkte, sondern eine Reihe von Punkten sind, die als Kurven dargestellt werden. Beispiele hierfür sind chemische Spektren, Sensoren und Batch- oder Streaming-Daten. Erfahren Sie, wie Sie den gesamten Datenbereich anstelle ausgewählter Punkte in der Kurve effizient nutzen...
Special Guest Presenter:
Prof. Nathan Coates
Faculty Director, Master of Science in Business Analytics and AI
Villanova University
The predictive power of machine learning models depends on hyper-parameter tuning. Years of university machine learning exercises have led Prof. Nathan Coates of Villanova University to develop a method for adjusting hyper-parameters, called Tune and Zoom, that has hel...
2025年9月にリリースされた最新バージョン「JMP 19」、「JMP Pro 19」の新機能を紹介します。 本ページでは、その中から分析、グラフに関する機能を、項目ごとに紹介します 目次 1. データフィルタ：多重応答値でのフィルタリングなど 2. グラフビルダー：複数のY軸表示、矢印チャートなど 3. 一変量の分布：許容区間の分布指定 4. 二変量の関係：Games-Howell検定、比の比較検定 5. モデルのあてはめ 6. プロファイル 7. 工程のスクリーニング（環境モニタリング） 8. 管理図とトリアージ 9. 実験計画法 10. 標本サイズエクスプローラ 11 . ベイズ最適化（JMP Pro） 12. 因果推論（JMP Pro） 13. 一般化線形混合モデル（GLMM）、線形混合モデル(LMM) （JMP Pro） 14. 関数データエクスプローラ（JMP pro） ...
Predictive modeling and machine learning are powerful tools for uncovering insights and making data-driven decisions. JMP and JMP Pro make these advanced techniques accessible to all users through point-and-click interface—no coding needed. See how to: Build predictive models using decision trees, forests, and neural netsVisualizations for interpreting modelsValidate models and compare performa...
Video using JMP 18 was posted in August 2025. Do you need to identify battery degradation patterns to help design or improve batteries for your renewable energy systems? Are you trying to identify optimal temperature or other parameters’ effect to prolong battery stability in your environment? This session shows how to: Manage, organize and combine large electrochemical data for numerous batte...
Video using JMP 18 was posted in August 2025. Do you need to make sense of sensor or other sequential measurement data where the responses are not single points, but a range of points presented as curves? Do you need to use that data to predict the likelihood of a system failure or maintain consistent power quality for a renewable energy or other systems? Would you like examples of how to use s...
With the JMP Student Edition, academic researchers get no-cost access to all JMP Pro capabilities, such as Python integration and more advanced tools for building machine learning models. This webinar will demonstrate advanced analytical applications for researchers in Chemistry, Chemical Engineering, Materials Science, and related fields by enhancing JMP Student Edition’s native features with...
Which Model When? The models you use depend on your data, the questions you are trying to answer and the problems you want to solve. See how to decide by working through case studies that illustrate how to identify, fit and evaluate models that might be most useful in achieving your analysis goal. (view in My Videos)
Get JMP software free for academic use at jmp.com/student Predictive modeling encompasses a range of techniques for using historical data to predict future outcomes, and it has applications in nearly all quantitative disciplines. The JMP Student Edition has robust predictive modeling capabilities, including a range of algorithms for classification and regression, automated model cross-valida...
Video was recorded in March 2025 using JMP 18 and JMP Pro 18. Do you work with complicated processes where possible unaccounted-for variables might be impacting your predictions? Do you sometimes struggle to identify the validity of factors impacting the results of complicated interactions? Are collinear factors thwarting the efficiency of your process improvement projects? In this Mastering JMP s...
Learn how to analyze sequential measurement data where measurements you want to analyze as responses are not single points, but a range of points presented as curves. Examples include chemical spectra, sensors, and batch or streaming data. Learn how to efficiently use the full range of data rather than selected points in the curve to provide immediately interpretable results, including graphs typi...
4 Day Course Monday, November 18 | 8:30 a.m. - 12:00 p.m. ETTuesday, November 19 | 8:30 a.m. - 12:00 p.m. ETWednesday, November 20 | 8:30 a.m. - 12:00 p.m. ETThursday, November 21 | 8:30 a.m. - 12:00 p.m. ET This course covers the skills required to develop, assess, tune, compare, and score predictive models using JMP Pro software. This course teaches you how to build and understand predictive...
This video was updated in September 2024. (view in My Videos) See how to: Explore curve data using Graph BuilderFit all basic functions using Functional Data ExplorerFit wavelets and examine resultsFit B- and P- splinesExamine results of P-spline model using Functional Design of Experiments (FDOE) ProfilerUse JMP Pro 18 FDE peak finding models to characterize spectra and determine how different ...
Building Predictive Models for Correlated and High Dimensional Data Video was updated in August 2024. Multicollinearity is the existence of such a high degree of correlation between supposedly independent variables being used to estimate a dependent variable that the contribution of each independent variable to variation in the dependent variable cannot be determined. High multicollinearity in you...
Since the Functional Data Explorer was introduced in JMP Pro 14, it has become a must-have tool to summarize and gain insights from shape features in sensor data. With the release of JMP Pro 17, we have added new tools that make working with spectral data easier. In particular, the new Wavelets model is a fast alternative to existing models in FDE for spectral data. See how to: Understand what ...
Seit der Einführung des Funktionalen Datenexplorers (FDE) in JMP Pro 14 ist er zu einem unverzichtbaren Tool für die Zusammenfassung von Formmerkmalen und die Erkenntnisgewinnung geworden. Mit der Veröffentlichung von JMP Pro 17 haben wir zudem neue Werkzeuge hinzugefügt, die die Arbeit mit Spektraldaten erleichtern. Insbesondere das neue Wavelets-Modell bietet eine schnelle Alternative zu bestehe...
Generalized Linear Mixed Models were introduced in JMP Pro 17, where you specify two distributions - Binomial and Poisson. GLMM combines two approaches: the linear mixed model and generalized linear model frameworks . GLMM is useful for three types of model structures: Randomized complete and incomplete block designsSplit-plot experimentsRandom coefficient models See how to: Model mixed effect...
See how to: ID new JMP 17 capabilites, including JMP extrapolation controlCustomize Profiler appearanceShare Profilers as HTMLUse the Interaction Profiler to show or hide interaction plots that update as you update the factor values in the Prediction ProfilerUse the Surface Profiler to produce a surface plot for the fitted modelUse the Simulator in the profilers to define random inputs, run s...
関数データエクスプローラの概要と関数主成分分析（5分34秒）利用例１：関数データを用いたサンプルの分類 （16分52秒）利用例2：関数データを用いた特性値の予測（12分49秒）利用例3：関数応答実験計画（11分27秒）利用例4：スペクトルデータの分析（12分29秒） 「関数データエクスプローラ」（FDE; Functional Data Explorer）プラットフォームは、関数データ・シグナルデータ・時系列データを分析するためのプラットフォームです。関数データに対し、特徴を表す関数主成分を抽出して次元縮小をおこない、関数データの判別、予測モデルの作成などを行なえます。 本動画に関する資料（PDF) をダウンロードできます。 関数データエクスプローラの概要と関数主成分分析（5分34秒） 関数データについて、関数データの特徴量として関数主成分を抽出すること。この後に説明する、利用例1～利...
This video was updated in August 2024. Sampling points from curved data typically is not the most accurate way to create a predictive model. In many cases the sampled points miss variability that could impact outcome. This demo uses a case study to show how to use JMP functional Data Explorer to address this challenge. (view in My Videos) The Case Study: Use spectra to determine factors that...
Get JMP software free for academic use at jmp.com/student Researchers in numerous domains often encounter functional data, or one continuous measure that unfolds across another. Prominent examples include chemical spectra, financial series, and sensor data; think any data where the unit of analysis is not a single point, but a curve. Functional data analysis (FDA) offers techniques to analyze...
Observational data lends itself to analysis useful for large data sets, data that probably do not exhibit orthogonality and situations where we are interested in prediction rather than interpretation. The Generalized Regression platform is the place to build regression models for both designed experiments and observational data. Some things to keep in mind: Penalized regression shows great promis...
(view in My Videos) See how to: Model using Partition, Bootstrap Forests and Boosted TreeUnderstand pros and cons of decision trees Pros: Uncover non-linear relationships, get results that are easy to understand, screen large number of factors Cons: Handle one response at a time, forms if-then statement not mathematical formula, high variability can lead to major differences in a model for simi...
(view in My Videos) See how to: Quantify positive or negative sentiment in unstructured text dataUnderstand basics of Lexical Sentiment Analysis Scores sentiment from individual words in each doc when no external measure of sentiment is availableUses a Sentiment dictionary (aka "lexicon") that specifies scores (e.g., wonderful = +90, disappointed = -30)Scores individual sentiment phrase to...
(view in My Videos) See how to: Understand a neural network as a function of a set of derived inputs, called hidden nodes, that are nonlinear functions of the original inputsInterpret Neural Network diagram inputs (factors) and outputs (responses) Understand terms and how they apply to building Neural Networks (nodes, activation type, activation functions)Understand types of activation function...
(view in My Videos) See how to: Understand why transformations stabilize variance, make the error more uniform across the design region, remedy lack of fit and plot predictions in a way that does not violate physical limits, display negative counts or erroneously report yields as greater than 100%donnTransform data on the fly using Graph Builder and change scales to improve graph reada...
(view in My Videos) See how to: Understand the manufacturing yield example used in the demoFind patterns Use Distribution to examine the relationship between variables and between variables and responseUse Graph Builder to examine all variables, use icon drag-and-drop to fit lines to data, turn on statistics like R square, change fit type interactively and add color to highlight key fi...
See how to screen models to find the most appropriate, best model for your needs and the goals of your study or experiment. Model Screening, introduced in JMP Pro 16, streamlines your workflow to build and view results from multiple models at one time. The video does not cover building each type of model individually. (view in My Videos) See how to: Use validation options in Model Sc...