SAS JMP 是专为科学家和工程师打造的预测分析软件，是JMP统计发现软件的专业版。除拥有JMP的所有功能外，还提供一些可以让用户从容应对复杂分析工作的高级功能，通过简单易用的界面挖掘和理解数据。SAS JMP破解版可帮助您解决常规和困难的统计问题。 从轻松地从各种来源访问数据，到使用快速，可靠的数据准备工具以及执行选择统计分析，JMP可以让您在任何情况下都充分利用数据。
Extend the value of JMP to solve bigger and more challenging analytic problems with the latest data science techniques, including predictive modeling and machine learning.
Build better models
Without the right predictive analytics tools, building a model to predict what will happen with new customers, new processes or new risks becomes much more difficult. JMP Pro offers a rich set of algorithms that lets you build better models.
JMP PRO predictive models
Enhance the power of your predictive models with various types of data, including unstructured text data you’ve collected – repair logs, engineering reports, customer survey response comments and more. Use JMP Pro to organize and transform data into usable additions to your predictive models, enabling more confident decision making.
JMP PRO central modeling
Handling your models doesn’t have to be painful – JMP Pro makes it easy to find the best fit to your data with model screening. Easily build candidate models then profile, compare and generate score code in C, Python, JavaScript, SAS or SQL.
Predictive Modeling and Cross-Validation
Use the JMP Pro set of rich algorithms to build and validate your models more effectively.
Model Screening and Comparison
Build a variety of models and determine the best for the problem you are trying to solve.
Formula Depot and Score Code
Organize your models and save model score code in SAS, C, Python, Javascript, SQL.
Structural Equation Modeling (SEM)
Framework to fit a variety of models, including confirmatory factor analysis, path models, measurement error models, and latent growth curve models.
Modern Modeling
Use new modeling techniques, including Generalized Regression with penalized methods, to build better models, even with challenging data.
Functional Data Analysis
Create models of data that are functions, signals or series with Functional Data Explorer (FDE).
Reliability Block Diagrams
Easily fix weak spots in your system and be better informed to prevent future system failures.
Repairable Systems Simulation
Simulate system repair events to understand downtime and number and cost of repairable events.
Covering Arrays
Design your experiment to maximize the probability of finding defects while minimizing cost and time.
Term Selection and Sentiment Analysis
Use your unstructured data to identify terms associated with a response and explore basic sentiment.
Mixed Models
Analyze data involving both time and space, where multiple subjects are measured or groups of variables are correlated.
Uplift Models
Predict consumer segments most likely to respond favorably to an action, allowing targeted marketing decisions.
Advanced Computational Statistics
Use exact statistical tests, exact non-parametric statistical tests and bootstrapping statistics.
Generalized Linear Mixed Models (GLMM)
Enables fitting of models with both non-Gaussian response variables and random design effects.
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