depence r2
  • de
autodesk-gold-partner-logo-rgb-black
Due to a technical downtime, we are currently only available by phone. Received emails will be answered as soon as possible. In urgent cases, please contact the central office or your personal contact person by phone.
Moldflow Monday Blog

Depence — R2

Learn about 2023 Features and their Improvements in Moldflow!

Did you know that Moldflow Adviser and Moldflow Synergy/Insight 2023 are available?
 
In 2023, we introduced the concept of a Named User model for all Moldflow products.
 
With Adviser 2023, we have made some improvements to the solve times when using a Level 3 Accuracy. This was achieved by making some modifications to how the part meshes behind the scenes.
 
With Synergy/Insight 2023, we have made improvements with Midplane Injection Compression, 3D Fiber Orientation Predictions, 3D Sink Mark predictions, Cool(BEM) solver, Shrinkage Compensation per Cavity, and introduced 3D Grill Elements.
 
What is your favorite 2023 feature?

You can see a simplified model and a full model.

For more news about Moldflow and Fusion 360, follow MFS and Mason Myers on LinkedIn.

Previous Post
How to use the Project Scandium in Moldflow Insight!
Next Post
How to use the Add command in Moldflow Insight?

More interesting posts

Check out our training offerings ranging from interpretation
to software skills in Moldflow & Fusion 360

The MF ACADEMY logo showcases the letters MFA intertwined with an educational hat, symbolizing excellence in training and expertise. This logo represents MF SOFTWARE's commitment to providing comprehensive programs in injection molding, Moldflow, and Fusion 360. Unleash your potential with MF ACADEMY's top-notch educational offerings.

Get to know the Plastic Engineering Group
– our engineering company for injection molding and mechanical simulations

PEG-Logo-2019_weiss

The R2 (R-squared) metric is a widely used statistical measure that evaluates the goodness of fit of a regression model. It represents the proportion of the variance in the dependent variable that is predictable from the independent variable(s). However, traditional R2 measures are limited to linear relationships and do not capture non-linear dependencies between variables. This is where Dependence R2 comes into play.

Dependence R2, also known as Distance Correlation R2 or D-R2, is a statistical measure that extends the traditional R2 concept to non-linear relationships. It was introduced by Gábor J. Székely and Maria L. Rizzo in 2009. Dependence R2 assesses the strength of the relationship between two variables, X and Y, by quantifying the proportion of the variance in Y that can be explained by X, regardless of the relationship being linear or non-linear.

Dependence R2 is a valuable statistical measure that extends traditional R2 to non-linear relationships. Its ability to detect non-linear dependencies makes it a useful tool for data analysis, feature selection, and time series analysis. As data becomes increasingly complex, Dependence R2 is likely to play a more prominent role in statistical analysis and modeling.