You rely on Minitab Statistical Software for trusted analyses and valuable insights to help you confidently make better decisions, but how do you bring those insights to life and really drive impact at your organization? The right visuals and accessibility can make all the difference.
Discover how the latest release of Minitab Statistical Software can help you enhance the impact of your work in your business by checking our presentation.
New visualizations and graphics willbe showcased in Minitab Statistical Software, that can strengthen your decisions and influence, make your insights easy-to-share, and then even easier to understand.
We'll also discuss the latest software features, statistical updates, including Classification and Regression Trees (CART®) and Validation , and how faster collaboration and new levels of productivity can be achieved with Minitab now on the cloud.
This presentation was delivered by our Minitab Expert Jenn Atlas, Global Market Development Manager, it can definitely help you enhance your impact with the power of Minitab Statistical Software by your side. A webinar on-demand provides the recording of Jenn's presentation at https://hubs.ly/H0zyGYQ0. Register free to replay the webinar.
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Here is the list of topics we will cover today- I think you will find all of them helpful to you in some way.
One focus for this release was to make predictive analytics accessible to more users.
When it comes to analyzing data, some methods certainly seem easier than others. But we believe that analytics is for everyone- and that includes methods that can be categorized as Machine Learning. While model building might initially seem intimidating to you- it’s not as hard as you might think.
Machine Learning and Predictive Analytics continue to play a role in initiatives like digital transformation, and while these methods have broad application, they shouldn’t only be tools for data scientists.
Let me show you the enhancements around machine learning.
So we first introduced Classification and Regression trees in the spring of 2020 release of Minitab Statistical Software, and we’ve added to it now in 20. but many of you may not have experienced it yet, so let me introduce it if this is new to you. CART is a decision tree algorithm that is used for both regression and classification problems. The main idea behind CART is that we divide the predictor variables- or I should say we partition them into different regions so that the dependent variable (also known as the target variable) can be predicted more accurately. Think about CART as another useful tool for your toolbox- one that does not require many assumptions and can be done quite quickly.
Perform Faster
The all-in-one view allows the user to explore potential models without jumping from window to window:
Evaluate and select alternative models based on performance and complexity, using the Scree Plot Navigator
Examine the selected model in Tree View, including which predictor variables were brought into the model, where optimal splits were made, and node performance
Highlight an individual node in Tree View to zoom in on Node Detail: lineage or path to the node, counts, and performance summary
Another area where we have improved is for Model Validation. This is an important component of ML- and is something that is cumbersome for many data analysts- so they may skip this important step in their predictive modeling work.
And this really helps you with Better Decision making because
Validation prevents overfitting, and incorporating it gives a more conservative, and often more realistic view of model performance.
As you can see here with this illustration, on the left I can fit a model to data, but when I try to apply that same model to new data my results are not as wonderful. Overfitting means that a model does a good job of predicting the data that was used to build the model, but a poor job of predicting new data - which is often the goal of building the model in the first place.
I realize we are not going into great detail during this webinar, but if you would like more information, I strongly suggest you check out the machine learning webinar we have- it’s on demand so you can watch it any time- and it is available on our website under Events.
Variables measured on the same subject or unit are connected by a line, making it easy to visualize trends or patterns.
For example, it is easy to see that overall Urban has higher damage than Rural, but that Deer, which cause significant damage are only a problem in Rural areas