Analytics for social innovation
During a recent livestream event, Ayana Littlejohn of SAS discussed one of the many social innovation projects she’s worked on during her career.
During a recent livestream event, Ayana Littlejohn of SAS discussed one of the many social innovation projects she’s worked on during her career.
Some modeling problems will require you to look at a larger set of outputs to diagnose the health of the model and understand and communicate the results effectively. This post focuses on variance inflation factors, parameter estimates and interaction plots.
Even with the default settings in JMP as a starting place, there are still many outputs that one has to prioritize and figure out how to interpret.
“Which model outputs should I be looking at? There are so many! And how should I interpret them?” The biggest barrier I have seen from analysts who want to use models to solve their problems is that they get confused and/or bogged down with all the outputs they need to look at once they’ve built a model. This is the first of a five-part blog series aimed at helping analysts get started in their qu...
Sometimes you need to look at a few more outputs to improve your model or better understand what it is telling you...just like sometimes you need to add some healthy fats to your meal to satisfy your hunger and body's nutritional needs. This post goes into additional outputs that you can review in addition to those that were described in the previous week's post when necessary.
For Black History Month 2019, Montgomery College sponsored the Black Data Project, a contest to draw attention to the data analysis and visualization work of W.E.B. Du Bois. They selected 12 of his works from the 1900 World’s Fair in Paris and invited participants to create derivative works.
Here's what you'll find at the entirely online Discovery Summit Europe 2021.
Quantiles are often used for univariate outlier detection. This episode describes quantiles and the calculations used to flag whether data points are unusual, compared to the underlying distribution, and therefore warrant further investigation.
We look at identifying outliers in multiple dimensions using the Mahalanobis distance. We also take a quick look at T2, which is a simple extension of the Mahalanobis distance. Sample data are attached.
Outliers can be the bane of existence of many data analytics projects. Sometimes they are bad, and other times they can be good! But if we don't recognize Outliers and deal with them early in our analyses, results can be skewed, suboptimal settings found, and information missed! Join me in this, the first of a series of blogs about identifying and dealing with outliers.
Predictive modeling is all about finding the model that accurately predicts the outcome of interest. The new Model Screening platform in JMP Pro 16 provides an efficient workflow for simultaneously fitting, comparing, exploring, selecting and then deploying the best predictive model.
Ready for some good statistics?
“如果有一個從0分到10分的階梯,頂層的10分代表你可能得到的最幸福的生活,底層的0分代表你可能得到的最差的生活。你覺得自己現在在哪一層呢?”
The p in p-value can stand for a lot of things like Probability, Perplexing, and Part.
Machine learning, artificial intelligence and other buzzwords are thrown around a lot, but what do these terms mean other than methods to solve problems?
There are many valuable take-aways from this episode of Statistically Speaking. How can you gain a greater understanding of markets and customers to derive better informed policy and product decisions and strategies?
JMP Clinical 8 has arrived, and there is lots to talk about.
JMP Clinical 8 includes a new Medical Query Risk Report to analyze medical queries with a risk plot that includes up to four tables of counts and percentages.
Here are some tips for submitting abstracts for Discovery Summit.
When a clinical trial is ongoing, periodic safety updates to regulatory agencies are required.