Launched more than 20 years ago, the Aerospace Systems Design Laboratory (ASDL) at the Georgia Institute of Technology was created to fill the gap between education and industry, with the goal of fully preparing graduates to meet the needs of employers from day one. Is the mission succeeding? I recently
We’ve talked about the data. We’ve defined our thresholds for risk. Now it’s time to talk about how you can visualize the safety and quality signals from your ongoing clinical trials. If you want to minimize the impact of quality issues to the data or quickly address any safety concerns
To benchmark computer performance on statistical methods with big data, we can just generate random data and measure performance on that, right? Well, it could be that simulated data may not act the same as real data. Let’s find out. Logistic Regression Suppose that we are benchmarking logistic regression. So
Halloween is here. It’s a time when the weather gets colder and the leaves fall from the trees. A time when kids dress up in fun costumes and go trick-or-treating. A time when we start thinking about how to spend the holidays with our family and friends. And if you’re like me,
If you never have to deal with missing data, please count your blessings — nothing to see here today! However, for those of us who are not so lucky, I’ve got some good news: First, throughout JMP 11, we have added capabilities for dealing with missing data. The Reliability Growth
Sometimes emptiness is meaningful. If a loan applicant leaves his debt and salary fields empty, don’t you think that emptiness is meaningful? If a job applicant leaves this previous job field empty, don’t you think that emptiness is meaningful? If a political candidate fills out a form that has an
When you have millions of observations of real data and do a simple fit across two variables, if you don’t get a significant test, then it is strong evidence of fraud. The one kind of data that is reliably non-significant for very tall data tables is simulated data. We live
In semiconductor data, it is common for probe measurements that encounter an electrical short to exhibit measurements that are far out in the distribution, i.e., they are outliers. When we test that means are the same, these outlying values inflate our estimate of the standard deviation [sigma]. Remember that the
Purely random data has a 5% chance of being significant. Choose the most significant p-values from many tests of random data, and you will filter out the tests that are significant by chance alone. Suppose we have a process that we know is stable and consistent. We measure lots of
Welcome Home! Let me introduce you to the JMP Home Window for Mac OS X. When development began on JMP 11, the Mac OS X host development team started the process of investigating differences between our platform and the JMP product on Windows. We wanted to begin bridging the gaps