What is Data Science?
Its might be surprising that how we use data science I n everyday life, Marketing campaigns, Uber, Siri, price comparison sites, Amazon recommendations, gaming and image recognition, are all powered, to varying degrees, by data science.
Lean, also known as “Lean Manufacturing” or “Lean Production,” focuses on maximizing customer value by removing waste and eliminating defects. Lean tools are about understanding the process, looking for waste, preventing mistakes and documenting what you did.
Can you trust your data?
That's the very first question we need to ask when we perform a statistical analysis. If the data's no good, it doesn't matter what statistical methods we employ, nor how much expertise we have in analyzing data. If we start with bad data, we'll end up with unreliable results. Garbage in, garbage out, as they say.
On the heels of Healthcare Quality Week last week, we wanted to share our conversation with Dr. Sandy Fogel, the surgical quality officer at Carilion Clinic in Roanoke, VA.
Last week I was fielding questions on social media about Minitab 18, the latest version of our statistical software. Almost as soon as the new release was announced, we received a question that comes up often from people in pharmaceutical and medical device companies:
It’s usually not a good idea to rely solely on a single statistic to draw conclusions about your process. Do that, and you could fall into the clutches of the “duck-rabbit” illusion shown here: