R for logistic regression in health data analytics is a reasonable choice, if you know what packages to use. You don’t have to use SAS! My blog post provides you example R code and a tutorial!
Tag Archives: business research
12
Apr
Apr
Statistics trivia for data scientists will refresh your memory from the courses you’ve taken – or maybe teach you something new! Visit my blog to find out!
21
Mar
Mar
WISE Summit 2023 at Northeastern University was the biggest and best one so far! I was honored to lead a business intelligence workshop, as I talk about in my blog post.
12
Mar
Mar
CitePeeps is a new online community of scientific authors focused on increasing the number of citations to their published works. Join us!
28
Feb
Feb
GitHub beginners – even in data science – often feel intimidated when starting their GitHub accounts and trying to interact with the web page. Don’t be shy! Catch the highlights from a recent GitHub beginners workshop I held!
25
Nov
Nov
End-to-end AI pipelines are being created routinely in industry, and one complaint is that academics can only contribute to one component of the pipeline. Really? Read my blog post for an alternative viewpoint!
06
Jun
Jun
“AI on the edge” was a new term for me that I learned from Marc Staimer, founder of Dragon Slayer Consulting, who was interviewed in a podcast. Marc explained how AI on the edge poses a data storage problem, and my blog post proposes a solution!
15
Apr
Apr
What is the VA – which stands for Veterans Affairs? This United States federal agency is tasked with ensuring veterans receive their benefits – especially health benefits. Unfortunately, as my blog post explains, the VA is facing a myriad of challenges today, including funding problems as well as corruption.
16
Mar
Mar
Wondering what we mean by the National Institutes of Health (NIH)? In my blog post, I explain the NIH’s role in appropriating funding for research, and demystify its role in implementing federal priorities.
18
Feb
Feb
GPower case example shows a use-case where we needed to select an outcome measure for our study, then do a power calculation for sample size required under different outcome effect size scenarios. My blog post shows what I did, and how I documented/curated the results.
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