For those who aren’t glued to electoral politics, Labor Day typically marks the beginning of election season. Primaries are settled, pollsters stop sampling all adults to prioritize likely voters, and campaigns kick into high-gear.
Last November, I (finally) popped the big question and proposed! Since then, my fiance and I have been diligently planning our wedding. While we have most of the big-ticket items checked off (venue, catering, photography, etc.
Data manipulation and transformation is a fundamental part of any analysis. There are excellent tools in the R ecosystem for manipulating data frames (dplyr, data.table, and arrow, to name a few).
Generating prediction intervals with workboots hinges on a few core concepts: bootstrap resampling, estimating prediction error for each resample, and aggregating the resampled prediction errors for each observation. The bootstraps() documentation from {rsample} gives a concise definition of bootstrap resampling:
I recently picked up David Robinson’s book, Introduction to Empirical Bayes. It’s available online for a price of your own choosing (operating under a “pay-what-you-want” model), so you can technically pick it up for free, but it’s well worth the suggested price of $9.
I am an avid R user and will always advocate that others use R (or another programming language) for generating reproducible visualizations. In just about every organization, however, Excel plays an important role in an analyst’s toolkit.
In my role as an analyst, my team and I are required to put together reports that summarize each hospital’s patient satisfaction performance in a table. These are reviewed by our system’s executive leadership team and the hospital directors in monthly operational reviews (MORs).
A few months ago, Harrison Lavelle wrote a piece for Split Ticket reviewing the electoral challenges faced by house republicans who voted to impeach Donald Trump for his role in the assault on the capitol.
Sometimes, we want a model that generates a range of possible outcomes around each prediction and may opt for a model that can generate a prediction interval, like a linear model.
I’ve never built a house (shocking, I know), but from far too much time spent watching HGTV, I understand the basic gist of it. You lay a foundation, setup framing and walls, route mechanical and electrical, then work on final touches like painting and decorating (to be sure, I’m hand-waiving a lot of detail away here).