Back on March 19th, I wrote about some ways which I processed some NCAA Men’s Basketball data to make predictions for the crazy March Madness tournament, bragging about how well I had done with my analysis most years, beating former President Obama’s Barackets almost all the time, and all. Well, it seems I had really jinxed myself because this year’s March Madness results were so unpredictable that just picking by tournament seedings exactly as they were landed you in the top ten percentile! In other years, you’d be about middle of the pack. That’s because the upsets this year were so generally unpredictable that those looking to find upsets often got stung by getting many wrong, but then also stung by getting many expected winners wrong! Double-whammy, if you will!
Supposedly, we dream four to six times per night. Remembering, them, though, is a different matter. I’m not talking about remembering them in super details, or a long time. I’m just talking about realizing you had a dream when you wake up, whether you could only say a few words about it like something that was involved, or describe it in detail. For dream memory, the going rate seems to be one or twice a week, though the distribution is rather diverse, which is why the rate is once or twice, a 100% margin of error essentially. That’s all great to know, but it has no context for the individual, like me. As a result, with my daily activities tracker that I use to track my performance towards my many resolutions, I had decided to track my dreaming as well.
Wow! Where did this wind just suddenly come from?
Wait, that’s no wind. That’s a win, and that’s the American people, and global citizens, reacting to the Joe Biden and Kamala Harris victory in the US election!
Congratulations, Joe, Kamala, and citizens of the world!!!
I just added a Tableau Public set of dashboards showing combined 2015 and 2016 year results for the Canadian Community Health Survey (CCHS), which allowed for comparisons and rate calculations at geographies smaller than provincial and territorial levels. These included Census Metropolitan Areas (CMAs) and health areas (units, zones, districts, etc.), as well as combined large, medium and small population centres, and rural areas, within a province or territory. The roughly 680 thousand rows of data, including calculations of statistical significance in differences done by Statistics Canada, allowed for some amazing comparisons… and eye opening results! There currently is nothing else quite like this published by anybody to show CCHS results!
Click on the following link if you just want to use the dashboards directly, without explanations.
And click on the following link if you want the latest version of CCHS results, 2016, but without geographies below the provincial level.
If you view it and have any questions or feedback, please leave them there so the discussions can be in one place as much as possible.
The combined year results, with much more granular geographic results, will be explained soon, but are already posted here if you want to look ahead of time. Thank you.