Randomness: Crash Course Statistics #17

There are a lot of events in life that we just can’t predict, but just because something is random doesn’t mean we don’t know or can’t learn anything about it. Today, we’re going to talk about how we can extract information from seemingly random events starting with the expected value or mean of a distribution and walking through the first four “moments” – the mean, variance, skewness, and kurtosis.

Note: There are many formulas to calculate skewness and kurtosis (https://www.itl.nist.gov/div898/handbook/eda/section3/eda35b.htm), our formulas deal with what they have in common, their moment generating functions.

More on sheep study: http://aiweirdness.com/post/171451900302/do-neural-nets-dream-of-electric-sheep

More on fecal matter study: http://aem.asm.org/content/early/2018/02/05/AEM.00044-18.abstract

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Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:

Mark Brouwer, Glenn Elliott, Justin Zingsheim, Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Divonne Holmes à Court, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, SR Foxley, Sam Ferguson, Yasenia Cruz, Eric Koslow, Caleb Weeks, Tim Curwick, Evren Türkmenoğlu, D.A. Noe, Shawn Arnold, mark austin, Ruth Perez, Malcolm Callis, Ken Penttinen, Advait Shinde, Cody Carpenter, Annamaria Herrera, William McGraw, Bader AlGhamdi, Vaso, Melissa Briski, Joey Quek, Andrei Krishkevich, Rachel Bright, Alex S, Mayumi Maeda, Kathy & Tim Philip, Montather, Jirat, Eric Kitchen, Moritz Schmidt, Ian Dundore, Chris Peters, Sandra Aft, Steve Marshall

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Measures of Central Tendency: Crash Course Statistics #3

Today we’re going to talk about measures of central tendency – those are the numbers that tend to hang out in the middle of our data: the mean, the media, and mode. All of these numbers can be called “averages” and they’re the numbers we tend to see most often – whether it’s in politics when talking about polling or income equality to batting averages in baseball (and cricket) and Amazon reviews. Averages are everywhere so today we’re going to discuss how these measures differ, how their relationship with one another can tell us a lot about the underlying data, and how they are sometimes used to mislead.

Crash Course is on Patreon! You can support us directly by signing up at http://www.patreon.com/crashcourse

Thanks to the following Patrons for their generous monthly contributions that help keep Crash Course free for everyone forever:

Mark Brouwer, Nickie Miskell Jr., Jessica Wode, Eric Prestemon, Kathrin Benoit, Tom Trval, Jason Saslow, Nathan Taylor, Divonne Holmes à Court, Brian Thomas Gossett, Khaled El Shalakany, Indika Siriwardena, Robert Kunz, SR Foxley, Sam Ferguson, Yasenia Cruz, Daniel Baulig, Eric Koslow, Caleb Weeks, Tim Curwick, Evren Türkmenoğlu, Alexander Tamas, Justin Zingsheim, D.A. Noe, Shawn Arnold, mark austin, Ruth Perez, Malcolm Callis, Ken Penttinen, Advait Shinde, Cody Carpenter, Annamaria Herrera, William McGraw, Bader AlGhamdi, Vaso, Melissa Briski, Joey Quek, Andrei Krishkevich, Rachel Bright, Alex S, Mayumi Maeda, Kathy & Tim Philip, Montather, Jirat, Eric Kitchen, Moritz Schmidt, Ian Dundore, Chris Peters, Sandra Aft, Steve Marshall

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Mean/Funny Comments To My Mermaid Videos: A Selection | Stella the Siren

So I decided to read some of the mean comments people are posting to my YouTube videos, it is just too funny. I blacked out their last names or full user names, even though I guess what they posted is public – it just felt too spiteful to publicly call them out XD
Anyway, enjoy! Have fun and Merry Christmas!

P.S: Don’t worry, I’m not insulted by these comments, I just think they’re their own kind of hilarious. I get way too many nice comments to let these get to me ^^