This is Tumblr’s Fault
The internet.
The internet.
I have a couple Tumblr blogs that I “follow” (read: stalk but don’t actually follow Tumblr-style) and one of them has been consistently commenting “WHAT ARE THOOOOOOOOOSE” on every…I don’t know, 10th thing they post. It took me a little bit to figure out where this was coming from, but I found it: Vine, of course!
Here’s a compilation of “WHAT ARE THOOOOOOOOOSE” edits, with the original vine being the first video in the set.
It’s probably an annoying to most people but I just find it hysterical for some reason.
Edit: Hahahahaha, oh my god, this is the greatest.
Sadness!
The last one.
I miss Ray.
The conversation at 4:46 is great.
Here we go again…
Nate gets possession of the condo today, which can mean only one thing: TIME TO START MOVING STUFF!
You’d think I’d just be indifferent to the nonsense that is moving by now, given all the times I’ve done it, but I still dislike it. And that’s all we’re going to be doing this weekend. And it’s going to be like 90 degrees while we do it.
But at least we’ll have our own new place once it’s done!
Edit: oh thank god, the previous owners cleaned the condo. It was super gross when we saw it last but now it’s clean and we don’t have to spend three hours scouring someone else’s pee off the bathroom floor or cleaning out old gross food from the kitchen sink/stove/fridge.
Heartstrung
Heeeeeeeeeeeeey have a thing.
It sucks, but it’s a thing.
(I need to learn how to draw.)
1st Grade Literary Tomfoolery
So today I spent a lot of time packing for the move (when am I not packing for a move?) and I came across an old story I’d written in a journal in first grade. I’d like to share it with you because a) I want to demonstrate that my writing ability has in fact not improved since first grade and b) I have nothing else of interest today.
I remember we had to write a story about Halloween for this particular writing assignment, but other than that it was pretty open. My incredibly creative title for this thing was “The Poisonous Pumpkin.”
Once there was a boy named Jacob. His dad said, “Son, we are moving to Pennsylvania! But first we must buy some pumpkins, for it is getting close to Halloween.”
“Okay,” said Jacob. “Give me some money and I’ll got to the store and buy six pumpkins.”
“Okay,” said his dad. “Here’s six dollars, one for each pumpkin. Put on your coat.”
“Alright,” said Jacob. “Bye!”
Soon he got to the pumpkin selling place. “Here’s six dollars for six pumpkins!” said Jacob.
“Okay,” said the pumpkin seller. “Pick your pumpkins.”
So Jacob found the best six pumpkins. He was about to go home when he saw a pumpkin with a scary face and lips already carved out. He put back one of his pumpkins and took that one.
When he got home his dad had already packed. “Come on, son!” he said. “Put your bike and the pumpkins you bought in the back of the car. By the way, that’s a very strange pumpkin you bought.”
“I know,” said Jacob. “It was already carved. Can you believe it?”
“Now son, don’t start making up stories.”
“But…”
“Now let’s go!”
So they got into the car and drove off. Finally they reached Pennsylvania. Jacob got out of the car. “What a house!” he said.
“Don’t forget the pumpkins,” his dad said.
“I won’t.” He opened the back door of the car…”Dad?” asked Jacob.
“What?”
“The pumpkin with the face already carved out…”
“Yes?” said his dad.
“It’s missing.”
“What?”
“It’s missing!”
“Is the window open?” asked his dad.
“Yes,” replied Jacob.
“Well, it probably fell out the window.”
“But we didn’t hit any bumps!” said Jacob.
“Yes we did,” said his dad. “The gravel road.”
“But those were just little bumps,” said Jacob. “Even I barely felt them.”
“Oh, let’s just forget about the pumpkin.”
The next day Jacob woke up. [best line in this whole damn story.]
“Come on Jacob!” Said his dad. “You don’t want to be late for the first day of school.” Jacob got up, got dressed, and went downstairs for breakfast. Jacob saw the old dry leaves out the window and remembered the crackling he heard that night. But before he could say anything to his dad, the school bus arrived.
“Hurry!” said his dad. Jacob got his backpack and ran outside. But the poisonous pumpkin was watching behind a bush. He knew that when Jacob got home he would have a friend with him, and that he could poison Jacob’s friend.
When Jacob did get home, he did have a friend named Andrew with him. Andrew was spending the night.
“Let’s go upstairs and play,” said Jacob.
“Okay,” replied Andrew.
They played until it was time for dinner. When Jacob and Andrew and his dad went to bed, the door opened.
“Did you hear that?” said Andrew.
“I sure did,” replied Jacob. “My dad’s asleep. Let’s go down and see!” They went downstairs, turned around, and looked out the door. There was the poisonous pumpkin with a can of pop and an ax in his vines.
“Run!” said Jacob. Jacob and Andrew ran as fast as they could, but the pumpkin came after them, waving its ax.
“Dad!” yelled Jacob. “The pumpkin’s alive!”
Suddenly, the as slipped from the vines and flew in front of Jacob. He quickly grabbed it. Then he ran after the poisonous pumpkin. The poisonous pumpkin was drinking his pop and spitting poison at Jacob. One shot almost hit him. After a long time of running, the pumpkin got tired. Soon, it collapsed. Jacob chopped him up and burned him. The poisonous pumpkin was never heard of again.
The End.
Riveting. Man, that plot skips around like a scratched CD and then just crashes and burns, doesn’t it? Also, I love how the pumpkin has to infuse pop with the poison in order for it to be effective. And that he needed an ax, too, like as a backup. Way to write a villain, Claudia.
There are indeed illustrations for this, but they’re even more embarrassing than the writing, so you don’t get those.
Anyway.
RIP James Horner
SAD NEWS: the amazing composer James Horner was, in fact, killed the other day when his plane went down in California.
Who is James Horner, you ask?* He’s the conductor/composer behind a lot of really fantastic film scores. Examples:
- The Land Before Time
- An American Tail: Fievel Goes West
- We’re Back! A Dinosaur’s Story
- Braveheart
- Titanic
- The Perfect Storm
He also did the score for Apollo 13, which has my favorite movie song ever (listen to it here!). Seriously. Awesome.
R.I.P. James Horner. You were an awesome composer.
*Or maybe you don’t ask this. Maybe more people are familiar with his name and works than the news would have us believe.
Lens Factor
Alrighty, so every once and awhile I used to watch The Doctors on TV if I happened to catch them. Then they did this nonsense so they lost a lot of credit with me and I’d never stopped to watch them since.
However, today I was trying to find a channel to “watch” while I packed my crap for the move, and the channel playing The Doctors was the only decent choice, so I just had it on in the background while I did stuff in the back room.
I happened to come back out in the living room when they were talking about these, and I’m actually glad I did, because these special colored lenses seem pretty cool.
From the website: “The Irlen Method has earned millions of fans by providing a long-term, expertly developed solution to reading problems, headaches, light sensitivity, ADD and ADHD, autism and many other ailments–by directly addressing a core problem: the brain’s inability to process visual information.”
Check out the clip:
http://www.thedoctorstv.com/videos/test-for-perception-processing-disorder
Super interesting! I think it’s something that could help a lot of people.
Woo!
This blog is trash.
And so am I!
Gold
Want to see the difference a good remix makes with a song?
YOU KNOW YOU DO!
Okay. So here is Gold by Gabriel Rios, the Thomas Jack Radio Edit version.
Nice, huh? It’s my most recent 5-star and my currently most-played song.
And here’s the original Gold by Gabriel Rios:
Such a different mood, oh my god. At this point in the year I’m slowly starting to formulate the premise for my NaNoWriMo endeavor, and the un-remixed song fits perfectly with the general idea/atmosphere I’ve got going.
Sorry, I just really like the contrast between the original and this particular remix.
WOO!
Milestone?
Guess what? I’m 10,000 days old today. I’m not sure if that sounds like too few days for ~27.25 years or too many.
Also, that means that if I’d started blogging the day I was born*, today I would finally have finished with my 10,000 Days project.
I’ll be 20,000 days old on November 4, 2042.
ANYWAY, that’s all you get today because I feel like garbage.
*One-day-old Claudia would probably have posted a blog of equal or better quality than 18-something-years-old Claudia, just sayin’.
Baa Baa Black Sheep, Have You Any Blogs?
GUYS I HAVE PURCHASED A SLIDE RULE
Story: Okay, so it’s been like four days since I’ve gone walking, which is four days too many, so I decided to walk down to Chinook Centre (about 13 miles round trip). However, once I got down there, I changed my mind and decided to check out the Value Village close to the mall instead. I was just going to look around since I have practically no money at the moment, but when I was digging through the miscellaneous baskets on one of the shelves I came upon THIS:
And since it was only $2, I had to get it. So I did.
It didn’t have a manual or anything with it and I’ve never used a slide rule before in my life, so I went to teh internetz to see if I could figure out how it works. And it’s super cool! Let me show you a few basic things.
Multiplication: Say I wanted to multiply 1.2 by 3. What I’d do is find 1.2 on the “D” scale and slide my “C” scale so that its 1 is right above 1.2.
Then I would find 3 on my “C” scale. Whatever number on the “D” scale is right below the 3 on the “C” scale is the product of 1.2 times 3: 3.6!
Division: Let’s do 6 divided by 4. You take the divisor, 4, on the “C” scale and set it above the dividend, 6, on the “D” scale like so:
The quotient is whatever number is on the “D” scale right below the 1 on the “C” scale: 1.5!
Finding a square root: Say I wanted to find the square root of 5. I would find 5 on the “A” scale. Whatever number on the “D” scale that is below that 5 is the square root: approximately 2.23!
Super cool. You can also do things like cubes/cube roots, proportions, logarithms, and sines and tangents of angles, but I’m still learning.
WOO!
I need more colored pencils.
Heeeeeeeey, have some airport art. Certainly not my best, but I didn’t have that much time in Seattle (which is abnormal) so this is all you get.
Goodbyes
Today was grandma’s funeral mass and burial. It was sad, of course, but it wasn’t too sad; my dad gave the eulogy and his goal was to keep it light and full of funny stories (which it was; even the altar servers were laughing) and yesterday and today were basically spent telling stories about grandma and grandpa and all the weirdness of our family. I think that’s the way grandma would have wanted it.
In case anyone’s interested, here’s the obituary my dad wrote for her that appeared in the LA Times:
Emily Ortega Mahler, of Brea, California, formerly of Huntington Park and Los Angeles, CA, died peacefully in her sleep on June 3, 2015. Born June 27, 1927, she is predeceased by her parents Emilio C. and Mary Magdalene (Bustamante) Ortega of Huntington Park, CA and her late husband of 56 years, Robert A. Mahler of Los Angeles, CA. Emily is survived by her sister Evelyn (Ortega) Sheppard of Placentia, CA; son Robert Mahler of Moscow, ID; son Blake Mahler of Rancho Santa Margarita, CA; son Lance (Kerry) Mahler of La Habra, CA; daughter Vicke (Tom) Helmer of Greenwood Village, CO; six grandchildren; and three great-grandchildren. Emily went to St. Matthias elementary school in Huntington Park and to Huntington Park High School. She graduated from the University of Southern California in 1949. After completing her degree and while Bob was in the service she taught school in small California towns including Maricopa and Susanville. Later she taught in several junior high and high schools in the Los Angeles School District. After many years in the classroom her energies were spent raising four children that included involvement in supporting organizations such as the PTA, Boy Scouts and Girl Scouts. As her children matured she changed emphasis and became a professional volunteer. Emily was especially proud of being a Trojan (USC). She always supported her alma mater. She was a charter member of Trojan Guild, an important support group for USC, and served on many committees within the University. She has been a USC football season ticket holder since 1967. She will miss tail-gating, traveling to South Bend for the great rivalry between USC and Notre Dame, and going to all of the home football games at the Coliseum. She is especially proud that two of her children, Vicke and Blake are USC graduates along with her son-in-law Tom. Over the past 20+ years she has volunteered one day a week at the USC Norris Cancer Center. Many of her best friends were met either in school at USC or at events that supported the institution. Emily was a professional volunteer and always a member of “something.” The more important “somethings” included the Mother Goose Guild support organization to Children’s Hospital of Orange County (CHOC), several chapters of the American Association of University Women (AAUW), and Friends of California State University, Fullerton. Over the years she received dozens of awards for her efforts. In 1984 she received Brea’s first “Woman of the Year” award. Emily and her husband Bob were world travelers. They were able to visit all seven continents. She took tens of thousands of pictures to document her travels. Some of her favorite trips took her China, Antarctica, safaris in Africa, the Andes of South America and to Spain. Her last trip in 2014 was a Panama Canal cruise. Emily was always proud of her California family history. The Ortega’s, originally from northern Spain, have been in California for eight generations. Her great, great, great grandfather discovered San Francisco Bay as a scout for the Portola Expedition. She had relatives who are buried at 17 of California’s 21 missions. The Ortega Highway in Orange County is named for her family and her father, Emilio, founded the California-based Ortega Chili Company in the 1920’s. Many of those original products including Snap-E-Tom and canned green chilies are still on the market today. In college she wrote a history of this distinguished family. Being a catholic was always an important part of Emily’s identity. After marrying Bob she became a parishioner of St. Raphael’s in south-central Los Angeles where her children attended school. In addition to being active at St. Raphael she served as the school’s kindergarten teacher twice. She was a member of St. Angela Merici parish in Brea, California for the last 48 years. Her faith has allowed her to look forward to again seeing her husband Bob, parents Emilio and Mary, nephew Michael and many good friends who left before her on the other side of life, as we know it.
GUESS WHAT TIME OF THE YEAR IT IS
Awwwwww yeeeeeeeeeeeeah.
Yes, I’m going to read this every June/July until I die.
No, I don’t think that’s a sign of an unhealthy obsession.
Also, I posted this awhile back, but I’m going to post it again because it’s a really good discussion of a good amount of Leibniz’ philosophical viewpoints.
SCRATCH
So here’s something kinda cool: earlier today I wanted to find a drawing that I’d favorited on DeviantArt but didn’t want to go and log into my DA account itself, so instead what I did was Google “LadyLeibniz,” my DA username, so that I could just see my profile and find the drawing I wanted from my favorites list.
However, when I was looking at the search results, I found that a good number of my drawings have been posted on Tumblr and have quite a few notes—29, 156, 194, 621—snazzy! I had no idea my art had been Tumbld.
This also makes me appreciate even more those people who take time to properly credit the artists of pictures that they post to Tumblr. All of the ones I found had the title plus “by LadyLeibniz” (which, of course, is why I could find them by Googling) and most had links right back to my DA account.
Sorry, I just think that’s cool.
Scents and Scent-Ability
Want to know a weird thing I do every once and awhile? Every so often I catch myself making a mental comment about the smell of something. Even though I can’t smell.
Example: tonight (well, this morning) when I went to make dinner I opened my fridge. I’d just bought a bunch of broccoli and that (apart from a container of feta cheese) was the only thing in the fridge. I saw the broccoli and immediately thought, “man, this fridge smells like broccoli.”
Did it? No idea. But that was just my automatic reaction. Similar smell-related reactions happen every once and awhile.
So I have no idea if that means that I can kind of smell like on a subconscious level or I’m just accustomed to associating the idea of smell with things that people have told me have a distinctive odor (like broccoli or garbage or things like that) and thus automatically assume whatever contains such odorous things must be infused with the smell. I’d bet on the latter, but who knows.
Weird brain is weird.
TWSB: Analyzing Old Faithful with Faithful Old Regression
Alright y’all, sit your butts down…it’s time for some REGRESSION!
If you’ve ever gone to Yellowstone National Park, you likely stopped to watch Old Faithful shoot off its rather regular jet of water. In case you’ve never seen this display, have a video taken of an eruption in 2007:
(Side note: you can hear people frantically winding their disposable cameras throughout the video. Retro.)
What does Old Faithful have to do with regression, you ask?
Well, while the geyser is neither the largest nor the most regular in Yellowstone, it’s the biggest regular geyser. Its size, combined with the relative predictability of its eruptions, makes it a good geyser for tourists to check out, as park rangers are able to estimate when the eruptions might occur and thus inform people about them. And that’s where regression comes into play: by analyzing the relationship between the length of an Old Faithful eruption and the waiting time between eruptions, a regression equation can be created that can allow for someone armed with the length of the last eruption to predict the amount of waiting time until the next one. Let’s see how it’s done!*
Part 1: What is Regression?
(This is TOTALLY not comprehensive; it’s just a very brief description of what regression is. There are a lot of assumptions that must be met and a lot of little details that I left out, but I just wanted to give a short overview for anyone who’s like, “I know a little bit about what regression is, but I need a bit of a refresher.”)
Regression is a statistical technique used to describe the relationship between two variables that are thought to be linearly related. It’s a little like correlation in the sense that it can be used to determine the strength of the linear relationship between the variables (think of the relationship between height and weight; in general, the taller someone is, the more they’re likely to weigh, and this relationship is pretty linear). However, unlike correlation, regression requires that the person interested in the data designate one variable as the independent variable and one as the dependent variable. That is, one variable (the independent variable) causes change in the other variable (the dependent variable, “dependent” because its value is at least in part dependent on the changes of the independent variable). In the height/weight example, we can say that height is the independent variable and weight the dependent variable, as it makes intuitive sense to say that height affects weight (and it doesn’t really make sense to say that weight affects height).
What regression then allows us to do with these two variables is this: say we have 30 people for which we’ve measured both their heights and weights. We can use this information to construct an equation of a line—the regression line—that best describes the linear relationship between height and weight for these 30 people. We can then use this equation for inference. For example, say you wanted to estimate the weight for a person who was 6 feet tall. By plugging in the value of six feet into your regression equation, you can calculate the likely associated weight estimate.
In short, regression lets us do this: if we have two variables that we suspect have a linear relationship and we have some data available for those two variables, we can use the data to construct the equation of a line that best describes the linear relationship between the variables. We can then use the line to infer or estimate the value of the dependent variable based on any given value of the independent variable.
Part 2: Regression and Old Faithful
We can apply regression to Old Faithful in a useful way. Say you’re a park ranger at Yellowstone and you want to be able to tell tourists when they should start gathering around Old Faithful to watch it spout its water. You know that there’s a relationship between how long each eruption is and the subsequent waiting time until the next eruption. (For the sake of this example, let’s say you also know that this relationship is linear…which it is in real life.) So you want to create a regression equation that will let you predict waiting time from eruption time.
You get your hands on some data**—recorded eruption lengths (to the nearest .1 minute) and the subsequent waiting time (to the nearest minute) and you use this to build your regression equation! Let’s pretend you know how to do this in Excel or SPSS or R or something like that. The regression equation you get is as follows:
WaitingTime = 33.97 + 10.36*EruptionTime
What does this regression equation tell us? The main thing it tells us is that based on this data set, for every minute increase in the length of the eruption (EruptionTime), the waiting time (WaitingTime) until the next eruption increases by 10.36 minutes.
It also, of course, gives us a tool for predicting the waiting time for the next eruption following an eruption of any given length. For example, say the first eruption you observe on a Wednesday morning lasts for 2.9 minutes. Now that you’ve got your regression equation, you can set EruptionTime = 2.9 and solve the equation for the WaitingTime. In this case,
WaitingTime = 33.97 + 10.36*2.9 = 33.97 + 30.04 = 64.01
That means that you estimate the waiting time until the next eruption to be a little bit more than an hour. This is information you can use to help you do your job—telling tourists when the next eruption is likely.
Of course, no regression equation (and thus no prediction based off a regression equation) is perfect—I’ve read that people who try to predict eruptions based on regression equations are usually within a 10-minute margin, plus or minus—but it’s definitely a useful tool in my opinion. Plus it’s stats, so y’know…it’s cool automatically.
END!
*I actually have no idea if Yellowstone officials actually have used regression to determine when to tell crowds to gather at the geyser; I can’t remember how it’s all even set up at the Old Faithful location, seeing as how I was like six years old when I saw it and Nate and I were thwarted in our efforts to see it a few weeks ago. But hey, any excuse to talk about stats, right?
**There are a decent number of Old Faithful datasets out there; I chose this one because it was easy to find and decently precise with regards to recording the durations.
OH CRAP I FORGOT HOW TO BLOG
GUYS.
guysguysguysguysguysguysguys
I’m hyper.
Also, you know you’ve been watching too much Food Network when you have a dream in which Guy Fieri breaks into your house, chugs the entirety of your salt shaker’s contents, and then blasts through the roof using his salt-powered rocket feet. Not rocket shoes, rocket feet.
Edit: holy crap, Guy is 47? He doesn’t look that old. Must be the salt.
PROBABLY NSFW!
OH MY GOD THIS IS THE BEST THING.
(Don’t ask me how I found this.)
(Okay fine it was Tumblr.)
BLOG UPDATE!
Hello, readers!
So remember way back on the first of May when I promised I’d post these things at least once a week?
And remember those 4-ish weeks in between then and now during which I posted exactly 0 blogs?
Well, I have an excuse for the initial delay: I wanted to wait until I got back from my road trip with Nate so that I could post all of the trip-related blogs together. So there you go.
I promise to adhere to that “at least once a week” promise now.
WOO!
Eigenblogger 