Dear Maslow: I want no part of your pyramid scheme!
It’s a survey! Give me a break, today was a very long, cold, bus-involved day.
1. WERE YOU NAMED AFTER ANYONE?
Nopers.
2. WHEN WAS THE LAST TIME YOU CRIED?
Uh…this morning.
3. DO YOU LIKE YOUR HANDWRITING?
It’s microscopic. I dig it.
4. WHAT IS YOUR FAVORITE LUNCH MEAT?
Haha, I haven’t had meat in forever. Tuna, though, I suppose.
5. DO YOU HAVE KIDS?
Kids are gross.
6. IF YOU WERE ANOTHER PERSON, WOULD YOU BE FRIENDS WITH YOU?
I’d push me in front of a bus.
7. DO YOU USE SARCASM?
Pfft, no.
8. DO YOU STILL HAVE YOUR TONSILS?
Indeed.
9. WOULD YOU BUNGEE JUMP?
I’d sky dive, but never bungee jump.
10. WHAT IS YOUR FAVORITE CEREAL?
Reese’s Puffs! Or Mini Wheats. OR Cinnamon Toast Crunch. Cereal owns.
11. DO YOU UNTIE YOUR SHOES WHEN YOU TAKE THEM OFF?
Yup.
13. WHAT IS YOUR FAVORITE ICE CREAM?
Oreo!
14. WHAT IS THE FIRST THING YOU NOTICE ABOUT PEOPLE?
I really don’t know.
15. RED OR PINK?
Hot pink.
16. WHAT IS YOUR LEAST FAVORITE THING ABOUT YOURSELF?
Must I answer this?
17. WHO DO YOU MISS THE MOST?
My old roomies!
18. DO YOU WANT EVERYONE TO COMPLETE THIS LIST?
CONFORM!
19. WHAT COLOR PANTS AND SHOES ARE YOU WEARING?
Pink pants, no shoes.
20. If you could have one superpower, what would it be?
Why isn’t this question in ALL CAPS?
Time manipulation.
21. WHAT ARE YOU LISTENING TO RIGHT NOW?
Gramophonedzie’s Brazilian.
22. IF YOU WERE A CRAYON, WHAT COLOR WOULD YOU BE?
Radical Carrot!
23. FAVORITE SMELLS?
Why does every survey ask this?
24. WHO WAS THE LAST PERSON YOU TALKED TO ON THE PHONE?
Uh…a receptionist.
25. HOW DO YOU KNOW THE PERSON WHO SENT THIS TO YOU?
No one sent it, I’m my own man!
26. FAVORITE SPORTS TO WATCH?
Figure skating.
27. HAIR COLOR?
Black.
28. EYE COLOR?
Hazel.
29. DO YOU WEAR CONTACTS?
Nopers.
30. FAVORITE FOODS?
Potatoes, M&Ms, spaghetti squash, pasta.
31. SCARY MOVIES OR HAPPY ENDINGS?
Don’t care.
32. LAST MOVIE YOU WATCHED?
Supersize Me.
33. WHAT COLOR SHIRT ARE YOU WEARING?
Orange.
34. SUMMER OR WINTER?
Summer.
35. HUGS OR KISSES?
Pantslessness.
37. DESCRIBE YOUR PENCIL CUP.
I HAVE NO SUCH THING.
38. FAVORITE ARTIST(s)?
I dig Escher.
39. WHAT BOOK ARE YOU READING NOW?
Pasternak’s Dr. Zhivago.
40. WHAT IS ON YOUR MOUSE PAD?
I drew a mouse in the shape of my mouse on it. Yeah. I’m that cool.
41. WHAT DID YOU WATCH ON TV LAST NIGHT?
I don’t have TV.
42. FAVORITE SOUND(S).
Sleepyhead on repeat, anything with good bass, the sound Kraft macaroni and cheese makes when you stir it (don’t ask), and the sound of it not raining.
43. ROLLING STONES OR BEATLES?
Beatles!
44. WHAT IS THE FARTHEST YOU HAVE BEEN FROM HOME?
Stockholm.
45. DO YOU HAVE A SPECIAL TALENT?
Being a weirdo.
46 WHERE WERE U BORN?
Moscow.
47. FAVORITE PIECE OF JEWELRY?
Probably the necklace Aaron gave me. I don’t really have jewelry.
48. HOW DID YOU MEET YOUR SPOUSE/SIGNIFICANT OTHER?
I don’t have one of those “spouse/significant other” things.
49. MAYO OR MUSTARD
Mayo.
50. IF YOU COULD HAVE DINNER WITH ANY FAMOUS PERSON OR HISTORICAL FIGURE (dead or alive) WHO WOULD IT BE?
Leibniz!
Today’s song: Le Vrai le Faux by Jérôme Minière
This Week’s Science BloAAAAH GOD MY REALITY
Today we enter the world of auxetics, materials that, when stretched, expand perpendicular to the applied force.
Wait, what? This:
It sounds counterintuitive at first (at least it did for me), but this type of hexagonal material seems familiar for whatever reason. These materials have a negative Poisson’s ratio—thus their expansion when stretched. Most materials have a positive Poisson’s ratio, since they thin as they stretch. According to one article I’ve read, Poisson’s ratio is defined as minus the transverse strain divided by the axial strain in the direction of stretching force…Poisson’s ratios, denoted by a Greek nu, n, for various materials are approximately 0.5 for rubbers and for soft biological tissues, 0.45 for lead, 0.33 for aluminum, 0.27 for common steels, 0.1 to 0.4 for cellular solids such as typical polymer foams, and nearly zero for cork.” This article was written back in 1987, back before auxetic materials had been thoroughly examined. Now there are models that have just been put out to help explain the behavior of this weird stuff that occurs naturally in some rock, bone, and, apparently, paper.
More info here, here, and here. Cool, huh?
Today’s song: Mashina by NikitA
NaNoWriMo: T-minus 30 days
WOO!
The only good thing about October is that there are only 31 (30 now) days left until NaNo starts. Seriously. Every October for like the past three years has blown heavy metal chunks for me. Screw you, October.
I don’t have a definite plot in place. Actually, I do. I have like five definite plots in place. I just have to choose which one to implement. I’m leaning strongly towards the road trip/religious undertones one, but I might genre ditch and go for a more sci-fi story, just to annoy myself and try to work within a genre of which I’m not a big fan.
Who knows? I didn’t know where I was going with things last year, but I finally got an idea on paper that I’d had in my head for awhile.
Anyway.
Today was probably the last sunny day of the year up here, so I took the opportunity to test out the accuracy of the pedometer feature on the new Nano by comparing it to a regular old pedometer.
Not too big of a discrepancy, considering I spent like an hour of those three hours wandering around in Safeway. I think the Nano is more sensitive to “wandering” steps (as opposed to the more deliberate “get out of my way, I’m faster than you” steps) than the pedometer, hence the difference. I’d also trust the Nano’s calorie counter thingy more, since you can actually set your weight, something you can’t do on the pedometer.
And yes, it took me three hours to go ~11,000 steps. Like I said, Safeway, plus the whole “maybe I’ll stop and wait for the bus, ‘cause I have no damn idea where I am” ordeal when I couldn’t find the store I was looking for.
OH YEAH, and this:
I found this movie via Netflix and was going to watch it in its entirety tonight, but this song from the opening sequence totally ruined that, ‘cause I had to go find it, download it, and listen to it on repeat for about three hours. Apparently the movie is like Inception, but better.
Today’s song: Mediational Field by Susumu Hirasawa
Are invertebrates not allowed to drink Orange Crush?
So I impulse-bought an iPod Touch off of eBay this afternoon for no other reason than “hey look, this auction has 31 seconds left and I have money in my bank account!”
Who does that?
I now have THREE MOTHERFUCKING IPODS. I don’t even have the correct number of ears to justify that. What sane human being needs three iPods?
And I don’t even consider myself an Apple whore. I’ve never personally owned a Mac, the whole iPhone thing seems over-hyped to me, and I still don’t know what the hell the iPad actually is (aside from “a magical and revolutionary product at an unbelievable price”).
I’d sell Ye Olde iPod Classic, but it’s 80 gigs (SIZE OF OLD VAIO WTF) and it’s the only one compatible with my little car stereo plug-in thingy (meaning it charges as it plays). Plus it’s dented all to hell and I don’t know how I could convince a buyer that it works fine when it looks like a fat dude threw an elbow into its stainless steel.
Shiny new golden Nano is obviously staying, ‘cause I bought him less than a month ago and I adore the color. Nano also works best on the bus ‘cause he’s small and easy to deal with when I’m also carrying book/purse/umbrella/groceries.
And selling new Touch would be dumb.
Best plan of action: wait till Touch gets here, fondle the ever-living hell out of it and its Wi-Fi, then determine what Xbox games I should sell to compensate for my lack of restraint. I need to get rid of that old copy of Fallout 3, anyway.
Well, at least I don’t impulse-buy houses (that would make me my mom).
Today’s song: Mozart’s Mass in C Minor: Kyrie, performed by The Hungarian Radio Chorus
September’s Song Review
Today’s the 270th day of the year. It’s also September 30, which means it’s time for another month’s music review!
Graph of genres:
Mean song length: 3:54
The five star: Alejandro by Lady Gaga
Today’s song: Sour Cherry by The Kills
Multicollinearity revisited
So as none of you probably remember, I did a blog in April (March?) on the perils of dealing with data that had multicollinearity issues. I used a lot of Venn diagrams and a lot of exclamation points.
Today I shall rehash my explanation using the magical wonders that are vectors instead of Venn diagrams. Why?
1. Because vectors are more demonstrably appropriate to use, particularly for multiple regression,
2. I just learned how to do it, and
3. BECAUSE STATISTICS RULE!
So we’re going to do as before and use the same dataset, found here, of the 2010 Olympic male figure skating judging. And I’m going to be lazy and just copy/paste what I’d written before for the explanation: the dataset contains vectors of the skaters’ names, the country they’re from, the total Technical score (which is made up of the scores the skaters earned on jumps), the total Component score, and the five subscales of the Component score (Skating Skills, Transitions, Performance/Execution, Choreography, and Interpretation).
And now we’ll proceed in a bit more organized fashion than last time, because I’m not freaking out as much and am instead hoping my plots look readable.
So, let’s start at the beginning.
Regression (multiple regression in this case) is taking a set of variables and using them to predict the behavior of another variable outside the sample in which the variables were gathered. For example, let’s say I collected data on 30 people—their age, education level, and yearly earnings. What if I wanted to examine the effects of two of these variables (let’s say the first two) on the third variable (yearly earnings) That is, what weighted combination of the variables age and education level best predict an individual’s yearly earnings?
Let’s call the two variables we’re using as predictors X1 and X2 (age and education level, respectively). These are, appropriately named, predictor variables. Let’s call the variable we’re predicting (yearly earnings) Y, or the criterion variable.
Now we can see a more geometric interpretation of regression.
Suppose the predictor variables (represented as vectors) span a space called the “predictor space.” The criterion variable (also a vector) does not sit in the hyper plane spanned by the predictor variables, but instead it exists at an angle to the space (I tried to represent that in the drawing).
How do we predict Y when it’s not even in the hyper plane? Easy. We orthogonally project it into the space, producing a vector Y ̂ that lies alongside the two predictor variable vectors. The projection is orthogonal because we want to make the angle between Y and Y ̂ the smallest in order to minimize errors.
So here is the plane containing X1, X2, and Y ̂, the projection of Y into the predictor space. From here, we can further decompose Y ̂ into portions of the X vectors’ lengths. The b1 and b2 values let you know the relative “weight” of impact that X1 and X2 have on the criterion variable. Let’s say that b1 = .285. That means that for every unit change in X1, it is predicted that Y would change by .285 units. The longer the length of the b, the more influence its corresponding predictor variable has on the criterion variable.
So what is multicollinearity, anyway?
Multicollinearity is a big problem in regression, as my vehement Venn diagrams showed last time. Multicollinearity is essentially linear dependence of one form or another, which is something that can easily be explained using vectors.
Exact linear dependence occurs when one predictor vector is a multiple of another, or if a predictor vector is formed out of a linear combination of several other predictor vectors. This isn’t necessarily too bad; the multiple or linearly combined vector doesn’t add much to the analysis, and you can still orthogonally project Y into the predictor space.
Near linear dependence, on the other hand, is like statistical Armageddon. This is when you’ve got two predictor vectors that are very close to one another in the predictor space (highly correlated). This is easiest to see in a two-predictor scenario.
As you can see, the vectors form an “unstable” plane, as they are both highly correlated and there are no other variables to help “balance” things out. Which is bad, come projection time. In order to find b1, I have to be able to “draw” it parallel to the other predictor vector, which, as you can see, is pretty difficult to do. I have to the same thing to find b2. It gets even worse if you, for example, were to have a change in the Y variable. Even the slightest change would strongly influence the b values, since when you change Y you obviously chance its projection Y ̂ too, which forces you to meticulously re-draw the parallel b’s on the plane.
SKATING DATA TIME!
Technically this’ll be an exact linear dependence example (NOT the stats Armageddon of near linear dependence), but what’re you gonna do?
So bad things happen when predictor variables are highly correlated, correct? Here’s the correlation matrix for the five subscales:
I don’t care who you are, correlations above .90 are high. Look at the correlation between Skating Skills and Choreography, holy freaking crap.
So let’s see what happens in regression land when we screw with such highly correlated predictor variables.
If you’re unfamiliar with R and/or regression, I’m predicting the total Composite score from the five subscales. The numbers under the “Estimate” column are the b’s for the intercept and each subscale (SS, T, P, C, and I). But the most interesting part of this regression is under the “Pr(>|t|)” column. These are the p-values which essentially tell you whether or not a predictor significantly* accounts for some proportion of variation in the criterion variable. The generally accepted cutoff point is any value p < .05 (with anything less than .05 meaning that yes, the predictor variable accounts for a significant proportion of variance in the criterion).
As you can see by the values in that last column, not a single one of the predictor variables is considered to be significant. Which is odd, when you think about it initially—after all, we’re predicting the total Composite score, which is composed of these individual predictor variables—why wouldn’t any of them be significant in terms of the amount of variance they account for in the Composite? Well, because the Composite score is the five subscales added together, it’s a direct linear combination of the predictor variables. Because all of the predictor variables appear to account for an equal amount of variance—and because the variance in the Composite score involves a lot of “overlapping” variance from each of the predictors, none of them are deemed statistically significant.
Cool, huh?
*Don’t even get me started on this—this significance is “statistical significance” rather than practical significance, and if you’re interested as to why .05 is traditionally used as the cutoff, I suggest you read this.
Today’s song: Top of the World by The Carpenters
Was Ockham’s razor a Gillette?
This is fun.
Trait snapshot: depressed, introverted, neat, needs things to be extremely clean, observer, perfectionist, not self revealing, does not make friends easily, suspicious, irritable, hates large parties, follows the rules, worrying, does not like to stand out, fragile, phobic, submissive, dislikes leadership, cautious, takes precautions, focuses on hidden motives, good at saving money, solitary, familiar with the dark side of life, hard working, emotionally sensitive, prudent, altruistic.
Today’s song: Sick Muse by Metric
Netflix, eh?
HOLY CRAP Netflix.ca has happened!
Awesome.
Also, here’s a quiz that told me what type of cheese I am. I’ve posted this before, but it was long ago and my cheesy ways have changed (I used to be brie).
Today’s song: Falling Inside the Black by Skillet
I downloaded Steam and it downloaded my soul
So for all the PC gamers out there, I totally recommend downloading Steam. Sean was trying to get me to do so for the longest time and I never did, but for whatever reason, today I decided I *needed* to play around with Garry’s Mod, so I downloaded Steam and verified my old Half-Life CDs.
It’s pretty rad.
ALSO I can now play Deathmatch Classic, which is like my favorite thing ever. I also totally own at it, too.
Today’s song: End Love by OK Go
I AM BLOGGER, HEAR ME POST
Claudia’s Awesome Salad
Hey ladies and gents. Today I shall present you with a recipe for salad. Because it’s a freaking awesome salad.
Ingredients you shall need:
- Broccoli (1 ounce – I don’t know how many little florets this is; I’m picky about the way I cut my broccoli. Guesstimate or use a food scale)
- Carrots (1 ounce – approximately three baby carrots)
- Radishes (two medium-sized ones)
- Cauliflower (2 ounces – approximately two large florets)
- Shredded parmesan cheese (3 tablespoons, or to taste; I like cheese)
- Lettuce (100 grams – I use iceberg lettuce)
- Croutons (20 grams – about a small handful)
- Dressing (2 tablespoons – I use Kraft Calorie Wise Caesar)
Use a cheese grater to grate the carrots, radishes, and cauliflower (I just run the whole cauliflower florets over the grater, it seems to work best that way) and combine with the broccoli, chopped any way you prefer. Add the parmesan and stir. It should look something like this:
Pick apart lettuce to manageable sizes and add to the mixture. Toss to mix everything up. Add the dressing and toss again, then finally add the croutons. Voila!
Note: this makes a lot of salad, but depending on the type of dressing/croutons/cheese you use, it can still be pretty healthy. My version runs about 250 calories (10 grams of fat, 4 grams of fiber, almost 11 grams of protein), which is pretty good for the volume. I eat this and Mini Wheats for dinner ‘cause I’m weird.
Woo!
Today’s song: The Fame by Lady Gaga
My Internal Dialogue is in All Caps
So I had this super awesome statistics-related blog all planned out to post tonight, but a friend in Multivariate Analysis had to borrow my notes, so I figured I should wait until I get them back to make sure I don’t make any mistakes regarding the example I’m going to use.
So tomorrow. I promise. I also keep forgetting to update this more often than whenever the hell I feel like it; I apologize, I’m still conditioned from MySpace.
In the meantime, here’s a color sample of all the clothes in my closet.
Scary, huh?
Today’s song: Sometime Around Midnight by The Airborne Toxic Event
This just in: Cap’n Crunch promoted to Adm’ral
Cereal mascots have always fascinated me. Many of them are poor, deprived souls who just want to try the product they endorse (but are prevented by kids, circumstance, sugary villains, etc.). Others most certainly have mental issues brought on by the cereal they consume (a certain bird who really enjoys cocoa comes to mind).
And then there’s Cap’n Crunch.
First off, his full name is Captain Horatio Magellan Crunch, which is about as awesome as a name can get for a guy with no neck, a freakish cereal fetish, and who has been stuck at the rank of “Cap’n” for god knows how long.
Don’t get me started on the commercials. Says Wikipedia, “In modern TV ads, Cap’n Crunch is often seen riding his ship through a wall as the whistle blares.”
Sooo…Kool-Aid Man of the sea?
And: “He often comes in the middle of a predicament and uses his cereal to solve the problem at hand by ‘Crunch-a-tizing’ it.”
What I wouldn’t give to have that power.
Lab manager: Oh crap, SPSS is being a bitch (again) and we can’t get these analyses done!
Lab member: Our assignment is due in three hours! What are we to do?!
Me: *breaks down Kenny wall in a ship*
Lab manager: What the hell…?
Me: CRRRRRRUNCHATIZE!
Me: Join me for some high-sea, high-fructose fun!
Lab member: How would that possibly solve our problem?
Me: Cap’n’s orders!
*dumps cereal everywhere*
Lab manager: I doubt you’re a licensed mariner.
Lab member: Where did you get all this cereal?
Me: Set sail for dairy goodness!
*unleashes gallons of milk*
Lab manager: You destroyed my laptop!
Me: OOPS! All Berries!
Etc.
Yeah. I think he’s cool.
Today’s song: The Scientist by Coldplay
Stop Waiting for Godot! Take ACTION!
Because someone finally tagged me in one of these on Facebook, but I don’t write notes, so I put it here instead.
Plus I’m bored.
Official Rules: Once you’ve been tagged, you are supposed to write a note with 16 random things, facts, habits, or goals about you.
- Some people think I’m over exaggerating with the whole “I love Leibniz” thing. I’m not. I’d take that man in a second if he were alive today.
- I really, really, REALLY don’t like people touching my bangs.
- Plugging my iPod into the car stereo, cranking it, and driving around for hours = major stress relief.
- I try to give at least one compliment a day—if not verbally, than in my mind.
- Back in 1st/2nd grade, I’d construct life-size paper people out of construction paper. They had bones (popsicle sticks), blinking eyes, “working” digestive systems, and could wear my clothes. I also made elaborate little paper laptops during recess time.
- It really bothers me when people judge others based on their musical tastes.
- I freaking love McDonald’s French fries. I don’t care that they’re pretty much poison, I think they’re absolutely fantastic. Especially when they’ve been sitting under the heat lamp for a little too long.
- I haven’t worn a pair of shorts since 2nd grade.
- I haven’t worn a pair of jeans since 8th grade.
- The majority of my pet peeves are concentrated around the actions of self-centered people. You know, the people who ALWAYS direct the conversation back to themselves, the people who decide not to acknowledge someone waiting for an open locker in the changing room and instead stand in front of their locker once they’re done with their workout and text for ten minutes, the people who walk down the middle of a narrow walkway without realizing that perhaps someone would like to pass them…the list goes on.
- I can’t remember what my life was like back before I’d discovered my passions for statistics and philosophy. I must have been very miserable.
- My first word was “tick-tock.”
- I was a fantastic runner/jumper in elementary school (I wanted to go to the Olympics). Now running hurts just about every square inch of my body for whatever reason.
- There are some days when I have this incredible compassion towards the whole of humanity. There are other days where I just want to stab everyone.
- I would love to be a chef, but unless my olfactory bulb suddenly decides to work for the first time ever, I don’t see that ever successfully happening.
- I almost decided against going to college.
WOO!
Today’s song: The Tip of the Iceberg by Owl City
TWSB: You are my sunshine, my only sunshine…you may determine how I decay…
More news pertaining to our star for this week’s science blog: apparently scientists from Perdue and Stanford have found that the decay of radioactive isotopes fluctuates with the rotation of the sun’s core.
The fluctuations are small (and most likely won’t radically alter any anthropological findings), but they may lend a hand in predicting future solar flares as well as have an impact on medical radiation treatments. The scientists have been collecting data for nearly four years and have determined (at least in the cases of silicon-32 and chlorine-36) that decay rates follow a 33-day pattern.
So how the hell can the sun affect decay rates? The scientists believe it’s due to solar neutrinos, near weightless particles produced by nuclear reactions in the sun’s core. However, these neutrinos have never been known to actually interact with anything before, so one of the scientists summed things up in the rather humorous sentence, “So, what we’re suggesting is that something that can’t interact with anything is changing something that can’t be changed.”
Woo!
Today’s song: Robot Rock by Daft Punk
AAAAHHJESUSJESUSHOLYFREAKINGCRAP
I…I have no words. These topics are…me. Almost every single one of the articles/chapters on this page is something I’d kill to study.
This wonderful, glorious human being is a professor at the University of Wisconsin-Madison. His research interests include general philosophy of science, philosophy of physics, and philosophy of statistics. Therefore, I must apply to the University of Wisconsin-Madison.
MY DESTINY LIES IN THE MIDWEST!
I’m off to go dance around now. This made me more excited than anything probably should.
Today’s song: U + Ur Hand by P!nk
10 Reasons Why Everyone Should Love Quake
Quake rules and here’s why:
10. When you’re totally out of ammo, you still get an axe
Almost as good as the crowbar in Half-Life (though nothing will ever beat that), when you’ve exhausted all ammo (Shamblers, anyone?) you’re left with a little bloody axe that you swing like a dork. It’s freaking great.
9. Scrags
I love Scrags, and I’m not really sure why. I remember we had to make soda bottle water rockets in 5th grade, and I managed to decorate mine to look like a Scrag (obsessive much?). There’s a level that’s almost entirely Scrags; I like to go into God mode and just play with them. Yeah, I’m that cool.
8. You kill the final boss by waiting until a purple spiked ball floats through her
To my little first grade mind when I first played through Quake, this was so freaking amazing. You had to time it so that you went through a teleport gate as the little spiky goes through her body. While you’re surrounded by Shamblers. And lava. Yay.
7. Story line? Pfft.
That was the good thing about mindless FPS games back in the early nineties—they were mindless. I like shooters, especially when there’s no other point than to see how good you can be with strafing while shooting. When (actual) story lines are developed, it loses some of the genre’s charm.
6. Cheating is super fun
No clip activated in water + God mode = JESUS FLIGHT! I always used to fly up and out of the map, or into the weird little ceiling textures above some of the upper levels. It was great. I really need to play more Quake.
5. The Nailgun(s)
DUDE I LOVE THESE. I got my gamer name (Nailpit) partially due to these guns. There’s nothing more satisfying than firing a crapton of nails at stuff. Except maybe being Gordon Freeman.
4. Shamblers
These things scared me when I was little. I really, really didn’t like them. They shoot lightning bolts from their hands and make this awful guttural growl. Plus they’re one of the hardest enemies to kill.
3. Quake is perfect for speedruns
Quake Done Quick and The Rabbit Run are two very nice speedruns through all the levels of Quake, proving that with games like these, run-throughs can be done at a ridiculously fast pace. Pretending to be good at doing so is pretty fun, too.
2. Quake begot Half-Life
And we all know how awesome Half-Life is.
1. The fact that you can play it effectively with just arrow keys, a spacebar, and the control button
This is my favorite component of Quake. It has a y-axis, but you don’t really need to use it. “Aiming” is essentially accomplished by pointing your weapon in the general direction of the enemy and firing; there’s never any real need to look up or down, unless you’re paranoid about platforms or possible enemies on floors above.
I guess I like it ‘cause you don’t need a mouse to play it, just a bit of finger dexterity on the keyboard. That appealed very strongly to my first grade mind, and now that the majority of games I play are on the Xbox 360 or are PC games that require the use of y-axis looking, I really appreciate the simplicity of “up, down, right, left, spacebar jump, control key shoot” gameplay.
Woot.
Today’s song: Caramelldansen by Caramell
How many grad students does it take to configure an office space?
Answer: Three. One to try and decide in what arrangement the desks should be, one to move said desks, and one to realize, after all possible combinations of desk arrangement have been attempted, that they’ve decided on keeping the desks in the order they were originally.
Yeah.
Related news: I’m moving to the Botany Annex, ‘cause the Social/Personality area needs more space. It’s okay, though, ‘cause in the Annex the windows actually open.
Woo.
Today’s song: Put Your Hands Up for Detroit (Radio Edit) by Fedde Le Grande
It’s a post! It’s a blog! It’s a survey!!
10 things you love
1. Statistics.
2. Philosophy.
3. Leibniz.
4. R.
5. Writing.
6. Color.
7. Analyzing stuff.
8. Puns.
9. Antarctica.
10. The internet.
9 talents
1. Obsessing.
2. I’m very good at packing a lot of stuff into very small spaces. I should be a professional mover.
3. Bending R to my will.
4. Rocking it at Quake.
5. Writing (or so I’d like to think).
6. I’m pretty damn good at principal components analysis.
7. Starting Flash projects and never, ever completing them.
8. Dressing like I stood in the blast zone of an exploding Crayola factory.
9. Can blogging count?
8 favorite people
1. Sean.
2. Aaron.
3. Matt.
4. Maggie.
5. Rebeca.
6. Nick.
7. Jacob.
8. Aneel.
7 goals
1. Accrue as much knowledge as possible.
2. Get a PhD.
3. Have a career in which it’s part of my job to screw around with R.
4. Do something with Prime, aside from let it sit untouched on a flash drive.
5. Visit Antarctica.
6. Finish my book list that I’ve been working on since 7th grade. I have a lot more time to read now that I’m not taking 8 classes a semester.
7. Make a metric ton of money and then do something good with it.
6 things you think about a lot
1. Determinism.
2. Where I can find data to analyze.
3. Trends and patterns (in pretty much everything).
4. Philosophy of science-related stuff.
5. What the next thing I can draw/write/create should be.
6. What to blog about.
5 favorite songs
1. Passion Pit’s Sleepyhead (and all its beautiful, beautiful variants).
2. Dan Black’s Symphonies.
3. Morten Lauridsen’s O Magnum Mysterium.
4. Leisure Alaska’s Hey There Mr.
5. Battles’ Atlas.
4 worries
1. My MA thesis. It scares me.
2. The next 4-5 years of my life. They scare me.
3. Mental health “issues.”
4. What the next day will bring.
3 things you believe in
1. A deterministic universe.
2. Logic.
3. Hard work.
2 best experiences of your life
1. Finishing my first degree in 2 ½ years with a 4.0. I worked so damn hard for that.
2. A certain night that won’t be mentioned by name, but you all probably know what it is…
1 thing you want right now
1. Reassurance.
Today’s song: Breakeven (Falling to Pieces) by The Script
Mmm, fresh data!
Hey ladies and gents. NEW BLOG LAYOUT! Do you like it? Please say yes.
Anyway.
So this is some data I collected in my junior year of high school. I asked 100 high schoolers a series of questions out of Keirsey’s Please Understand Me, a book about the 16 temperaments (you know, like the ISFPs or the ENTJs, etc.). When I “analyzed” this for my psych class back then, I didn’t really know any stats at all aside from “I can graph this stuff in Excel!” (which doesn’t even count), so I decided to explore it a little more. I wanted to see if there were any correlations between gender and any of the four preference scales.
The phi coefficient was computed between all pairs (this coefficient is the most appropriate correlation to compute between two dichotomous variables). Here is the correlation matrix:
First, it’s important to note how things were coded.
Males = 1, Females = 0
Extraversion = 1, Introversion = 0
Sensing = 1, Intuiting = 0
Feeling = 1, Thinking = 0
Perceiving = 1, Judging = 0
So what does all this mean? Well, pretty much nothing, statistically-speaking. The only two significant correlations were between gender and Perceiving/Judging and Sensing/Intuiting and Perceiving/Judging. From the coding, the first significant correlation means that in the sample, there’s a tendency for males to score higher on Perceiving than Judging, and for females to score higher on Judging. The second significant correlation means that in the sample, there’s a tendency for those who score high on Feeling tend to score high on Perceiving, and a tendency for those to score high on Thinking to score high on Judging.
The rest of the correlations were non-significant, but they’re still interesting to look at. There’s a positive correlation between being female and scoring high on Extraversion, There’s a correlation between being male and scoring high on Feeling, and there’s a very, very weak correlation between Feeling/Thinking and Extraversion/Introversion.
Woo stats! Take the test, too, it’s pretty cool.
Today’s song: Beautiful Life by Ace of Base
Exactly how does one go about kissing the rain?
So as probably none of you know, I recently came into contact with an old friend from 7th grade. It was weird—early last week he randomly popped into my head and I thought, “I wonder what ever happened to Ross?” and then last Thursday I get a call from my dad—Ross had called him (sometimes it’s a good thing when at least someone in my family is able to keep the same phone number for more than 5 years) and dad gave me all his contact info.
Now we’re friends on Facebook and we talk on Messenger on occasion. He’s pretty much exactly the same, which is good to know ‘cause he was blind and insane and really awesome to hang with in 7th grade and is apparently still blind and insane and really awesome to IM.
Apparently I’m still the same, too. We spent the other night talking about the old Knowledge Bowl competitions we “participated in” solely because it meant we got to go to McDonald’s and then goof around on the bus to the competition/at the competition/on the bus back from the competition.
A few more memories passed between us, and then he said this: “You never seemed very happy, still breaks my heart to think about.”
This kinda surprised me. Really? I didn’t ever seem very happy? Back in junior high, when all I had to worry about was stalking Patrick learning how to type and not getting my thumb sawed off in shop class?
I find that very…disconcerting.
Do those of you who know me now find me unhappy? Am I like this harbinger of depression or something? ‘Cause I certainly don’t remember being Emo Central in 7th grade (that was 8th grade, but I was on meds and they killed my soul, among other things) but apparently that’s how I came across. There’s a lot more shit going down in my life right now than there was back then, but aside from the occasional “I HATE MY LIFE” blog—and let’s be honest, who doesn’t have those every once and awhile?—I don’t think I’m all that unhappy-sounding.
Meh. Probably overanalyzing it. I’M GONNA GO WRITE DEPRESSING POETRY NOW WOO!
Today’s song: Vancouver City (featuring Linda Ganzini) by Innerlife Project
This Week’s Science Blog: Microwaves – The Answer to Everything
I knew about the microwave background “noise,” but this video describes it and the paradox very well.
I also realize that I missed last week’s science blog, so here’s a link to the Internet Encyclopedia of Science. I know it’s no substitute for one of my obnoxious science reviews, but it’s certainly a lot more useful.
I LOVE YOU ALL!
RED BULL!!
Today’s song: Touch the Sky (Original) by Iambic
The Periodic Table of Academic Disciplines
Alternate title: Claudia’s Bored
(Click to enlarge!)
(Source for list of disciplines/categories)
YES I KNOW it’s not exactly like the actual periodic table groupings, but I gave it my best shot. I tried to keep the general “this is how things are organized” patterns, but some of the disciplines just didn’t fit in anywhere else (anatomy, I’m looking at you). I didn’t keep the P-, S-, D-, F-blocks ‘cause they didn’t work out in terms of layout, but I kind of made my own blocks instead (take THAT, Mendeleev!). The groups, however, are still sorta there.
Group 1: Formal sciences, applied
Group 2: Formal sciences, more theory-based
Group 3: Physics
Group 4: Physics-related stuff
Group 5: More specific physics
Group 6: Physics and space-related stuff
Group 7: Chemistry and biology
Group 8: More specific biology
Group 9: Specific types of organism-based biology
Group 10: Earth-related stuff
Group 11: Climate-related stuff
Group 12: More earth (ground)-related stuff
Group 13: Applied sciences that don’t fit anywhere else
Group 14: Arts (and marketing and accounting)
Group 15: More arts
Group 16: More arts
Group 17: Written arts
Group 18: Humanities
As for the colors, they’re more related to the blocks I guess. And the periods generally go from most fundamental/basic/theoretical (at the top) to the more applied (near the bottom of the columns).
Yeah.
I love Red Bull.
Today’s song: Alejandro by Lady Gaga (another “why the hell didn’t I have this song yet?” day)
Eigenblogger 