Entries in the “Projects” category
Ever since my first attempt at making beer I’ve wanted to try it again, but living in a Manhattan apartment has some limitations, and making 5 gallons of beer can be one of them. When I found out that Brooklyn Brew Shop made a 1 gallon beer making kit that was perfect for a city apartment, I decided it was time to give it another try. I got a Bruxelles Blonde kit, which came with almost everything to make an ale style beer:
- 1 gallon glass carboy
Additional equipment needed, but not included in the kit:
- 6 quart stock pot
- Second pot
- A fine mesh strainer
- Bottles and caps
Since I had everything needed, I started making my second batch of homemade beer in late August.
Basketball is not one of my favorite sports. Generally speaking, the season is too long, teams don’t really play defense, scoring is too easy, and, when games are close at the end, the losing team constantly fouls the winning team to try and preserve clock time — what could be the most exciting part of the game is reduced to abject drudgery.
The NCAA Men’s Division I Basketball Championship, also known as “March Madness”, can be very exciting due to it’s single-elimination format, however, the way they play the last few minutes of close games mars an otherwise enjoyable experience.
I know that strategy works once in a while, but that doesn’t make it fun to watch.
As the NCAA tournament got closer this year, I began to wonder just how long, on average, the last two minutes of a game actually takes to play, and if the closeness of the score matters. So, during the tournament, I got out my trusty stopwatch (last seen during Super Bowl XLIV) and timed the last two minutes of as many games as I could.
In mid-January, the Wall Street Journal analyzed the actual amount of play time of the average football game. They added up the amount of time the ball was actually alive and in play in four different games, and it averaged out to about 11 minutes. They concluded that the average game broadcast on TV shows 17 minutes of replays and 67 minutes of players standing around. With the biggest game of the year coming up, I decided to do my own analysis of the actual play time. Here are the results:
For the holidays, my mother got me a Chia Garfield as a gift. In early January 2009, I “planted” it. Just like the last time I planted a Chia Pet, I’m going to document this one.
Back in May came word that Kellogg’s was bringing back Hydrox cookies for a limited time. As soon as I heard, I knew an Oreo vs. Hydrox showdown would have to be waged. Today, it was.
Similar to the Oreo vs. Chips Ahoy! showdown, at 2:30pm 16 cookies of each variety were placed on plates in a central location in our department, and an email was sent out announcing that cookies were available. Because Oreos and Hydrox look so similar, the e-mail stated that both were available.
My thought going into this showdown was that Hydrox would win based on the novelty factor, or at least there would be a tie as people took one of each to compare.
The other day I ran into one of my favorite shows on TV, the Discovery Channel’s “How It’s Made”. This particular episode was of special interest to me because they showed how fortune cookies are made, and I’m somewhat fond of them. The production of fortune cookies was about what I expected it would be: ingredients mixed; cookies baked; fortunes inserted and cookies folded; cookies wrapped, boxed and shipped.
During the segment, they gave out two interesting statistics that I was hoping they would. This particular fortune cookie factory produces 4 million cookies per day, and uses 5,000 different fortunes, which means that each fortune printed is going to be duplicated 800 times per day. Afterward, the number 4 million got stuck in my head, and danced around in there as I tried to sleep. I’m not sure why, but it just seems like a very large number to me.
A few co-workers and I were discussing cookies at lunch one day. As the conversation went on, the question of which cookie is the best selling cookie in America was asked, and the answer didn’t surprise us: Oreo. We all agreed that chocolate chip cookies are probably the best selling as a type, but there are so many brands and varieties that one just can’t compete with Oreo for the crown.
During that lunch, we decided to conduct a little test with our fellow co-workers. We would set out an equal number of Oreos and Chips Ahoy! on plates in the central area of our department and see which one disappeared first. I predicted that Oreos would win, but the others figured the Chips Ahoy! would win.
Below is the individual sample pack findings of M&M Color Distribution Analysis.
I love M&M’s. I’m partial to the plain Milk Chocolate variety, but I’ve been known to have a Peanut from time to time in order to remind myself why I don’t like them that much. Often, while eating a pack, I’ll wonder how they’re made and how the colors are distributed.
I once took a factory tour at Ben & Jerry’s and saw that they make ice cream by making one flavor per production run and then storing them to be shipped out later. While that kind of production makes sense for ice cream since there are many different flavors and each flavor has many different ingredients, it doesn’t make sense for M&M’s since, except for the color of the candy shell, they are all the same. I assume that all the different colors are made at the same time and they’re combined together along the way into the different size packages.
After wondering about it a little more, I checked out M&M’s web site. According to it, each package of Milk Chocolate M&M’s should contain 24% blue, 14% brown, 16% green, 20% orange, 13% red, and 14% yellow M&M’s. I checked the next few packages of M&M’s that I ate and found that their percentages were not even close to the stated distribution. In my mind, this sort of confirmed my thoughts about how they produce M&M’s: When they make M&M’s, in any production run, they produce the stated percentage of each color and then just fill the packs off a conveyor line or some other weight based method. This would mean that any single package could be way off from the stated percentage; but analyze the counts over a large number of packages, and they should converge towards the stated percentages.
That’s what I aim to do here.