Code in the rough

Business Analytics Class Update

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I learned a lot of vocabulary

  1. Measures of Center
  2. Mean
  3. Median
  4. Mode
  5. Continuous Data
  6. Discrete Data
  7. Categorical
  8. Nominal
  9. Quantitative

I will explain this vocabulary in my article.

In many movies and tv shows I can remember the narrator sort of jokingly describing a family as average saying they live in a small house in some suburb with a dog and 2.4 kids.  Using 2.4 to emphasize just how average they were, and also making a joke about people who do stats and how they view the world as all continuous, but people who do stats know that there are actually various ways to measure “average” that takes into account the differences between continuous and discrete objects.

In math we learn to calculate the average by adding up all the figures in front of us and then dividing the sum by the number of figures.  For example: 1, 2, 2, 3, 4 .  These numbers add up to 12.  Then we divide that number by five(the number of figures) and we get 2.4.

So, anyway, they have this term “measures of center” and it’s basically different ways to find average and those ways are mode, median, and mean. Mode is the value that appears most in some data.  For example, from my list from above, 2 is the most common number, so would be the mode.  Median is the number in the dead center of the list of sorted figures, which is 2 again in my example. And mean is just a weird way of saying average, not trying to be mean. 

But there’s another definition of average that we use in everyday conversation.  When we say average we really want to say what’s most common.  “I’m average.” = “I’m like most people”. We can also use one object universally thought of as average to describe another thing or person.  Jim is the Toyota Corolla of office workers.  He’s categorically average.  He’s just ok.  He probably makes median wage.

They have this way of classifying data, basically giving it a type.  There’s categorical which is like the label given to something.  For instance there are a bunch of animals on a farm.  Some are cows, some are shee, and some are dogs.  Sheep and cows are a type of categorical data. It’s a label that describes what something is. 

Then there’s quantitative data.  How many cows, dogs, and sheep there are are quantitative data.

Then there’s nominal, which describes the quality of something.  Let’s say one of the dogs on the farm likes to steal a piece of bacon when no one’s looking.  That’s a bad boy.  Another dog is good at herding the sheep.  That’s a good boy.  And finally one is good at scarring off the coyotes.  That’s best boy.

That’s all for now.  Thanks for reading.  Subscribe and follow me on my journey into tech.

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