- [Instructor] So we have this question that says
millions of Americans rely on caffeine to get them up
in the morning.
Now that is probably true, although for me if I drink
even a little bit of caffeine in the morning,
I won't be able to sleep that night.
Here's nutritional data on some popular drinks
at Ben's Beans Coffee Shop.
And so we have the names of different drinks
and for each of those drinks, we know not only their name,
we know whether they are hot or cold, we know
how many calories they contain, sugar content, caffeine.
Alright, now with that, let's try to answer these questions.
And like always, try to pause the video and see if you
can answer these questions before I do.
And on this one on the right, there's a little bit below
what you see, there's a little bit below, and actually
I'll just leave it there.
So the individuals in this data set are.
So when you see the term individuals, you might immediately
say, oh, they must be talking about people, because
in every day language, we talk about individuals,
we tend to be talking about people.
But in statistical language, it's really referring to
what are the different entries of the data set
referring to?
So each of these rows of the data set, they're not referring
to types of people or customers and things about those
customers, that's not about Ben's Beans' customers,
I have trouble saying that, each of these rows represent
one of the drinks that are offered at Ben's Beans
Coffee Shop.
So those are the individuals.
In statistical language, individuals could refer to things.
What are the things represented in that data set?
Then we're asked, the data set contains, and we're asked
how many variables does it contain, and whether or not
they're categorical or not.
So first, what's a variable?
Well the variables are what are the things that are
different about these different entries, about these
different individuals that this table tells us about,
that this data set tells us about.
Well, the name is variable, each of these drinks
have different names, that's one variable,
the type is variable, they can be hot or cold,
the calories are variable, the sugar content is variable
and the caffeine is variable.
So we have five variables over here.
The name, type, calories, sugar, caffeine.
So we have five variables, so we can rule out
the sixth variable choices right over there,
so we're gonna rule that one, and that one out.
And so how many of these are categorical?
So a categorical variable, lemme write this down,
categorical variable means that you're gonna,
that dimension of the thing you're measuring
is gonna be placed into some categories, or that variable
takes on values that are a name of a category.
So for example, drink name, this is a categorical variable
because the values that drink name can take on,
well, those are the actual names of the drinks.
These are the categories, it could be brewed coffee,
cafe latte, cafe mocha, cappuccino, so on and so forth.
So this one right over here is, this one over here is
categorical, categorical.
Now you might say what's another type of variable?
Well another type of variable is quantitative.
Quantitative.
A quantitative variable is going to take on numerical values
as opposed to categorical values.
Numerical values that are measuring some type of attribute.
So let's go through these.
So what about type?
Well type isn't a numerical value, it's taking on
a category, it's either gonna be hot or cold.
So that is also going to be categorical,
but these other three, these take on numerical values
that represent in the case of calories, the number
of calories a serving has, the number, the amount of sugar
in the sugar variable, or the amount of caffeine
in the caffeine variable.
So these three right over here, these are quantitative
variables.
So you have five total variables, two of which
are categorical, and three of which are quantitative.
And so that is this choice right here, five variables,
two of which are categorical.
The two categorical ones are, well what name,
what's the name of the drink, and also whether
they are hot or cold.