I everyone.
Let us start with the basics of statistics.
What are the things we must know when we are planning to do that analysis?
The first thing is the different types of data, we must know this because different types of analysis
run on different types of data, and when we see the data, we should be able to quickly identify its
type and then figure out what type of analysis is to be done on it.
Basically, there are two types of data, qualitative and quantitative.
Qualitative is also called categorical data because it has categories.
Within qualitative, we have nominal and ordinal data, and within quantitative, we have discrete and
continuous data.
Let us look at these tapes one by one.
Here's a definition of qualitative data data which can be classified into two or more categories is
qualitative or categorical data.
Such data is for the classified into nominal and ordinal.
This is the capability of putting these categories into some order.
Nominal is where we cannot assign any order, for example, if we collect our student and one of the
variable is students gender.
This data is categorical as it has categories such as male or female, but we cannot order these.
We cannot assign that one or rank to any particular gender.
Similarly, the country to which a person belongs is also nominal, it only has a name, it has no other
information.
It may have meaning for you, but the data analysis software will reach all countries equally.
On the other hand, ordinal data has names and order, that is, you can rank the categories as higher
or lower.
For example, when they were in service of a restaurant, the options such as not satisfied, satisfied
and delighted, can be ordered while doing analysis.
It will be better that you tell the order of preference of these values.
Similarly, spiciness of food is another ordinal categorical data.
It contains values such as less spicy Mildenhall.
We know that hot is more spicy than mine, but we do not know how much amount.
We know that mild is more spicy than less spicy, but we cannot quantify the amount of spice.
More spiciness that much less than less spicy to these three are categorical variables and we can order
them in the order of spiciness, but they do not have particular numerical values assigned to it, so
we cannot quantify it.
Now, let us look at quantitative data.
Quantitative data can be measured numerically.
That is, it has a numerical value.
It has inherent order, which is the numerical value.
There are two types of quantitative variables, one which can take discrete values.
This will have only certain values and cannot have intermediate values.
For example, if you toss a coin 10 times, the number of times you'll get ahead can be an integer value
from zero to 10, but it cannot be one point five or two point one, etc..
Second, by this continuous variable, continuous variables can take any value in between.
Also.
For example, I taught student it can be any value, like 170 centimetres, 170 point five centimeters,
120 point five, six centimeters, etc., It may have a range between the values will fall, but within
that range, it can take up any value.
So these are the different types of data.
Remember these keywords which we used in this lecture as these will be used in the lectures to come
also.