So whenever we have the data in the tabular format ready with us, we should first look at the descriptors
of each variable.
The descriptive statistics, as we discussed in another section, are used to describe the data and
summarize it.
And since we will describe each and every single variable and not relationships between two or more
variables, it is called univariate analysis unit stange.
What one where it stands, what variable?
So it is one variable analysis.
So we can look at the buildings as part of the univariate analysis.
We can see mean median mode.
We can see measures of dispersion like range quartiles and standard deviations.
And for categorical data, we can look at count of each category.
Most software packages for statistics have a very easy way to do univariate analysis for all the variables
of the dataset.
And when we run it, we see something like this.
So for a variable like age, we can have info like mean median, minimum, maximum the 25th.
58 and 75 percentile values.
Imagine if I arrange all the ages in ascending order, the first value will be the minimum value.
The last value will be the maximum value.
The 25th percentile value this twenty five point seven five.
Value will be at one fourth position.
That is, 25 percent of the values will be lower than this value.
Twenty nine.
This 50 percentile value will be at the middle.
That is, 50 percent of values will be lower than this.
And this 50 percentile value is same as the median also.
If you remember the definition of median.
Seventy five percent value prettified went to file is added Trefort position.
So seventy five percent values are below thirty five point two for a.
And from 75 percentile to the maximum value is thirty five point two five two fifty one.
This range is 70 and they'll do maximum.
So you can observe what this data.
The first twenty five percent values are in a very small range of twenty four to twenty five point seven
five.
But if you only see the last quartile, the last twenty five percent of values add between the huge
range of thirty five point two, five to fifty one.
So this distribution is not evenly distributed.
Such observations help us identify issues in our data.
This whole information for all the variables of the dataset is called the Extended Data Dictionary.
Using EDT, we can deduce a lot of things like patterns of outliers, presence of missing values and
so on.
What are these issues and how we handle them will be covered in the coming videos?