So whenever we have the data in the tabular format ready with us, we should first look at the descriptions
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 stands for one very standard variable.
So it is one variable analysis.
So we can look at the bulletins 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 fiftieth
and 25th 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 days, 50 percent daily value will be at the middle.
That is 50 percent of value will be lower than this.
And this 50 percentile value is the same as the median.
Also, if you remember the definition of median.
Seventy five percent in value, 35, five to five is a Trefort position, so 75 percent values are below
35 point to five.
And from 75 percentile to the maximum value is thirty five point five to 51, this range is 75 percentile
do maximum.
So you can observe for this data.
The first 25 percent values are in a very small range of 24 to twenty five point seven five.
But if you see the last quartile, the last 25 percent of values are between a 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 data.
This whole information for all the variables of the dataset is called the Extended Data Dictionary.
Using day, we can use 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?