No, let us see how to do univariate analysis in our.
We'll first find the extended data dictionary of each variable that is EDT.
And after looking at EDT, if we have doubt on distribution of a particular valuable, we will plaudit
its program to look at its distribution.
Then we will also plot budgets of the categorical variables.
And lastly, we will summarize our observations from the univariate analysis.
Two good EDT.
We just run somebody and within bracket relate D.F..
And we done this.
You can see in the window below.
Let us make this window big.
But looking here.
So what each variable we are getting, the minimum, maximum mean and cortile values by quarter, I
mean the first quarter, that is 25th percentile, the median, which is 58 percentile.
And third quartile, which is 70 percent.
So since this is a little bit difficult to read, because the same in value is going into two different
roles for different types of variables.
Let us put it into a readable format.
We will reduce the font size for this.
So go to view.
Beans, and we'll go to being labeled with an appearance.
We will reduce this font size from third to eleven.
Click on to play.
Let's click on Okina and we will extend this to the late.
So that.
It will die in order.
So.
Let's keep it here.
Now it is visible clearly.
As you know, the difference between mean and median values really indicate skewness and outliers.
So if we look at price and it's mean and median values does seem fairly close.
So we need not look at price.
Crime rate, however, has.
Bigger difference between mean and median values.
Another indicator of Skewness and outliers is the distribution within quartiles.
So if we look at the first quartile of crime rate within a short range of zero point zero six two zero
point zero eight, in fact, did a one zero zero six two zero point zero eight.
There is the first quartile that is, Gerti, five percent of values are within this short range.
But the third quarter lendee, maximum value.
That is the last quartile.
It has a huge range from three point six to eighty eight point nine.
And it also has 25 percent values.
So there is a huge difference in the ranges and they both are containing only 25 percent of the values.
So this very well either has outliers or skewness in its distribution.
As we move ahead and look at each variable and its mean and median values, the next variable of interest
is in hard rooms.
And also the mean and within values have some difference.
But if you look at the quartiles, the max value is exceptionally high.
So from minimum of ten point zero 06 to third quartile, it is only going from 10 to 14.
But from third quarter to maximum.
It is moving from 14 to one zero one.
So there is some distribution or outlier issue here.
Similarly with rainfall.
Minimum is three.
And the first court date comes at twenty eight.
But for all other quartiles, the distribution is nearly, what, a range of 10 unit to this minimum.
Two, first cortile is a little bit skewed or has some outliers.
So for crime rate and rooms and rainfall, we believe there is either Skewness in the distribution.
Or presence of outliers.
Not another thing to notice in this variable and horse, Biggs.
It has one additional value of.
And is there are eight any values?
So while we import data from us, yes, we file.
If there is any blank will, you are automatically assigned it a value of any.
So we need to handle these missing values also.
Now, let's get back to these three variables that we identified earlier, which either have outliers
or Skewness.
We need to see the actual distribution during the five which issued they are actually facing.
The distribution can be seen in histograms or in scatterplot.
First, we will plot histogram for each of these.
To plot Instagram.
Just write his.
ATSDR.
And and record, we relate.
The F dollar crime under read.
On the day, you can see the plot for this.
If we look at the Instagram, it has bulk of values on the left between zero drippin probably.
But how many values are going beyond the value of twenty?
We are not really sure, even if there are 10 15 values.
These may not be outliers.
These may be genuine values.
So this histogram is not giving us the correct picture for this particular variable.
We should go and look at Scatterplot.
So let's applaud Scatterplot for each of these three variables to get scatterplot for all of these simultaneously.
We will rate payers and within bracket.
We will start with DeLay and we'll start writing all the variables that we want in this bad.
So first is price.
Plus.
Crime rate, close and heartbeat.
Plus, rainfall, comma.
All might be days, including if.
Dundies.
Let us correct this variable.
It should be in hot rooms.
Let's run it again.
You can see we have a set of scatterplot here.
Let us click on Zoom Button to look at them.
That's Maximizer.
So first, we need to look at price vs. crime rate.
It has a lot of values within the range of zero to 10.
But still a considerable number of values are going beyond this value took price and crime rate probably
do not have a linear relationship.
They have some other type of relationship.
So we need to transform this variable in some way so that they end together leaner listenership.
We will see this transformation a little reduce.
Next, we will look at price and in hot rooms.
If you look at this graph, all the values are within short range, but two values are exceptionally
out of order.
This exceptionally out of order values are certainly at outliers that do not behave in any particular
pattern.
They are clearly outliers.
Similarly for rainfall.
All of these values seem to be beyond 20 and within 60.
Only one value is even below 10.
This value is clearly an outlier.
So we have identified that in hot rooms and rainfall has outliers crime rate as some different type
of relationship with price.
And we will manipulate this variable so that it has a linear type of relationship with price.
Later on.
One last thing we should do is looking at bar plots of categorical variables.
We have three categorical variables.
Airport, water, body and bus terminals, which is represented by bus and escort her.
Do applaud, but applaud.
We just right.
But applaud.
And within bracket.
We'll right able and again within Blacket will specify the variable.
It is D.F. Dollar Airport.
Not Sundays.
So you on the right.
We have Bob blood of.
Airport, which is a categorical variable as to values.
Yes and no.
And by looking at this graph, we do not see anything suspicious about this particular variable.
No, let us do the same thing for everybody.
Just change this very well.
Do what, awardee?
And on it, this also has forward values, Lake Lake, and we were bored none.
And they were.
And this also seems fine to us.
Now, let us run Lebar platform bus terminal.
Here you can see it has only one value on the cities offer of our dataset has bus terminal in them.
So since all duties of our data have bus terminal, we cannot identify whether this variable impacts
the final solution or not.
So we need to ignore this variable in our analysis.
So we did EDT, Blood-Red, Scatterplot and Bartletts to identify variables with outliers, skewness,
missing values, and I use less categorical variable.
So let's list Dondi observations.
Posters and hard rooms and rainfall has outliers.
And rainfall has always
taken in hospitals as missing lose.
It is bus terminal is a useless variable.
And the last observation is crime rate.
Crime rate has some other functional relationship with price.
We'll handle these issues in the coming videos.