Now the next thing which we need to understand, is the chiefs of distributions.
Now a data distribution shows all the possible values of the data and how when each value occurs.
So what does of distribution?
So let's say we have this particular data which is three, four, five, seven, eight, eight, nine,
11, 12, 14, 15, 17, 18, 19, 19.
So what we do here is we like this on a graph.
And we try to create a call out of.
So when three has one value and then four has one value, these values will have just a constant value
and eight, because it has a higher number of values.
The code will go up a little and then come down.
The nine has one value, any living has one value and so on.
So.
These are the different type of distributions which we have.
The normal distributed mean and median and mode are all equal and therefore are all appropriate measures
of central tendency for skewed.
The median may be a more appropriate measure of synchronous demands.
So we have the bell shaped echo.
So initially the smaller values have low values, low number of occurrences, and then the number of
occurrences actually increase and then they go down again.
It could be something like, let's say height of students in a class.
So there would be less number of students with low height and less number of students with very high
height.
But then average height would be constant for the students it would bring in, you know, smaller number
would mean a middle number.
This is called the Bell Sheeple, which is also the normal distribution.
Then there could be a triangular distribution, there could be a uniform distribution that all the values
have seen the multiple sentences.
Then there is the reverse GC.
GC, right skewed.
Right skewed because it is kind of stretched out and skewed out towards the right hand side.
This is left skewed because it is stretched out towards the left hand side.
Bimodal, which has two modes.
Which is it?
Which is it has two tips to it and multimodal, which has multiple things to it.
Now for normally distributed data, which is basically this kind of data, the mean median and mode
unseen.
So what all of these mean median and what are appropriate measures of centers?
And it's why even we talk about the left skewed or right skewed the mean value.
And the mood value will be different from each other.
So median will be a more appropriate measure.
We'll see how that happens.
But for now, you can understand that median will be a more appropriate measure of central tendency.
Now let us look at normal distribution.
So a normal distribution, sometimes called as the bagel, is a distribution that occurs naturally in
many situations.
For example, the BALTO is seen in scores of settlements.
So when we are talking about schools of students, the bulk of the students will, for the average,
see value.
Basically, there would be a lot of students who will scored average value.
While there will be a smaller number of Syrians who will score, be or B and there will be a very small
number of students who will be scoring an F one in eight.
So this will create a distribution which will resemble the double.
America is symmetrical in nature.
And half of the D doesn't fall on the left side of the mean and half of the data will fall on the right
side of the.
And many groups will follow this type of fight.
So this is a highly used distribution, the normal distribution.
So let us see some characteristics of the vehicle.
It is created on the bottom.
He does mean and standard deviation.
And it can be followed.
For various purposes.
LAKE Let us see scholarship distribution.
So let us say we have this medical.
This is the makeup.
And in this makeup, we have this kind of a distribution.
So the most number of screens will have the C rating and then there will be fewer students who will
have to be A, B or B, and there will be a very small number of strings we got.
Who would have got anything or everything right now?
Let us say we want to distribute scholarship.
Then what we can do is we can so you get all the students on the basis of the topmost ratings.
So this has a distribution where the middle most, 80 of which is the mean value plus one standard deviation
and minus one standard deviation.
The area between this month's spray has 68.27% of the beta.
While the meme plus y is standard deviation, I'm the mean minus twice standard deviation.
This entire area has 95% of the entire data and mean minus three standard deviation and mean plus three.
Standard deviation has 99.73% of the data.
So what we can do is we can give the scholarship to any student who has scored more than plus standard
deviation.
Or let's say we want to fire people from the organization.
Then we can put the people from a rating less than mean minus the standard deviation and do the.
One of the things on where we can monitor the performance of the people at work.
So the bellicose can be followed for various focuses like scholarship distribution, finding the best
performers or the worst performers, or finding out the segment of a data.
Now what is for doses?
Gold doses is a statistical measure that defines how heavily the fields of our distribution differ from
the themes of normal distribution.
So we have this particular distribution and it has this same.
Right.
This deal is like a normal this deal frozen.
Now, if the deal is highly extended or if the deal is not extended, it is a very small deal.
So if you want to understand how the deal is, then we can use good doses for that.
So Caduceus identify as whether the feel of the garden given distribution contains extreme values or
not.
So these are the types of producers.
One is liptak a talk.
We're just having a.
Similar number of values in the go, and it is having a more number of values in the middle values.
So in this, the middle most values will have a very high.
While in case of normal distribution, it is just a normal distribution.
And this is Lidegaard, which has a very large number of values in their veins.
And the little convict has very few values in the state.
Now let us discuss about skewness.
We saw this is the image of the skewness we had left skewed and we had the right skewed.
So let us discuss about the skewness.
So skewness refers to the distortion or asymmetry in a symmetrical vesicle or a normal distribution
in a set of data.
If the curve is shifted towards the left or to the right.
In this sad twist to the stillness can be quantified as a representation of the extent to which a given
distribution varies from the normal distribution.
So we have a normal distribution and we want to see how much this normal distribution has been pulled
towards the right or the left inside.
We calculate this using skewness a normal distribution.
We have skewness zero.
So here we have negatively skewed, normal, skewed or positively skewed a normal distribution has no
skew.
So for this, the mean median and mode will be at the same value.
And this is a perfectly symmetrical distribution.
When we have the negatively skewed, it is skewed towards the negative towards the left.
And here we have the positively skewed where it is skewed towards the right hand side.
So when we have the negatively skewed distribution, so here the mode will be higher than the median
and the median will be higher than the mean value.
Why in case of positively skewed, the mean will be greater than the median and the median will be greater
than the more divided.
Now the next thing which we need to talk about is correlation and obedience.
This is a very important topic.
So both the measures they measure the relationship and the dependency between two variables.
So let's see, we have two variables.
One is age of a person and another is the income of the person.
So in an ideal situation, as the age of the person will increase the.
Income of the portion will also increase.
So here there is a positive relationship between the age and the income.
Why?
When we talk about.
The age and the health of the person as the age of the person will increase, the health will deteriorate.
So what are these measures?
Correlation and obedience.
They measure the relationship and dependency between two variables.
In the two variables are dependent on each other or if they are changing together or not.
Covariance indicates the direction of a linear relationship between the variables, so equal medians
will be giving if the values are changing towards positive direction or they are changing towards the
negative.
That action and correlation will give the direction also.
And along with that it will also give the strength of the relationship.
If the values are very highly dependent on each other and they are strongly changing together, or that
is a very minor relationship between the change in the values dependent on each of other.
So here you can see that we have these dual values and here the correlation coefficient has a values
0.9996, which is very, very high value.
So here there is a strong was too lenient correlation.
So these values are changing together and they're changing very strongly.
Okay.
Positive that way.
Here the value is negative.
Z 2.19.
So it is a strong negative negative because it has a negative sign.
So when one value increases, the other value decreases.
And here in strong positive relationship, when one value increases, the other value also increases.
And if the value would have been close to zero, which means that the values I have a weak relationship
with meaning.
So if the value is lower.
It is 0.64 and it is 2.99.
So a lower value means that they have a weak relationship.
Strong relationship is depicted by a higher value, close to one or close to minus one.
While a weak value would be closer to 0.5 or something like if the value is very close to zero.
This will mean that there is no linear relationship between these two values.
So here at -0.58, it means that there is a negative relationship, but it is weak.
Here the value the relationship value is 0.64.
So the there is a negative, positive relationship, but it is a weak relationship.
So this is about the descriptive statistics.
Thank you.