In this session, we will discuss about statistics.
Machine learning is deeply based on statistics.
Statistics is the science and the art of learning from statistics is the collection, analysis and interpretation
of data.
It is the effective communication and presentation of results, relying on the data.
So as part of statistics, we will try to understand how statistics is used to understand the data and
how it could be used to support machine learning.
Here.
You can see that we have a complete population.
Population is the entire dataset which we have.
Let us say we are talking about the entire country.
So the population of the country would be the complete consensus of the country.
All the people present in the country would be a part of the population.
But when we are conducting a particular study, we will not be conducting the survey on the entire population,
but rather be taking a small sample from the population.
But while we are taking samples from the population, the sample would have to be a good representation
of the population.
So the sample has to be selected randomly from the population so that it can represent the viewpoint
of the population correctly.
We use the samples and analyze the samples to derive inferences.
The.
Regarding the population.
This is what?
Population and sample has a relationship between.
Now.
What is population and what is something?
A population includes all the elements from a set of data.
While a sample consists of one or more observations drawn from the population.
A measurable characteristic of a population such as the mean standard deviation is called a barometer.
So all the measurable characteristics of a population are called the barometer, while the measurable
characteristic of the sample are called statistics.
The reports are a true representation of the opinion.
So if they are trying to find out the mean of the population, then the mean of the population is a
true representation of the opinion.
While if we find out the meaning of the sample, then it will have a margin of error and a confidence
interval.
The value will not be exactly same as the population.
There is a possibility of error and there is a confidence interval or interval in which the value of
the mean of a sample can range.
So the mean of the sample will be present in the range between the mean of the population.
So we cannot say that the mean of the sample will be exactly same as that of the population.
It will be a little, but this is why we are applying different methods on the samples so that we can
find out the values of the population accurately.
And this entire thing is called statistics.
The entire study of this characteristic is called statistics.
So why we are taking the samples, we have to apply several sampling techniques.
So there are two types of sampling techniques which are available.
One is the probability sampling, and another one is the non probability sampling.
Probability sampling involves the random selection, allowing you to make statistical inferences about
the whole group.
So in case of probability sampling, the sampling is done by random selection of elements from the population.
Why?
In case of non probability sampling the non random selection based on convenience or any of the criteria
allowing you to easily collect the initial data is used.
So non probability sampling allows a convenient selection of values by probability.
Sampling is used for random selection.
So whenever we are making a random selection it is called probability sampling and whenever the selection
is based on convenience, it is called non probability sampling.
Now there are different types of probability sampling.
One is simple random sampling, where we take random values from the entire set.
We pick out random elements from the entire set.
These are called simple random samples.
Systematic sampling is where we systematically pick a particular sequence of values from the entire
data.
So here, if you see we have values from 1 to 9, so we are picking out all the multiples of three.
To get the samples from this dataset.
The stratified sampling has the values without a clear cluster has formed.
Different clusters have been formed from this data, so cluster of old and young men have been baking
separately.
Another cluster of women have been taken separately and one cluster of children has been taken.
And then from these clusters people are now trying to meet, this is what started this fight, nine
of them sampling this and cluster sampling is we create clusters out of the entire data.
We create different clusters from the data and we just pick out one cluster from this entire data.
We pick out random clusters from this entire clusters of data, and we use them for samples.