Machine Learning - The Nature of Probability and Statistics - Descriptive and Inferential Statistics

In this video we are discussing descriptive and inferential statistics

So, at first we are going to define statistics. Statistics is the science of conducting studies to collect, organize, summarize, yze, and draw

conclusions from data. So, this is a very simple way to define statistics

That means, this is a science where we'll be studying our data, which has got collected

And then we shall organize that data in some representable form. We can summarize

We can draw some conclusions. We can summarize it. We shall do the ysis

And obviously, at the end, we shall draw some conclusions, predictions, and forecasting

on the data. So, definition of a variable. A variable is a characteristic or attribute that can assume different values

We know that a variable can assume different values. So, we can consider the age of a person

So, age of a person is nothing but one variable because it can assume different values

So data are the values, measurements or observations that the variables can assume and variables

whose values are determined by chance are called random variables. So we know that data are nothing but the values and which will be obtained through some observation or through some measurements and that the variables can assume And the variables whose values will be determined by chance will be known as the random variable

A collection of data values forms a dataset and each value in the data set is called a data

value or a datum. So either you call data value or you can call datums

Data is a wrong English because data itself is plural. So, data can be used in different ways and the body of the knowledge called statistics is sometimes divided into two main areas, depending on how the data are getting used

The two main areas of statistics are descriptive statistics and were having infer maintain statistics

So, let us discuss them one by one. So, at first, we are starting with the descriptive statistics

Descriptive statistics, these statistics consists of the collection, organization, summarization, and presentation of data

So, data will get collected. We shall organize it. We shall draw some summary from the data

And also, we shall represent some data in some representable, might be in some visual

representation also It can represent data in some meaningful form such as charts graphs and tables We know that charts and graphs they are the visual representation of our data

If you go for the inferential statistics, so inferential statistics consists of generalizing

from the samples to populations and performing estimations and hypothesis tests and determining

relationships among multiple different variables and also it can do predictions. So, in case of inferential statistics will be going for different hypothesis testings

we shall calculate some estimates from the sample to the population and determine their

respective relationships and also among the variables, the relationships, how it is

occurring, what is the relationship they are having, that will be expressed in our inferential

statistics. In case of inferential statistics, we're having one extra feature that means this

particular statistics can go for predictions and forecasting on the present set of data

after doing the ysis. Here the statistician tries to make inferences from samples to population

Inferential statistics uses probability, that is the chance of an event occurring

If you play cards dice bingo and lotteries you win or lose the accordingly to the laws of probability So either you can win or you can lose the game depending upon the laws of probability

So, inferential statistics use the concept of probability. So let me discuss what is a population and what is a sample

A population consists of subjects. It may be human or, say, some otherwise, and that are being studied

So, population means we are considering the whole, the human being, or the others, which

will be considered, which will be satisfying a certain criteria. So that is known as the population which consists all the subjects

But in case of most of the time, due to the expense, time, size of the population, medical

concerns, and whether we are reachable to all subjects in the population or not

So feasibility, doing all this study, we can, it is not possible to even

use the entire population for a statistical study. So in those cases, researchers used to go to study samples

So the samples will be taken from the populations. So samples are nothing but those subjects which have got taken from the population and will

be doing some research work on them. A sample is a group of subjects selected from a population

So in this particular video, we have discussed multiple different terminologies which will

be required in our concept building. Thanks for watching this video