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
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 So, you 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
Datas 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 inferential 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 the 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