STATISTICS FOR DATA ANALYTICS - 8

Inferential Statistics :-

Very large amount of data for analysis is needed, which may need too much time and resources.

We take small samples of data from big data and create models to get output.

Probability :-

Introduction to Probability :-

What is the likelihood of the event Is called probability.

Type of probability

Mutually exclusive.
Eg:- Dice ( everything is independent )
Non mutually exclusive.
Eg :- Dependent on each other

Rules in Probability

Additive Rule of Probability

Multiplicative Rule of Probability.

Dependent

Independent

BASIC OF PROBABILITY

Steps :-
Find all the combination
Find probability of each
Frequency distribution.
Probability distribution
Use and get the result with Expected value.

Random Variables :-

It is a process of mapping the output of the random process or experiment to a number.
Eg ➖ Tossing a coin.

Sets :-

a={1,2,3,4,5,6,7,8}
b={1,2,3,4}

Intersection - getting common from the sets.
Union - getting everything from the sets.
Difference - (a-b) getting only which is not in b.
Subset - b is a subset of a.
Superset - a is a superset of b.

Probability distribution
It is a distribution form of representation that tells us the probability for all the possible values of X.

PROBABILITY DISTRIBUTION FUNCTION :-

It has two type : -

OVERVIEW :-

PROBABILITY DENSITY FUNCTION ( pdf )
It is for the continuous random variable.
EG :- Height
PROBABILITY MASS FUNCTION ( pmf )
It is for the discrete random variable.
EG :- Rolling of dice
Cumulative Distribution FUNCTION ( cdf ) :-
Cumulative means sum of previous and current value. It can be created for both pdf and pmf. To show the sum up value.

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