STATISTICS FOR DATA ANALYTICS - 10
Continuous probability distribution :- Pdf ( Probability Density Function )
It is a variable that can take an infinite number of values over a given interval.
Cumulative distribution function ( cdf ) :-
The cumulative probability of X is defined as the probability of the variable being less than or equal to x.
So, from above which one is used commonly in real life
( pdf is mostly used in real life )
Types :-
Uniform distribution
It is a probability distribution in which every value between an interval from a to b is equally likely to occur.
Continuous Uniform Distribution ( pdf )
Rectangle distribution.
Eg: - no.of candy sold.
Discrete uniform distribution ( pmf )
Eg:- rolling a dice
Normal distribution / Gaussian distribution :-
Mostly this distribution we will get in the industry.
Empirical formula :- 68% , 94% , 98.7 %
Log normal distribution
We say that if ln(x) is normally distributed, a random variable X is log normally distributed.
Uses :-
Finance sector
Exponential distribution
Future prediction rate lambda.
High lambda sudden drop of slope.
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