"Day 31 of My Learning Journey: Setting Sail into Data Excellence! Today's Focus: Mathematics for Data Analysis (Stats Day -10)

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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