Defining Probability Distribution, broad categories of Probability Distributions and observing three practical, real applications of Probability Distribution.

1. A probability distribution is a function that
describes the likelihood of obtaining the
possible values that a random variable can
assume.
p(x) = the likelihood that
random variable takes a
specific value of x.
Probability distribution functions into two types:
Discrete Probability Functions are also
known as Probability Mass Functions
they assume select values from a group
for each event.
For example, likelihood of rolling a
specific number on a die is 1/6. The
total probability for all six values equals
one.
Continuous Probability Functions are
also known as Probability Density
Functions.
For any event, a variable can assume a
values from a smooth continuum.
For example the temperature at 12 noon
could be any integer between 30 and 40.
Well, what is Probability
Distribution ?
>Discrete Functions as
>Continuous Functions as
Siddharth Upadhyay
1915127, B4,
MME4, IA2

2. Applications of
Probability
Distributions
Elections
Consumer
Production
Equities
and
investment
>Data samples of electorates are
collected and Probability Distribution
functions are generated.
>This helps in analysing the possible
deviations of the universal group
from the Political Party’s hypothesis
in the sample.
Siddharth Upadhyay
1915127, B4,
MME4, IA2
Normal Distributions are laid
out about various likes of
population and the production
is set targeting specific
segments such probability
distributions.
The historical price, volume and
days are noted and Probability
distributions are crafted that
help in analysing the price at a
particular combination of events
in future.