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2. A Continuous distribution talks about the data
within a particular range of values. In this case it is
assumed that the probability of a value will fall
within a certain range. These values are infinite in
nature.
An example for continuous distribution would be
time, wherein you can take data ranging from 0
seconds to hours that can be counted on and on.
Continuous Data
Distributions
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3. Discrete Data
Distributions
A discrete data distribution refers to the
arrangement of values that are countable and finite.
The types of discrete data distribution are:-
➔ Binomial Distribution
➔ Multinomial Distribution
➔ Poisson Distribution
➔ Bernoulli Distribution
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4. Measurements of
Relationships between variables
Variables in statistics are known to be of two types:-
a). Dependent Variable:- It is influenced by various other
factors. For instance your examination score depends upon
the amount of preparation you had made.
b). Independent Variable:- It doesn’t depend upon any of
the factors.
Example: A person’s age is an independent variable, as it
has nothing to do with his routine or his food habits.
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