Table of Contents
Is discrete distribution and is continuous distribution?
A discrete distribution is one in which the data can only take on certain values, for example integers. A continuous distribution is one in which data can take on any value within a specified range (which may be infinite).
Can any distribution be transformed to normal distribution?
Example 1: You cannot transform a discrete distribution (like the Poisson distribution) into a normal distribution because the Poisson distribution only has non-zero probability on the natural numbers whereas a 1 dimensional normal distribution has non-zero probability over the entire real line.
When a continuous distribution is used to approximate a discrete distribution?
What is the Continuity Correction Factor? A continuity correction factor is used when you use a continuous probability distribution to approximate a discrete probability distribution. For example, when you want to use the normal to approximate a binomial.
What is the difference between a discrete probability distribution and a continuous probability distribution?
A probability distribution may be either discrete or continuous. A discrete distribution means that X can assume one of a countable (usually finite) number of values, while a continuous distribution means that X can assume one of an infinite (uncountable) number of different values.
Can a discrete probability distribution be negative?
Each of the discrete values has a certain probability of occurrence that is between zero and one. That is, a discrete function that allows negative values or values greater than one is not a probability function.
How do you convert a normal distribution to a non-normal distribution?
Box-Cox Transformation is a type of power transformation to convert non-normal data to normal data by raising the distribution to a power of lambda (λ). The algorithm can automatically decide the lambda (λ) parameter that best transforms the distribution into normal distribution.
Can discrete data be normally distributed?
Normal distribution is strictly only applicable for data that is continuous though in some cases we can use the normal distribution to approximate data that is discrete.
Why do we need to correct for continuity when using discrete variables?
On the other hand, when the normal approximation is used to approximate a discrete distribution, a continuity correction can be employed so that we can approximate the probability of a specific value of the discrete distribution. The continuity correction requires adding or subtracting .