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Standard Discrete and Continuous Univariate Distributions Video Lecture | CSIR NET Crash Course for Mathematics - CSIR NET Mathematics
FAQs on Standard Discrete and Continuous Univariate Distributions Video Lecture - CSIR NET Crash Course for Mathematics - CSIR NET Mathematics
| 1. What are some common examples of continuous univariate distributions? |  |
Ans. Some common examples of continuous univariate distributions include the normal distribution, uniform distribution, exponential distribution, and beta distribution.
| 2. What is the difference between discrete and continuous univariate distributions? | |
Ans. Discrete univariate distributions are defined on a countable set of values, while continuous univariate distributions are defined on an interval of real numbers.
| 3. How is the probability density function (PDF) used in continuous univariate distributions? | |
Ans. The PDF in continuous univariate distributions gives the probability of a random variable falling within a particular interval. It is used to calculate probabilities for continuous random variables.
| 4. Can you give an example of a situation where a continuous univariate distribution would be applicable? | |
Ans. A continuous univariate distribution could be applied to model the heights of adult males in a population, where the random variable represents the height in inches or centimeters.
| 5. What is the Central Limit Theorem and how does it relate to continuous univariate distributions? | |
Ans. The Central Limit Theorem states that the distribution of the sum (or average) of a large number of independent, identically distributed random variables approaches a normal distribution. This theorem is often used in the context of continuous univariate distributions to understand the behavior of sample means and sums.
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