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What is the probability of choosing a number greater than 21 if a number is randomly chosen between 1 and 50?
Assuming the uniform continuous distribution, the answer is 29/49. With the uniform discrete distribution, the answer is 29/50.
What are the differences between Continuous and discrete variation?
Continuous variations have a wide range of possibilities. For example, your height is a continuous variation. There are many options (for example you could be 5'9, 4'6, 6'1) rather than an either/or situation. Discrete variations have only two possibilities. They can be thought of as "either/or" situations. For example, you can either roll your tongue or you can't. There is no grey area or in-between.
Difference between discrete and continuous?
A continuous variable is one that can take any value within an interval (or a set of intervals). A discrete variable is one that can only take certain values.Some further notes:* Often a discrete variable takes integer values, but that is not necessary.* Neither discrete nor continuous variables need be limited to a finite number of possible values.* Frequently, continuous variables are continuous only in principle, and the measuring instruments or recording make them discrete. Eg your height is continuous but as soon as it is recorded as 1.75 cm or 5'9", it is made discrete.
What is the difference between a discrete and a continuous probability function?
A discrete random variable (RV) can only take a selected number of values whereas a continuous rv can take infinitely many.
What is the difference between discrete and continuous graphs?
Discrete and Continuous GraphThis will be a very basic definition but understandable one A graph is discrete when one (or both) of the variables has discrete entries, its means that are entered number, without decimal part, so the graph has no continuity, the trace will be broken parts, not a single one.beside a continuous graph is a graph where both variables are continuous, it means that their field's are de Real number, so the trace it's a continuous line.Also we can differentiated because the range are points (in a discrete one) and all the numbers (in a continuous one).
What is the difference between a discrete and a continuous distribution?
A simple continuous distribution can take any value between two other values whereas a discrete distribution cannot.
What is the difference between discrete and cumulative distributions?
A discrete distribution is one in which the random variable can take only a limited number of values. A cumulative distribution, which can be discrete of continuous, is the sum (if discrete) or integral (if continuous) of the probabilities of all events for which the random variable is less than or equal to the given value.
Distinguish between binomial distribution and normal distribution?
Normal distribution is the continuous probability distribution defined by the probability density function. While the binomial distribution is discrete.
How does a discrete probability distribution differ from a continuous probability distribution?
A discrete probability distribution is defined over a set value (such as a value of 1 or 2 or 3, etc). A continuous probability distribution is defined over an infinite number of points (such as all values between 1 and 3, inclusive).
What is the difference between continuous and discrete system?
The difference between continuous and discrete system lies in the variables. Whereas the continuous systems have dynamic variables, the discrete system have static variables.
Can normal distridution apply on discrete data?
Yes, If you have a large data set, you can approximate the discrete data by Normal distribution (which is continuous). An example would be, "A coin is tossed 1000 times. What is the probability of rolling between 300 and 400 heads?" This problem, usually solved by Binomial distribution (which is a discrete distribution), is very difficult to solve because of the large data set and can be approximated by the Normal distribution.
What is the probability of choosing a number greater than 21 if a number is randomly chosen between 1 and 50?
Assuming the uniform continuous distribution, the answer is 29/49. With the uniform discrete distribution, the answer is 29/50.
What are the differences between Continuous and discrete variation?
Continuous variations have a wide range of possibilities. For example, your height is a continuous variation. There are many options (for example you could be 5'9, 4'6, 6'1) rather than an either/or situation. Discrete variations have only two possibilities. They can be thought of as "either/or" situations. For example, you can either roll your tongue or you can't. There is no grey area or in-between.
Is number of students continuous or discrete?
The number of students is discrete. There is no number of students between 4 and 5.
Difference between discrete and continuous?
A continuous variable is one that can take any value within an interval (or a set of intervals). A discrete variable is one that can only take certain values.Some further notes:* Often a discrete variable takes integer values, but that is not necessary.* Neither discrete nor continuous variables need be limited to a finite number of possible values.* Frequently, continuous variables are continuous only in principle, and the measuring instruments or recording make them discrete. Eg your height is continuous but as soon as it is recorded as 1.75 cm or 5'9", it is made discrete.
What is the difference between a discrete and a continuous probability function?
A discrete random variable (RV) can only take a selected number of values whereas a continuous rv can take infinitely many.
Can you demonstrate how to calculate are underneath a probability distribution and between two data values of your choice?
If the distribution is discrete you need to add together the probabilities of all the values between the two given ones, whereas if the distribution is continuous you will need to integrate the probability distribution function (pdf) between those limits. The above process may require you to use numerical methods if the distribution is not readily integrable. For example, the Gaussian (Normal) distribution is one of the most common continuous pdfs, but it is not analytically integrable. You will need to work with tables that have been computed using numerical methods.
