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You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.
You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.
You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.
You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.
You do not compute discrete variables. Some variables are discrete others are not. Simple as that. You do not compute people - you can compute their average height, or mass, or shoe size, etc. But that is computing those characteristics, you are not computing people. In the same way, you can compute the mean, variance, standard error, skewness, kurtosis of discrete variables, or the probability of outcomes, but none of that is computing the discrete variable.
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How do you compute the probability distribution of a function of two Poisson random variables?
we compute it by using their differences
What type of variables is the number of robberies reported in your city?
discrete
Brand of toothpaste discrete or continuous?
Discrete.
Is the number of pills in a container of vitamins discrete or continuous?
Discrete random variable
Examples of continuous variables?
A continuous variable is one that can take infinite number of values in an interval. Examples are weight, height. A person's weight can be 150.2 lbs, 150.456 pounds and so on. Discrete variables, on the other hand can only assume a finite number of values. For example, number of people in a movie theatre.
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.
What are continuous and discrete variables?
the value of variables is determined by the equation, discrete variables have absolute single value while the continuos have a range value
Number of notes in a song is discrete variable?
number of notes in a song is discrete variables or continuous variables
Are quiz scores discrete or continuous variables?
Quiz and exam scores are discrete variables because they are defined as one exact number.
Are negative numbers discrete random variables?
Discrete variables must be countable and not negative. So no a negative number must be a continuous variable.
How do you compute the probability distribution of a function of two Poisson random variables?
we compute it by using their differences
What type of variables is the number of robberies reported in your city?
discrete
What type of data has an infinite number of points between each measurement?
I think you are going for continuous variable, as compared with discrete variables.
Should there be equal steps between values in discrete random variables?
Not necessarily.
Classification of variables according of continuity of values?
Not continuous => Discrete variable. Continuous => Continuous variable.
What are the pros and cons of discrete probability distribution?
Strictly speaking, there are no cons because they are defined for discrete variables only. The only con that I could think of is the difficulty evaluating the moments and other probabilities for some discrete distributions such as the negative binomial.
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.
